task assignment model

• Trending Now
• Foundational Courses
• Data Science
• Practice Problem
• Machine Learning
• System Design
• DevOps Tutorial
• Distributed Systems Tutorial

Introduction to Distributed System

• What is a Distributed System?
• Features of Distributed Operating System
• Evolution of Distributed Computing Systems
• Types of Transparency in Distributed System
• What is Scalable System in Distributed System?
• Role of Middleware in Distributed System
• Difference between Hardware and Middleware
• What is Groupware in Distributed System?
• Difference between Parallel Computing and Distributed Computing
• Difference between Loosely Coupled and Tightly Coupled Multiprocessor System
• Design Issues of Distributed System
• Introduction to Distributed Computing Environment (DCE)
• Limitation of Distributed System
• Various Failures in Distributed System
• Types of Operating Systems
• Types of Distributed System
• Centralized vs. Decentralized vs. Distributed Systems
• Three-Tier Client Server Architecture in Distributed System

Communication in Distributed Systems

• Features of Good Message Passing in Distributed System
• Issues in IPC By Message Passing in Distributed System
• What is Message Buffering?
• Multidatagram Messages in Distributed System
• Group Communication in Distributed Systems

Remote Procedure Calls in Distributed System

• What is RPC Mechanism in Distributed System?
• Distributed System - Transparency of RPC
• Stub Generation in Distributed System
• Marshalling in Distributed System
• Server Management in Distributed System
• Distributed System - Parameter Passing Semantics in RPC
• Distributed System - Call Semantics in RPC
• Communication Protocols For RPCs
• Client-Server Model
• Lightweight Remote Procedure Call in Distributed System
• Difference Between RMI and DCOM
• Difference between RPC and RMI

Synchronization in Distributed System

• Synchronization in Distributed Systems
• Logical Clock in Distributed System
• Lamport's Algorithm for Mutual Exclusion in Distributed System
• Vector Clocks in Distributed Systems
• Event Ordering in Distributed System
• Mutual exclusion in distributed system
• Performance Metrics For Mutual Exclusion Algorithm
• Cristian's Algorithm
• Berkeley's Algorithm
• Difference between Token based and Non-Token based Algorithms in Distributed System
• Ricart–Agrawala Algorithm in Mutual Exclusion in Distributed System
• Suzuki–Kasami Algorithm for Mutual Exclusion in Distributed System

Source Management and Process Management

• Features of Global Scheduling Algorithm in Distributed System

What is Task Assignment Approach in Distributed System?

• Load Balancing Approach in Distributed System
• Load-Sharing Approach in Distributed System
• Difference Between Load Balancing and Load Sharing in Distributed System
• Process Migration in Distributed System

Distributed File System and Distributed shared memory

• What is DFS (Distributed File System)?
• Andrew File System
• File Service Architecture in Distributed System
• File Models in Distributed System
• File Accessing Models in Distributed System
• File Caching in Distributed File Systems
• What is Replication in Distributed System?
• Atomic Commit Protocol in Distributed System
• Design Principles of Distributed File System
• What is Distributed shared memory and its advantages
• Architecture of Distributed Shared Memory(DSM)
• Difference between Uniform Memory Access (UMA) and Non-uniform Memory Access (NUMA)
• Algorithm for implementing Distributed Shared Memory
• Consistency Model in Distributed System
• Distributed System - Thrashing in Distributed Shared Memory

Distributed Scheduling and Deadlock

• Scheduling and Load Balancing in Distributed System
• Issues Related to Load Balancing in Distributed System
• Components of Load Distributing Algorithm | Distributed Systems
• Distributed System - Types of Distributed Deadlock
• Deadlock Detection in Distributed Systems
• Conditions for Deadlock in Distributed System
• Deadlock Handling Strategies in Distributed System
• Deadlock Prevention Policies in Distributed System
• Chandy-Misra-Haas's Distributed Deadlock Detection Algorithm
• Security in Distributed System
• Types of Cyber Attacks
• Cryptography and its Types
• Implementation of Access Matrix in Distributed OS
• Digital Signatures and Certificates
• Design Principles of Security in Distributed System

Distributed Multimedia and Database System

• Distributed Database System
• Functions of Distributed Database System
• Multimedia Database

Distributed Algorithm

• Deadlock-Free Packet Switching
• Wave and Traversal Algorithm in Distributed System
• Election algorithm and distributed processing
• Introduction to Common Object Request Broker Architecture (CORBA) - Client-Server Software Development
• Difference between CORBA and DCOM
• Difference between COM and DCOM
• Life cycle of Component Object Model (COM) Object
• Distributed Component Object Model (DCOM)

Distributed Transactions

• Flat & Nested Distributed Transactions
• Transaction Recovery in Distributed System
• Mechanism for Building Distributed File System
• Two Phase Commit Protocol (Distributed Transaction Management)

A Distributed System is a Network of Machines that can exchange information with each other through Message-passing. It can be very useful as it helps in resource sharing. In this article, we will see the concept of the Task Assignment Approach in Distributed systems.

Resource Management:

One of the functions of system management in distributed systems is Resource Management. When a user requests the execution of the process, the resource manager performs the allocation of resources to the process submitted by the user for execution. In addition, the resource manager routes process to appropriate nodes (processors) based on assignments. 

Multiple resources are available in the distributed system so there is a need for system transparency for the user. There can be a logical or a physical resource in the system. For example, data files in sharing mode, Central Processing Unit (CPU), etc.

As the name implies, the task assignment approach is based on the division of the process into multiple tasks. These tasks are assigned to appropriate processors to improve performance and efficiency. This approach has a major setback in that it needs prior knowledge about the features of all the participating processes. Furthermore, it does not take into account the dynamically changing state of the system. This approach’s major objective is to allocate tasks of a single process in the best possible manner as it is based on the division of tasks in a system. For that, there is a need to identify the optimal policy for its implementation.

Working of Task Assignment Approach:

In the working of the Task Assignment Approach, the following are the assumptions:

• The division of an individual process into tasks.
• Each task’s computing requirements and the performance in terms of the speed of each processor are known.
• The cost incurred in the processing of each task performed on every node of the system is known.
• The IPC (Inter-Process Communication) cost is known for every pair of tasks performed between nodes.
• Other limitations are also familiar, such as job resource requirements and available resources at each node, task priority connections, and so on.

Goals of Task Assignment Algorithms:

• Reducing Inter-Process Communication (IPC) Cost
• Quick Turnaround Time or Response Time for the whole process
• A high degree of Parallelism
• Utilization of System Resources in an effective manner

The above-mentioned goals time and again conflict. To exemplify, let us consider the goal-1 using which all the tasks of a process need to be allocated to a single node for reducing the Inter-Process Communication (IPC) Cost. If we consider goal-4 which is based on the efficient utilization of system resources that implies all the tasks of a process to be divided and processed by appropriate nodes in a system.

Note: The possible number of assignments of tasks to nodes:

But in practice, the possible number of assignments of tasks to nodes < m x n because of the constraint for allocation of tasks to the appropriate nodes in a system due to their particular requirements like memory space, etc.

Need for Task Assignment in a Distributed System:

The need for task management in distributed systems was raised for achieving the set performance goals. For that optimal assignments should be carried out concerning cost and time functions such as task assignment to minimize the total execution and communication costs, completion task time, total cost of 3 (execution, communication, and interference), total execution and communication costs with the limit imposed on the number of tasks assigned to each processor, and a weighted product of cost functions of total execution and communication costs and completion task time. All these factors are countable in task allocation and turn, resulting in the best outcome of the system.

Example of Task Assignment Approach:

Let us suppose, there are two nodes namely n1 and n2, and six tasks namely t1, t2, t3, t4, t5, and t6. The two task assignment parameters are:

• execution cost: x ab refers to the cost of executing a task an on node b.
• inter-task communication cost: c ij refers to inter-task communication cost between tasks i and j.

Note: The execution of the task (t2) on the node (n2) and the execution of the task (t6) on the node (n1) is not possible as it can be seen from the above table of Execution costs that resources are not available.

Case1: Serial Assignment

Cost of Execution in Serial Assignment:

Cost of Communication in Serial Assignment:

Case2: Optimal Assignment

Cost of Execution in Optimal Assignment:

Cost of Communication in Optimal Assignment:

Optimal Assignment using Minimal Cutset:

Cutset: The cutset of a graph refers to the set of edges that when removed makes the graph disconnected.

Minimal Cutset: The minimal cutset of a graph refers to the cut which is minimum among all the cuts of the graph.

Please Login to comment...

Similar reads.

• Distributed System

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 25 March 2023

Machine endowment cost model: task assignment between humans and machines

• Qiguo Gong   ORCID: orcid.org/0000-0002-7783-9467 1  

Humanities and Social Sciences Communications volume  10 , Article number:  129 ( 2023 ) Cite this article

1210 Accesses

2 Citations

Metrics details

• Operational research

Although research on human–machine task assignment has presently received academic attention, the theoretical foundation of task assignment requires further development. Based on the two-dimensional characteristics of task flexibility and cognition, a machine endowment cost model is built to examine the economic allocation of tasks between humans and machines. The model derives a machine production possibility curve that directly divides all tasks into two categories, one each for machines and humans. The model shows the dynamic of task allocation between humans and machines as the economic environment evolves, such as wage growth and technological development, and provides conditions wherein task polarization may prevail. The model can be applied to human–machine task assignment decisions in industry and services.

Similar content being viewed by others

The future of the labor force: higher cognition and more skills

Robots, labor markets, and universal basic income

Psychological reactions to human versus robotic job replacement

Introduction.

Automation promotes the replacement of people with machines in accomplishing tasks. Economists have found that replacing humans with machines leads to a phenomenon called “task polarization” (Acemoglu, 1999 ; Autor et al., 2003 ; Autor et al., 2006 ; Goos and Manning, 2007 ; Goos et al., 2009 ; Goos et al., 2014 ). If skills are classified by levels such as low, medium, or high, machines are likely to replace humans in performing middle-skill tasks, while humans will be primarily engaged in low- and high-skill tasks. The trend of machines replacing humans has accelerated as technology advances. Although task polarization has several definitions, the understanding of tasks performed by machines instead of humans requires improvement. Concerns regarding this include “establishing a theoretical basis for whether a task should be assigned to machines or humans” and “investigating why some tasks can be assigned to machines instead of humans, whereas others cannot.”

Some scholars have analyzed this issue conceptually. Autor et al. ( 2003 ) divided tasks into routine and nonroutine categories. Machines can perform routine tasks in place of humans. Routine tasks can be summarized as series of specific activities completed according to clearly defined instructions and procedures. Conversely, a nonroutine task requires flexibility, creativity, problem-solving, or interpersonal skills. Acemoglu and Autor ( 2011 ) and Autor ( 2015 ) further categorized tasks along two dimensions—cognitive and manual—in addition to routine and nonroutine. The difference between cognitive and manual tasks lies in the degree of mental and physical activity. Tasks that software engineers can code to be performed automatically by machines, such as accounting, are routine cognitive tasks. Accurately performing repetitive physical operations in a stable environment, such as assembly tasks, is a routine manual task. It is challenging to computerize nonroutine manual and cognitive tasks. Nonroutine manual workers, such as personal care workers in service occupations, generally appear at the lower end of the occupational skill spectrum. Meanwhile, nonroutine cognitive workers, such as economists, tend to appear at the higher end.

In recent years, the engineering community has shown increased interest in task assignment between humans and machines (Ranz et al., 2017 ; Malik and Bilberg, 2019 ; Yuan et al., 2020 ). Their purpose is to achieve better human–robot cooperation and COBOT (collaborative robot) development.

Two-dimensional capabilities are required for machines to perform tasks: manual flexibility and cognitive ability. These attributes are called the flexibility and cognitive endowments of the machine. In this study, we build a machine endowment model using these two-dimensional task characteristics to directly determine task assignments between humans and machines. Previous models have explained task polarization; however, they did not consider the two-dimensional characteristics of tasks (Acemoglu and Autor, 2011 ; Acemoglu and Restrepo, 2018b ; Acemoglu and Restrepo, 2018c ).

Task assignment between a machine and a human depends on the costs of the machine and the human. Cognitive skills are related to educational level, the main driver of wage growth (Yamaguchi, 2012 ; Michaels et al., 2014 ; Frey and Osborne, 2017 ; Alabdulkareem et al., 2018 ). Occupations with less educated humans often rely on manual skills and pay poorly (Frey and Osborne, 2017 ; Alabdulkareem et al., 2018 ). Thus, cognitive ability enhances human wages in our model, while manual flexibility is unrelated to human wages.

We synthesize the above views regarding skill classification. Skills are categorized as low, medium, and high based solely on cognitive ability, with manual flexibility not considered.

This paper makes a contribution to the literature by examining how tasks can be allocated economically between humans and machines according to the two-dimensionality required by flexibility and cognitive endowments. A second contribution is shedding light on the dynamic of task allocation as the economic environment evolves, such as with increases in wage level and technological advances. A third contribution is providing conditions wherein task polarization may prevail.

First, we compare the cost of a task completed by a machine with that completed by a human. Using our model, we obtain a machine production possibility curve (MPPC) to determine whether the task should be assigned to a machine or a human. Based on our cost model, we find that companies would replace expensive humans with cheaper machines (Acemoglu and Restrepo, 2018a ; Basso and Jimeno, 2021 ).

Second, regarding the dynamic of task allocation between humans and machines when the economic environment evolves; generally, machine technology is progressing toward middle-skill tasks (Autor et al., 2003 ; Cortes, 2016 ; Spitz-Oener, 2006 ; Ross, 2017 ; Wang, 2020 ; Atalay et al., 2020 ). In the future, with the advancement of technology, tasks will be increasingly assigned to machines. The expected technological advancement will cause the elimination of 83% of the jobs in low-wage industries (Acemoglu and Restrepo, 2018b ; Frey and Osborne, 2017 ; Acemoglu and Restrepo, 2020 ). However, the direction of the influence of machine technology may differ; for example, technological development could be directed toward increasing the flexibility of machines while reducing associated costs. Another example is technological development to improve cognitive ability, such as deep learning. The model explains that technological progress is often affected by human wages. A decrease in minimum wage hinders the employment of minimum wage workers in regular occupations (Aaronson and Phelan, 2019 ; Lordan and Neumark, 2018 ). Another example is the rate at which wages increase with cognitive ability. If the rate is high, so the wages of middle-skilled labor are high, technology will develop in the direction of replacing middle-skilled labor to save cost. That is, skill-biased technological development occurs. Skill-biased technological development was less evident in European countries with lower wage differentials (Acemoglu, 2003 ).

Third, the model provides conditions under which task polarization may prevail.

The remainder of the paper is organized as follows. The second section details our model. The third is a case verification in the engineering field. The final section provides a conclusion and discusses the implications.

Machine endowment cost model

Previously, machines only had one dimensional capability: manual flexibility or cognitive ability. An example is simple operations for production tasks requiring low flexibility and no cognitive skills. Another example is computer programming algorithms, which only require a certain degree of cognition. However, currently, machines require operational flexibility and cognition. For example, a garbage-sorting robot must first identify the type of garbage, requiring cognitive ability, and then sort the garbage into different trash bins, requiring operational flexibility. Today, machines are developing in the direction of humans. With the progress of technology, the scope of tasks that machines can complete is evolving. For example, developing soft robots may better accomplish manual tasks than traditional rigid robots. Meanwhile, machine learning in artificial intelligence may better accomplish cognitive tasks. It is possible to replace humans with machines for manual and cognitive tasks with the advances in machine technology and the increased capabilities of machines. Task classification should include tasks that combine manual and cognitive tasks, for example, aircraft piloting and maintenance.

In the model, g and f represent cognitive and flexibility endowments, respectively; the endowment of a machine is ( g , f ). The cost of the machine is as follows:

where α represents the technology level, a is the coefficient of cognitive cost, and b is the coefficient of flexibility cost ( α  > 1, a  > 1, b  > 1).

When ρ  = 1, Eq. ( 1 ) demonstrates constant elasticity of substitution, implying that doubling a machine’s flexibility and cognitive ability doubles cost. It is reasonable to assume that doubling a machine’s flexibility and cognitive ability will cost more than double the cost; thus, we keep ρ  > 1. From Eq. ( 1 ), we obtain ∂ C ⁄∂ f  > 0, ∂C⁄∂ g  > 0, ∂ C ⁄∂ a  > 0, ∂ C ⁄∂ b  > 0, and ∂ C ⁄∂ α  < 0 (see Proof 1 in the Appendix ).

∂ C ⁄∂ α  < 0 implies that the cost of a machine with no change in endowment decreases as technology advances.

Wages are highly correlated with educational level (Frey and Osborne, 2017 ; Alabdulkareem et al., 2018 ). Humans are paid according to their cognitive ability; thus, wages do not depend on flexibility. The wage for a task requiring endowment ( g , f ) is as follows:

where c  > 0, β  > 0, and c represents the minimum wage. When g  = 0, the minimum wage is c ρ . The minimum wage is unrelated to cognitive ability but rather to the value of flexibility. β is the coefficient of human cognition. The superscript ρ is the same as in Eq. ( 1 ) for convenient analysis, ρ  > 1, meaning that wages increase faster than cognitive ability develops. Obviously, wages are also affected by the base minimum wage. As humans’ cognitive ability grows, so does their flexibility value. The same ρ is used for simplicity in Eqs. ( 1 ) and ( 2 ). If ρ is different, the cost of larger ρ will increase faster, which will inevitably be a disadvantage when assigning tasks. Therefore, it is reasonable to set the same ρ .

Machine production possibility curve

If the machine and human costs of accomplishing the tasks requiring endowment ( g , f ) are equal,

From (3b), the MPPC is derived as follows (let \(\left( {\widetilde g,\widetilde f} \right)\) be the point on the MPPC):

The MPPC divides the endowment ( g , f ) area in two parts (Fig. 1 ), where the horizontal and vertical axis is g and f , respectively. When \(\widetilde g = 0\) , \(\widetilde f = cb^{ - \frac{1}{\alpha }}\) , and when \(\widetilde f = 0\) , \(\widetilde g = c\left( {a^{\frac{1}{\alpha }} - \beta } \right)^{ - 1}\) , we find that if \(a^{\frac{1}{\alpha }} \le \beta\) , \(\widetilde g\) tends to infinity. Thus, machines will be able to replace all high-skill jobs. It is generally believed that humans have a cognitive advantage over machines; thus, \(a^{\frac{1}{\alpha }} \le \beta\) .

Machine production possibility curve (MPPC).

Proposition 1. Humans perform tasks in the endowment area above the MPPC. Machines perform tasks below the MPPC (see Proof 2 in the Appendix ) .

Therefore, the MPPC is a direct and simple method to divide tasks into two, one assigned to machines and the other assigned to humans. This differs from the literature, where the division needs to be clarified.

Task characteristics are measured by flexibility and cognition. In Fig. 1 , we assume that cognitive endowments of g  ≤ 20, 20 <  g  ≤ 40, and g  > 40 are low, medium, and high cognitive endowments, respectively. Flexibility endowments of f  ≤ 30, 30 <  f  ≤ 60, and f  > 60 are low, medium, and high flexibility endowments, respectively. Manual tasks (points A, D, and F) only require low, middle, and high flexibility endowments, respectively. Cognitive tasks (points B, E, and G) only require low, middle, and high cognitive endowments, respectively. Tasks can be of several types: low flexible manual and low cognitive (point C), middle flexible manual and middle cognitive (point H), high flexible manual and low cognitive (point I), low flexible manual and high cognitive (point J), and high flexible manual and high cognitive (point K).

The tasks below the curve include those requiring low-to-moderate cognition and flexibility. Meanwhile, the tasks above the curve require moderate-to-high cognitive ability and flexibility. Any tasks requiring high flexibility or high cognitive abilities are above the curve. Classifying skills as low, medium, and high is not related to flexibility but rather to cognition (Yamaguchi, 2012 ; Michaels et al., 2014 ; Frey and Osborne, 2017 ; Alabdulkareem et al., 2018 ). Thus, A, B, C, D, F, and I in Fig. 1 are low-skill tasks; E and H are medium-skill tasks; and G, J, and K are high-skill tasks.

Why do humans still perform some middle-skill tasks? These middle-skill tasks require midrange cognitive abilities and moderate-to-high flexibility. Middle-skill tasks include middle cognition and high manual tasks. It is difficult for machines to perform tasks that require high flexibility and medium cognition such as product repair tasks in a manufacturing plant. Machines have replaced nearly all humans on some production lines of smart factories. However, defective products require repairs by humans. As maintenance tasks cover nearly all aspects of a product, they require employees to master a large amount of product knowledge, requiring medium or high cognition. Further, maintenance workers must perform various maintenance tasks requiring a high degree of flexibility. Therefore, humans with high flexibility and middle cognitive ability are required for these medium-skill tasks.

Previously, we primarily trained low-skilled manual workers (under college level) and workers with high cognitive abilities (undergraduate and above). Currently, we require a workforce with a high degree of flexibility and cognitive ability. College education must combine manual labor skills with high cognitive skills, as in the German educational system (Wang, 2020 ). In Germany, middle-skilled laborers have not been replaced in large numbers as in the UK; most turn to high-skill jobs if machines replace them. This occurs because German workers receive training in new technology and can hence better adapt to the conditions of the new computer age. The flexibility of humans far exceeds that of machines; thus, assigning humans to production lines can better satisfy personalized needs.

Lower-skill tasks are assigned to machines because such tasks require a low flexibility endowment; therefore, machines are able to perform these tasks. Therefore, not all low-skill tasks are assigned to humans. Machines replace humans in performing middle- and low-skill tasks. In factories, many machines are engaged in mostly simple repetitive tasks. These tasks require low flexibility and very little or no cognitive skills. Machines have reduced the employment share of low-skilled workers (Graetz and Michaels, 2018 ). The increase in machine adoption is significantly related to the decline in employment share of routine jobs (De Vries et al., 2020 ).

The dynamic of task allocation

We consider the change in task allocation as the economic environment evolves, such as from technological development and wage growth.

Proposition 2. It can be seen that \({\tilde{f}}\) increases with α, c, and β, and decreases with a and b. The maximum \({\overline {g}}\) of \({\tilde {g}}\) of the MPPC increases with α, c, and β, and decreases with a (see Proof 3 in the Appendix ).

From Proposition 2, an increase in \({\tilde {f}}\) and \({\overline {g}}\) with α shows that machines perform more tasks as technology advances. When the technological level increases from α  = 2 to α  = 3, MPPC moves up (Fig. 2 ).

Tasks where humans replace machines with increasing technological advancement.

Therefore, more middle-skill tasks are relocated to machines, that is, technology advances in the direction of middle-skill-biased technological change (Autor et al., 2003 ; Cortes, 2016 ; Spitz-Oener, 2006 ; Ross, 2017 ; Wang, 2020 ; Atalay et al., 2020 ). As the MPPC moves up, some high-skill tasks become middle-skill tasks with the shifts in the curve.

If technology develops in the direction of flexibility ( b decreases) or cognition ( a decreases), what will happen to human–machine task allocation?

From Proposition 2, \(\widetilde f\) decreases in a and b and \(\overline g\) decreases in a . When technology boosts the flexibility endowment of machines through increased investment and cost reductions, that is, b decreases, machines can easily replace humans in completing tasks requiring greater flexibility. Traditional rigid robots have natural limitations in performing some operations, such as complex operations and grasping actions, due to their low degree of freedom. Unlike rigid robots with limited degrees of movement, soft robots have higher flexibility and possess a high degree of freedom (Rus and Tolley, 2015 ; Lee et al., 2017 ; Wang et al., 2018 ).

If technological development facilitates cognitive endowment, that is, a decreases, what impact will it have? In this case, machines can easily replace humans in completing tasks that require not only greater cognition but also greater flexibility. This is different between a and b because \(\widetilde f\) is related to a and b , while \(\overline g\) is only related to a ; \(\overline g\) is the value when \(\widetilde f = 0\) .

Currently, the most important general technology is artificial intelligence, especially machine learning, that is, the ability of a machine to continuously improve its model without requiring humans to explain how to perform the tasks (Brynjolfsson and Mitchell, 2017 ). Therefore, machines can replace humans in performing high-skill tasks. The flourishing of artificial intelligence will eventually lead to the replacement of humans with machines in high-intelligence tasks. Artificial intelligence will take over analytical tasks, and developing analytical skills will become less important (Huang and Rust, 2018 ). A system using IBM technology automates the claims process of an insurance company in Singapore (Brynjolfsson and Mcaffe, 2017 ). Artificial intelligence can be applied in various professional fields, including medicine, finance, and information technology. Therefore, artificial intelligence may reduce the number of job opportunities (Frank et al., 2019 ).

What happens to human–machine task assignments if human wages increase?

From Proposition 2, \(\widetilde f\) and \(\overline g\) increases in c and β . We already know that c represents the base wages for tasks that rely solely on flexibility and can be regarded as the minimum wage base. Therefore, an increase in the minimum wage base makes \(\widetilde f\) and \(\overline g\) increase, and the maximum flexibility and cognitive endowment of machines that can replace humans increases. Increasing the minimum wage base will lead to machines replacing people in more jobs. Aaronson and Phelan ( 2019 ) and Lordan and Neumark ( 2018 ) showed that increasing minimum wage reduces the employment of minimum wage workers.

Another situation is the rate at which wages increase with cognitive ability. When increases in the wage rate are for cognitive ability, machines’ maximum cognitive endowment increases. Machines will replace more middle-skilled workers. Acemoglu ( 2003 ) found that European countries with lower wage differentials show less evidence of skill-biased technological change.

Aggregate endowment analysis

For tasks requiring that the aggregate endowment equal θ , that is, g  +  f  =  θ , the cost function of Eq. ( 1 ) is as follows:

and Eq. ( 3b ) becomes

Upper bound

Is there an upper bound on aggregate endowments? When the required aggregate endowment exceeds the upper bound, the tasks can only be assigned to humans. In this way, task allocation can be directly judged through the required aggregate endowment.

Let θ U be the upper bound. If θ  >  θ U , C ( g , θ - g ) >  W ( g , θ - g ) (Fig. 3 ). In Fig. 3 , the sum of the cognitive and flexibility endowments at any point on the straight line representing the upper bound of the aggregate endowment is θ U . Therefore, the straight line is an aggregate endowment isoline.

Upper bound of aggregate endowments.

Proposition 3. If the aggregate endowment required by a task is higher than the upper bound of the aggregate endowment (θ   >   θ U ), the cost to accomplish the task by a human is less than the cost of using a machine .

Proposition 3 means that if the flexibility endowment required by the task is high enough, a machine cannot replace a human to accomplish the task.

We can obtain the change in the upper bound of the aggregate endowment on the parameters (see Proof 4 in the Appendix ).

Proposition 4. The upper bound of the aggregate endowment θ U increases in α, c, and β, and decreases in a and b .

Proposition 4 indicates that when technological advancement ( α ), the minimum wage basis ( c ), and the rate of wage increases for cognition ( β ) increase, some tasks assigned to humans should be reallocated to machines. Conversely, when the coefficients of cognitive cost ( a ) and flexibility cost ( b ) increase, some tasks assigned to machines should be reallocated to humans.

Lower bound

Is there a lower bound on aggregate endowments? When the aggregate endowment is less than the lower bound, tasks can only be assigned to machines.

Let θ 1 be the intersection of the MPPC on the vertical axis. In Eq. ( 6 ), when \(g = 0,\,\theta _1 = b^{ - \frac{1}{\alpha }}c\) . Let θ 2 be the intersection of the MPPC on the horizontal axis. In Eq. ( 6 ), when f  = 0, \(\theta _2 = c/\left( {a^{\frac{1}{\alpha }} - \beta } \right)\) . The smaller value of the two intersection points is the aggregate endowment’s lower bound ( θ L ).

Figure 4 shows \(a^{\frac{1}{\alpha }} - b^{\frac{1}{\alpha }}\, < \beta ,\,\theta _L = b^{ - \frac{1}{\alpha }}c\) , and Fig. 5 shows \(a^{\frac{1}{\alpha }} - b^{\frac{1}{\alpha }}\, > \, \beta ,\,\theta _L = c/\left( {a^{\frac{1}{\alpha }} - \beta } \right)\) . Similarly, the straight lines representing the lower bounds in Figs. 4 and 5 are the aggregate endowment isolines.

Lower bound when θ L  =  θ 1 .

Lower bound when θ L  =  θ 2 .

Proposition 5. If θ   ≤   θ L , the aggregate endowment required by a task is less than the lower bound; thus, the cost of using machines to accomplish this task is less than that of using humans .

If the aggregate machine endowment required to accomplish a task is low, a low-skilled worker accomplishing this task would be replaced with a machine.

We obtain the change in the lower bound of the aggregate endowment on parameters in Proof 5 in the Appendix .

Proposition 6. θ L increases in α, c, and β, and decreases in a and b .

We can see that the lower bound in Proposition 6 follows the same trend as the upper bound in Proposition 4 with each parameter.

Task polarization

Tasks requiring an aggregate endowment larger than the upper bound or smaller than the lower bound can be directly assigned to humans and machines, respectively. Between the upper and lower bounds of the aggregate endowment, the endowment on an aggregate endowment isoline corresponding to each aggregate endowment θ is ( g , θ - g ), 0  ≤  g  ≤ θ. Some tasks on the aggregate endowment isoline are assigned to humans while others are assigned to machines. Task polarization occurs when tasks at the ends of the aggregate endowment isoline are assigned to humans and tasks in the middle are assigned to machines (Acemoglu, 1999 ; Autor et al., 2003 ; Autor et al., 2006 ; Goos and Manning, 200 ; Goos et al., 2009 ; Goos et al., 2014 ).

There are three situations. The first two situations are in ( θ 1 , θ 2 ).

θ 1  <  θ  <  θ 2

For each θ , the aggregate endowment isoline ( g , θ - g ) has one intersection ( g 1 , θ - g 1 ) with the MPPC (Fig. 6 ). The intersection ( g 1 , θ - g 1 ) makes the task assignment on the aggregate endowment isoline ( g , θ - g ). Low-skilled humans perform tasks requiring endowments ( g , θ - g ), g  <  g 1 , and machines perform tasks requiring endowments ( g , θ - g ), g  >  g 1 . In this case, task polarization does not occur.

The intersection ( g 1 , θ - g 1 ) when θ 1  <  θ 2 .

θ 2  <  θ  <  θ 1

For each θ , the aggregate endowment isoline ( g , θ - g ) has one intersection ( g 2 , θ - g 2 ) with the MPPC (Fig. 7 ). The intersection ( g 2 , θ - g 2 ) makes the task assignment on the aggregate endowment isoline ( g , θ - g ). Machines perform tasks requiring endowments ( g , θ - g ), g  <  g 2 , and high-skilled humans perform tasks requiring endowments ( g , θ - g ), g  >  g 2 . Therefore, task polarization does not occur.

The intersection ( g 2 , θ - g 2 ) when θ 1  >  θ 2 .

max ( θ 1 , θ 2 ) <  θ  <  θU

In this case, there are two situations. One is θ 1  <  θ 2 , and the other is θ 1  >  θ 2 . Here, we consider the case of θ 1  <  θ 2 and a similar situation.

For each θ , the aggregate endowment isoline ( g , θ - g ) has two intersections ( g 3 , θ - g 3 ) and ( g 4 , θ - g 4 ) with the MPPC (Fig. 8 ). The two intersections ( g 3 , θ - g 3 ) and ( g 4 , θ - g 4 ) make the task assignment on the aggregate endowment isoline ( g , θ - g ). Low-skilled humans do tasks requiring endowments ( g , θ - g ), g  <  g 3 ; machines do tasks requiring endowments ( g , θ - g ), g 3  <  g  <  g 4 ; and high-skilled humans do tasks requiring endowments ( g , θ - g ), g  >  g 4 . In this situation, humans are responsible for tasks at both ends of the aggregate endowment isoline and machines perform tasks in the middle of the aggregate endowment isoline; thus, task polarization occurs.

The intersections ( g 3 , θ - g 3 ) and ( g 4 , θ - g 4 ) when max( θ 1 , θ 2 )<  θ  < θ U .

Proposition 7. If max(θ 1 , θ 2 )   <   θ   <   θ U , task polarization occurs .

Therefore, task polarization only occurs between the upper and lower bounds of the aggregate endowment in the middle endowment area.

Task polarization occurs in the middle endowment area because machines with high flexibility or cognitive endowments have higher costs compared to machines with moderate flexibility and cognitive endowments. Most tasks where humans are replaced by machines are middle-skill tasks requiring medium flexibility and cognitive ability. The machine’s aggregate endowment is given; the low cognitive endowment and the high flexibility endowment, or the high cognitive endowment and the low flexibility endowment, are combined in the high cost of the machine, indicating that low-skilled individuals can accomplish high flexibility tasks and high-skilled individuals can perform high cognitive tasks.

We can obtain the changing trend of the g 1 , g 2 , g 3 , and g 4 parameters if θ is kept constant when the parameters change (see Proof 6 in the Appendix ).

Proposition 8. If θ is kept constant, g 1 and g 3 decrease in α, c, and β, and increase in a and b. Conversely, g 2 and g 4 increase in α, c, and β, and decrease in a and b .

From Proposition 8, we can see the dynamic of the g 1 , g 2 , g 3 , and g 4 parameters α , β , a , b , c . When the parameters change, tasks should be reallocated according to Proposition 8. Especially, when advances in technology ( α ), the minimum wage ( c ), and wage rate for promoting cognition ( β ) increase, g 3 moves to the left and g 4 to the right on the aggregate endowment isoline on which task polarization occurs. That is, some tasks assigned to humans should be reallocated to machines. In contrast, when the coefficients of cognitive cost ( a ) and flexibility cost ( b ) increase, g 3 moves to the right and g 4 to the left. In this case, some tasks assigned to machines should be reallocated to humans. Therefore, we obtain the dynamic of task allocation under polarization.

Application in engineering

We discuss the application of our model with a numerical example combined with examples from the literature on human–machine task assignments. Consider that the parameters are set to a  = 2, b  = 3, c  = 50, α  = 2, and β  = 0.8.

Malik and Bilberg ( 2019 ) score the complexity of assembly tasks, assigning tasks with low scores to machines and those with high scores to humans. We can regard the complexity of the assembly tasks as the flexibility needed by the machines. Therefore, tasks requiring high flexibility are assigned to humans, while those requiring low flexibility are assigned to machines.

We calculate the lower bound as 28. That is, tasks requiring aggregate endowments of less than 28 ( θ  =  f  +  g  ≤ 28) are assigned to machines. Malik and Bilberg ( 2019 ) only consider the flexibility endowment of the machine required by the task, that is, g  = 0; therefore, tasks requiring flexibility endowments less than or equal to 28 ( f  ≤ 28) are assigned to machines. Tasks requiring aggregate endowments greater than 28 ( f  > 28) are assigned to humans. Therefore, the case of Malik and Bilberg ( 2019 ) is consistent with our model.

Yuan et al. ( 2020 ) divided the complexity of tasks into assembly and cognitive complexity and scored them separately. Tasks with low assembly and cognitive complexity scores are assigned to machines, while tasks with high scores are assigned to humans. We can regard the assembly and cognitive complexity of the tasks as the flexibility and the cognitive endowments needed by machines, respectively. Therefore, tasks requiring high flexibility and cognitive endowments are assigned to humans, while tasks requiring low flexibility and cognitive endowments are assigned to machines.

Unlike Malik and Bilberg ( 2019 ), Yuan et al. ( 2020 ) considered the flexibility and cognitive endowments required to accomplish the tasks. We calculate the upper bound as 92 using the above parameters. That is, if the tasks requiring the aggregate endowments are more than 92 ( θ  =  f  +  g  ≥ 92), they are assigned to humans. Meanwhile, tasks requiring an aggregate endowment less than or equal to 28 ( f  ≤ 28) are assigned to machines. If the tasks requiring the aggregate machine endowments are between 28 and 92, how are the tasks assigned? Yuan et al. ( 2020 ) empirically divided the task into two parts. The low-scoring part of the tasks requiring low flexibility and cognitive endowments is assigned to machines, while the high-scoring part requiring high flexibility and cognitive endowments is assigned to humans. This is basically consistent with our model, but there is an important difference—Yuan et al. ( 2020 ) do not consider task polarization when the aggregate endowments are between the lower and upper bounds. For example, the tasks are assigned to low-skilled humans if the endowments required are θ  =  f  +  g  = 87 and g  ≤ 57, thus f  ≥ 87–57 = 30. The tasks are assigned to machines if the endowments they require are θ  =  f  +  g  = 87 and 57 <  g  < 81, thus 87−81 = 6 ≤  f  ≤ 87−57 = 30. The tasks are assigned to high-skilled humans if the endowments they require are θ  =  f  +  g  = 87 and g  ≥ 80; thus, f  ≤ 87−81 = 6.

The literature shows that task polarization occurs as machines replace humans to accomplish middle-skill tasks, making humans engage in only low- and high-skill tasks. From the perspective of machines, we define the endowment of machines based on the two-dimensional characteristics of tasks—flexibility and cognitive ability. A machine can obtain any endowment with related costs; thus, establishing the cost model of the machine endowment is essential to determine the cost of a machine required to complete a task.

We derive the MPPC that divides the tasks into two categories. Machines accomplish tasks below the curve, whereas humans perform tasks above the curve. Therefore, the MPPC provides a theoretical basis for human–machine task allocation.

Our model indicates the dynamic of future changes in human–machine task distribution. As technology advances, the curve moves upward. Machines can accomplish more tasks in existing tasks, while humans perform fewer tasks. Not all middle-skill tasks should be assigned to machines. If cognitive technologies develop faster, machines will replace mid- to high-skill jobs. Machine learning is an example of this phenomenon. Meanwhile, if flexibility technologies develop faster, machines such as soft robots will replace low-skill jobs. In the era of information technology, technology for cognitive ability develops faster than technologies for flexibility; thus, machines will replace mid- and high-skill jobs in the future. Additionally, changes in the minimum wage and the rate at which wages increase will cause changes in the allocation of tasks to humans and machines.

This study finds that using the aggregate endowment (the sum of flexibility and cognitive endowments) analysis method—that is, when the sum of flexibility and cognitive endowments is fixed—the model can give the conditions under which task polarization happens. Task polarization is an essential finding in economics. There is a lower and an upper bound of aggregate endowment. If a task requiring the aggregate endowment is less than the lower bound, the task is assigned to a machine. If a task requiring the aggregate endowment is higher than the upper bound, the task is assigned to a human.

The proposed model makes it possible to analyze the task assignment between humans and machines in detail, providing a theoretical foundation for corporate decision-making. The findings suggest that the education system should produce a middle-skilled workforce that is also capable of manual labor, as in the case of the German educational system.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Aaronson D, Phelan BJ (2019) Wage shocks and the technological substitution of low‐wage jobs. Econ J 129(617):1–34

Article   Google Scholar  

Acemoglu D (1999) Changes in unemployment and wage inequality: an alternative theory and some evidence. Am Econ Rev 89(5):1259–1278

Acemoglu D (2003) Cross‐country inequality trends. Econ J 113(485):F121–F149

Acemoglu D, Autor D (2011) Skills, tasks and technologies: Implications for employment and earnings. In: Handbook of Labor Economics, vol. 4, Part B. Elsevier, North Holland. pp. 1043–1171

Acemoglu D, Restrepo P (2018a) Low-skill and high-skill automation. J Hum Cap 12(2):204–232

Acemoglu D, Restrepo P (2018b) The race between man and machine: Implications of technology for growth, factor shares, and employment. Am Econ Rev 108(6):1488–1542

Acemoglu D, Restrepo P (2018c) Modeling automation. AEA Papers Proc 108:48–53

Acemoglu D, Restrepo P (2020) Robots and jobs: evidence from US labor markets. J Polit Econ 128(6):2188–2244

Alabdulkareem A, Frank MR, Sun L, AlShebli B, Hidalgo C, Rahwan I (2018) Unpacking the polarization of workplace skills. Sci Adv 4(7):eaao6030

Article   ADS   PubMed   PubMed Central   Google Scholar  

Atalay E, Phongthiengtham P, Sotelo S, Tannenbaum D (2020) The evolution of work in the United States. Am Econ J Appl Econ 12(2):1–34

Autor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J Econ Perspect 29(3):3–30

Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: an empirical exploration. Q J Econ 118(4):1279–1333

Article   MATH   Google Scholar  

Autor D, Katz LF, Kearney MS (2006) The polarization of the US labor market. Am Econ Rev 96(2):189–194

Basso HS, Jimeno JF (2021) From secular stagnation to robocalypse? Implications of demographic and technological changes. J Monet Econ 117:833–847

Brynjolfsson E, Mcaffe A (2017) The business of artificial intelligence. Harvard Bus Rev 7:3–11

Google Scholar  

Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358(6370):1530–1534

Article   ADS   CAS   PubMed   Google Scholar  

Cortes GM (2016) Where have the middle-wage workers gone? A study of polarization using panel data. J Labor Econ 34(1):63–105

De Vries GJ, Gentile E, Miroudot S, Wacker KM (2020) The rise of robots and the fall of routine jobs. Labour Econ 66:101885

Frank MR, Autor D, Bessen JE, Brynjolfsson E, Cebrian M, Deming DJ, Rahwan I (2019) Toward understanding the impact of artificial intelligence on labor. Proc Natl Acad Sci USA 116(14):6531–6539

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Chang 114:254–280

Goos M, Manning A (2007) Lousy and lovely jobs: the rising polarization of work in Britain. Rev Econ Stat 89(1):118–133

Goos M, Manning A, Salomons A (2009) Job polarization in Europe. Am Econ Rev 99(2):58–63

Goos M, Manning A, Salomons A (2014) Explaining job polarization: routine-biased technological change and offshoring. Am Econ Rev 104(8):2509–2526

Graetz G, Michaels G (2018) Robots at work. Rev Econ Stat 100(5):753–768

Huang MH, Rust RT (2018) Artificial intelligence in service. J Serv Res 21(2):155–172

Lee C, Kim M, Kim YJ, Hong N, Ryu S, Kim, HJ, Kim S (2017) Soft robot review. Int J Control Autom Syst 15(1), 3–15

Lordan G, Neumark D (2018) People versus machines: the impact of minimum wages on automatable jobs. Labour Econ 52:40–53

Malik AA, Bilberg A (2019) Complexity-based task allocation in human-robot collaborative assembly. Ind Rob 46(4):471–480

Michaels G, Natraj A, Van Reenen J (2014) Has ICT polarized skill demand? Evidence from eleven countries over twenty-five years. Rev Econ Stat 96(1):60–77

Ranz F, Hummel V, Sihn W (2017) Capability-based task allocation in human-robot collaboration. Procedia Manuf 9:182–189

Ross MB (2017) Routine-biased technical change: panel evidence of task orientation and wage effects. Labour Econ 48:198–214

Rus D, Tolley MT (2015) Design, fabrication and control of soft robots. Nature 521(7553):467–475

Spitz-Oener A (2006) Technical change, job tasks, and rising educational demands: looking outside the wage structure. J Labor Econ 24(2):235–270

Wang H, Totaro M, Beccai L (2018) Toward perceptive soft robots: progress and challenges. Adv Sci 5(9):1800541

Wang X (2020) Labor market polarization in Britain and Germany: a cross-national comparison using longitudinal household data. Labour Econ 65:101862

Yamaguchi S (2012) Tasks and heterogeneous human capital. J Labor Econ 30(1):1–53

Article   CAS   Google Scholar  

Yuan S, Gao Z, Dong M, Liu L, Xu T, Guo K, Wu F (2020) Evaluation of human-robot collaborative assembly task allocation plan. In: International Workshop of Advanced Manufacturing and Automation. Springer, Singapore. pp. 487–495

Download references

Author information

Authors and affiliations.

School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Qiguo Gong .

Ethics declarations

Competing interests.

The author declares no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Additional information.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Gong, Q. Machine endowment cost model: task assignment between humans and machines. Humanit Soc Sci Commun 10 , 129 (2023). https://doi.org/10.1057/s41599-023-01622-0

Download citation

Received : 08 September 2022

Accepted : 14 March 2023

Published : 25 March 2023

DOI : https://doi.org/10.1057/s41599-023-01622-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Google OR-Tools
• Español – América Latina
• Português – Brasil
• Tiếng Việt

Solving an Assignment Problem

This section presents an example that shows how to solve an assignment problem using both the MIP solver and the CP-SAT solver.

In the example there are five workers (numbered 0-4) and four tasks (numbered 0-3). Note that there is one more worker than in the example in the Overview .

The costs of assigning workers to tasks are shown in the following table.

The problem is to assign each worker to at most one task, with no two workers performing the same task, while minimizing the total cost. Since there are more workers than tasks, one worker will not be assigned a task.

MIP solution

The following sections describe how to solve the problem using the MPSolver wrapper .

Import the libraries

The following code imports the required libraries.

Create the data

The following code creates the data for the problem.

The costs array corresponds to the table of costs for assigning workers to tasks, shown above.

Declare the MIP solver

The following code declares the MIP solver.

Create the variables

The following code creates binary integer variables for the problem.

Create the constraints

Create the objective function.

The following code creates the objective function for the problem.

The value of the objective function is the total cost over all variables that are assigned the value 1 by the solver.

Invoke the solver

The following code invokes the solver.

Print the solution

The following code prints the solution to the problem.

Here is the output of the program.

Complete programs

Here are the complete programs for the MIP solution.

CP SAT solution

The following sections describe how to solve the problem using the CP-SAT solver.

Declare the model

The following code declares the CP-SAT model.

The following code sets up the data for the problem.

The following code creates the constraints for the problem.

Here are the complete programs for the CP-SAT solution.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2023-01-02 UTC.

• Advanced Search

Cooperative mapping task assignment of heterogeneous multi-UAV using an improved genetic algorithm

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, recommendations, an improved genetic algorithm with conditional genetic operators and its application to set-covering problem.

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an ...

Cooperative Multiple Task Assignment of Heterogeneous UAVs Using a Modified Genetic Algorithm with Multi-type-gene Chromosome Encoding Strategy

The cooperative multiple task assignment problem (CMTAP) of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) performing the Suppression of Enemy Air Defense (SEAD) mission against multiple ground stationary targets is studied in this ...

Modified multi-objective firefly algorithm for task scheduling problem on heterogeneous systems

Scheduling an application in a heterogeneous environment to find an optimal schedule is a challenging optimisation problem. Maximising the reliability of the application even when processors fails, adds more complexity to the problem. Both the ...

Information

Published in.

Elsevier Science Publishers B. V.

Netherlands

Publication History

Author tags.

• Heterogeneous system
• Cooperative mapping
• Task assignment
• Constrained optimisation
• Genetic algorithm
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Asana Vs. Todoist (2024 Comparison)

Published: Jul 26, 2024, 3:32pm

Table of Contents

Asana vs. todoist: at a glance, how asana and todoist stack up, cost of asana vs. todoist, customer reviews and reputation, top asana and todoist alternatives, bottom line, frequently asked questions (faqs).

Asana and Todoist are two reputable project management solutions that can help you stay organized and efficient. While they both offer a variety of features, each one has its own unique use cases. To help you decide which option is ideal for your unique business model and goals, we’ve created this Asana vs. Todoist review below.

Featured Partners

$3.49 per month

Yes (add-on)

On NordVPN's Website

On Surfshark's Website

$3.33/ month

1, 5 and 10

On Norton's Website

Private Internet Access

On Private Internet Access' Website

Founded in 2008, Asana focuses on agile project management through calendars, timelines, databases and kanban boards. It’s a robust platform that can allow you to collaborate with your team without the clutter of back-and-forth emails. You can assign each task to the right team member and break it down with subtasks that may also be assigned to different individuals.

Todoist has been around since 2007 and was designed to “help you prioritize the important things and juggle many things at once without feeling overwhelmed.” It’s a to-do list and productivity app but can also be used as a project management tool as it allows you to create and track projects with ease.

Starting price

$10.99 per user, per month

Free version

Key features

Variety of views, task dependencies and budgeting

Asana is a project management team that aims to improve productivity through customizable workflows and automations. Once you assign a due date to an individual task, you can track every team member’s progress in one place. Plus you can get a high-level overview of the status of each project at any time.

Since Asana seamlessly integrates with more than 200 popular tools, such as Google Sheets, Zoom and Microsoft Teams, you can streamline your workflows easily. You can get started with the free plan, which includes a long list of features, or upgrade to a paid plan and enjoy additional features, such as more automations and advanced reporting.

Learn more: Read our Asana review .

• Generous free plan
• Unlimited storage available
• Customizable Workflow Builder
• Gantt charts require integrations
• Top-tier plans can be expensive
• Slower loading times for larger projects

$4 per month

Role assignment capabilities, timelines and smart scheduling

Todoist is a flexible project management app that offers a few views, including list, calendar and kanban board. Whether you’d like to manage personal tasks, complete work-related projects or collaborate with teams, you’ll appreciate its user-friendly interface and affordability. The app offers Trevor AI, a smart schedule feature that recommends the best times to work on each task based on due dates, priorities and estimated completion times.

With the Beginner plan, which is free forever, you’ll be able to create five personal projects, use three filter views and track one week of activity history so you can gauge how you’re doing from a productivity standpoint. The paid plans, which are very budget-friendly, open the doors to 500 team projects, an artificial intelligence (AI) assistant and team billing.

Learn more: Read our Todoist review .

• Affordable plans
• Scheduling suggestions via Trevor AI
• Smartphone app for iOS and Android devices
• Project limits with all plans
• No off-line functionality
• Limited third-party integrations

Asana and Todoist have a lot in common. However, there are also many differences between them. The table below gives a brief overview of how these popular project management solutions compare.

When it comes to pricing, Asana is more expensive. While there is a Free forever plan, its paid plans start at $10.99 per user, per month. Todoist, which also offers a free plan called Beginner, has paid plans that start at $4 per user, per month. If you’re looking for a cost-effective solution, Todoist wins the cake.

Both Asana and Todoist come with a number of views you can use when you manage projects. Asana views include kanban board, list, timeline, calendar and Gantt chart. Its list view stands out in that it lists the tasks required for each step of the project rather than to-dos under each team member responsible for a task. Todoist only offers three views: list, kanban board and calendar.

Automations

Asana has advanced automations that automate workflows and repetitive tasks. It features more than 70 prebuilt rules in its rules gallery. You may use these rules to assign tasks, change dependencies, shift due dates and more automatically. While Todoist offers fewer automation capabilities, its AI-powered smart scheduling feature can make it easier for you to stay productive.

Ease of Use

Compared to Asana, Todoist is easier to use. Its simple interface makes it a breeze to create tasks, schedule due dates, add comments and mark tasks completed. Asana has far more features, so it may come with a steep learning curve, especially if you’ve never used it before or have more complex project management needs.

Both Asana and Todoist offer customizable templates that can allow you to get started right away. Asana’s templates are organized by use case, such as agile, CRM, project planning, marketing agendas and software development. Todoist’s template library is organized by broader categories, such as work, education and management. Plus, it has a search bar so you can find what you’re looking for quickly.

Integrations

There’s a good chance you use several tools for your business, such as Microsoft Teams, Slack and Zoom. While Asana and Todoist provide the ability to integrate their software with your tools, Asana supports more than 200 integrations, compared to Todoist, which offers just over 80 integrations.

Customer Support

Once you choose a project management platform, questions are bound to arise. With Asana, you may receive the support you need through a variety of channels, including an online contact form, knowledge base, Asana’s Academy training and community form. Todoist’s primary support channels are email, X and the Help Center.

Both Asana and Todoist offer free plans. However, Asana’s free plan is more robust as it allows you to collaborate with up to 10 team members and comes with unlimited tasks and projects. Meanwhile, Todoist’s free version is designed for five personal projects.

As far as paid plans are concerned, Asana’s range from $10.99 per user, per month, to $24.99 per user, per month. Todoist’s paid plans are a lot less expensive and cap out to $6 per user, per month. Both tools offer a discount if you pay annually rather than monthly.

Asana Pricing

Todoist Pricing

Although Asana is more popular than Todoist, it has many online complaints due to billing issues and glitches in the software. Asana only scored 2.2 out of 5 stars out of 178 reviews on Trustpilot. Fortunately, its ranking of 4.3 out of stars out of more than 9,000 reviews on G2 is better. Todoist earned a Trustpilot score of 3.3 out of 5 stars based on 42 reviews. On G2, Todoist ranked 4.4 out of 5 stars out of more than 700 reviews.

Once you do your research and learn more about Asana and Todoist, you may find that neither option is a good fit for your particular budget and needs. The good news is there are many other reputable project management tools out there. ClickUp, Wrike and Airtable are three platforms that made our list of the best project management software and are certainly worth exploring.

Asana and Todoist are both created to simplify your project management processes. While Asana’s all-in-one solution is customizable and scalable, Todoist offers a simpler, more affordable tool that’s ideal for personal needs or the basic projects you may have as a freelancer, startup or small business.

Asana is best for:

• Small businesses searching for a scalable project management platform
• Startups and small businesses looking for a more robust option with no shortage of integrations

Todoist is best for:

• Freelancers, solopreneurs, startups or small businesses in need of a budget-friendly project management tool
• Small businesses that prioritize project management that’s simple and affordable

Why do I need a project management tool, such as Asana or Todoist?

A project management tool, such as Asana and Todoist, can help you become more efficient and productive. You can use it to automate your processes and ensure you complete your simple and complex projects on time or way ahead of schedule.

Which is better: Asana or Todoist?

Whether Asana or Todoist is better depends on your particular needs. If scalability is important to you and you don’t mind paying more for feature-rich software, Asana is your best bet. However, if you work independently or have a very small team with basic projects, Todoist is the way to go.

Which platform is cheaper: Asana or Todoist?

Todoist is cheaper than Asana. Its paid plans start at $4 per user, per month, compared to Asana’s paid plans, which start at $10.99 per user, per month.

• Best Project Management Software
• Best Construction Project Management Software
• Best Project Portfolio Management Software
• Best Gantt Chart Software
• Best Task Management Software
• Free Project Management Software
• Best Enterprise Project Management Software
• Best Kanban Software
• Best Scrum Software
• Asana Review
• Trello Review
• monday.com Review
• Smartsheet Review
• Wrike Review
• Todoist Review
• Basecamp Review
• Confluence Review
• Airtable Review
• ClickUp Review
• Monday vs. Asana
• Clickup vs. Asana
• Asana vs. Trello
• Asana vs. Jira
• Trello vs. Jira
• Monday vs. Trello
• Clickup vs. Trello
• Asana vs. Wrike
• What Is Project Management
• Project Management Methodologies
• 10 Essential Project Management Skills
• SMART Goals: Ultimate Guide
• What is a Gantt Chart?
• What is a Kanban Board?
• What is a RACI Chart?
• What is Gap Analysis?
• Work Breakdown Structure Guide
• Agile vs. Waterfall Methodology
• What is a Stakeholder Analysis
• What Is An OKR?

Next Up In Project Management

• Project Management Software
• Asana Competitors & Alternatives
• Best Scheduling Apps
• Asana Review: Features, Pricing & More
• Trello Review: Features, Pricing & More

Smartsheet Vs. ClickUp (2024 Comparison)

Trello Vs. ToDoist (2024 Comparison)

Fax.Plus Review 2024: Features, Pros & Cons

Zoho Projects Review 2024: Features, Pros & Cons

Best B2B E-Commerce Platform Of 2024

Best Smart TV VPN Of 2024

Anna Baluch is a freelance writer from Cleveland, Ohio. She enjoys writing about a variety of health and personal finance topics. When she's away from her laptop, she can be found working out, trying new restaurants, and spending time with her family. Connect to her on LinkedIn.

Delay Risk Detection in Road Construction Projects Utilizing Large Language Model

• Conference paper
• First Online: 25 July 2024
• Cite this conference paper

• Gundidza Florence 13 ,
• Masato Kikuchi 13 &
• Tadachika Ozono 13  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1047))

Included in the following conference series:

• International Conference on Intelligent Systems Design and Applications

Construction projects worldwide consistently face the persistent challenges of time and cost overruns, wreaking havoc on budgets and economies. Effective mitigation necessitates identifying root causes—a complex task often dependent on deciphering project reports and subjective expert knowledge. Remarkably, there is a notable lack of suitable artificial intelligence (AI) techniques for addressing the complex challenges in road construction management. This study addresses this gap by employing a large language model (GPT-3), which is an advanced language model, to develop an unbiased delay risk detection framework. This research methodology involved a comprehensive literature review unveiling the limitations of conventional approaches that rely heavily on subjective data collection methods. To address these shortcomings, the proposed framework automates the generation of question–answer pairs using ChatGPT, thereby ensuring data consistency and efficiency. Real-time project reports serve as the crucible to validate the system’s efficacy, resulting in impressive performance metrics: high precision for “Class 0 (No Delay)” (0.79), “Class 1 (Delay Detected)” (0.91), and “Class 2 (False Positives)” (1.00), high recall for “Class 0” (1.00) and “Class 1” (0.89), a balanced F1-Score (0.90 overall), and an accuracy of 0.85, emphasizing its ability to minimize both false positives and false negatives. This study explored the potential of AI-driven solutions, such as GPT-3, to contribute to advancements in construction project management. Our framework presents a data-driven approach for forecasting project delays, enhancing monitoring capabilities, and bolstering sustainability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Mahamid, I., Bruland, A., Dmaidi, N.: Causes of delay in road construction projects. J. Manag. Eng. 28 , 300–310 (2012). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000096

Article   Google Scholar  

Frimpong, Y., Oluwoye, J., Crawford, L.: Causes of delay and cost overruns in construction of groundwater projects in developing countries, Ghana as a case study. Int. J. Project Manage. 321–326 (2003)

Google Scholar  

Kaming, P.F., Olomolaiye, P., Holt, G.D., Harris, F.C.: Factors influencing construction time and cost overruns on high-rise projects in indonesia. Construct. Manag. Econ. 15 , 83–94 (1997). https://api.semanticscholar.org/CorpusID:108527688

Agyekum-Mensah, G., Knight, A.: The professionals’ perspective on the causes of project delay in the construction industry. Eng. Constr. Archit. Manag. 24 , 828–841 (2017). https://doi.org/10.1108/ECAM-03-2016-0085

Zou, Y., Kiviniemi, A., Jones, S.: Retrieving similar cases for construction project risk management using natural language processing techniques. Autom. Constr. 80 , 66–76 (2017). https://doi.org/10.1016/j.autcon.2017.04.003

Marzouk, M., Enaba, M.: Text analytics to analyze and monitor construction project contracts and correspondence. Autom. Constr. 98 , 265–274 (2018). https://doi.org/10.1016/j.autcon.2018.11.018

Nedeljković, Kovašević, M.: Building a construction project key-phrase network from unstructured text documents. J. Comput. Civil Eng. 31 , 04017058 (2017). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000708

Alsubaey, M., Asadi, A., Makatsoris, H.: A Naïve Bayes approach for EWS detection by text mining of unstructured data: a construction project case, pp. 164–168 (2015). https://doi.org/10.1109/IntelliSys.2015.7361140

Vaswani, A., et al.: Attention is all you need. In: NIPS (2017). https://doi.org/10.48550/arXiv.1706.03762

Kim, Y., Bang, S., Sohn, J., Kim, H.: Question-answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers. Autom. Constr. 134 , 104061 (2022). https://doi.org/10.1016/j.autcon.2021.104061

Amer, F., Jung, Y., Golparvar-Fard, M.: Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction. Autom. Constr. 132 , 103929 (2021). https://doi.org/10.1016/j.autcon.2021.103929

Download references

Acknowledgment

This work was supported in part by JSPS KAKENHI Grant Numbers JP19K12266, JP22K18006.

Author information

Authors and affiliations.

Nagoya Institute of Technology, Nagoya, Aichi, 4668555, Japan

Gundidza Florence, Masato Kikuchi & Tadachika Ozono

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Gundidza Florence .

Editor information

Editors and affiliations.

School of Artificial Intelligence, Bennett University, Greater Noida, Uttar Pradesh, India

Ajith Abraham

Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

University of Applied Sciences and Arts Northwestern, Olten, Switzerland

Thomas Hanne

National University of Kaohsiung, Kaohsiung, Taiwan

Tzung-Pei Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

Florence, G., Kikuchi, M., Ozono, T. (2024). Delay Risk Detection in Road Construction Projects Utilizing Large Language Model. In: Abraham, A., Bajaj, A., Hanne, T., Hong, TP. (eds) Intelligent Systems Design and Applications. ISDA 2023. Lecture Notes in Networks and Systems, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-031-64836-6_11

Download citation

DOI : https://doi.org/10.1007/978-3-031-64836-6_11

Published : 25 July 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-64835-9

Online ISBN : 978-3-031-64836-6

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Finite Elemente

• Highlights SOFiSTiK 2025
• Highlights SOFiSTiK 2024
• Highlights SOFiSTiK 2023
• Bridge Design
• Building Design
• Steel Design
• Geotechnics/Tunneling
• Reliability Analysis
• Rhinoceros Interface
• Bridge + Infrastructure Modeler
• Reinforcement
• Analysis + Design for Revit

Analytical Model Generator

FEM - Packages

• SOFiSTiK FEM Packages
• 3D FEM professional
• 3D FEM premium
• 3D FEM ultimate / 50

Further Products

The highlights of version SOFiSTiK | 2025

Connecting Use Cases

Benefit from the experience of the only construction software manufacturer with products for structural analysis and design. The current challenges of digitalization in structural design require the combination of use cases. Start consistently using the advantages of BIM models for structural analysis by automatically generating the analytical model from the architectural model and adapting it appropriately - quite simply with the milestone product Analytical Model Generator for Autodesk Revit.

Stay in the BIM workflow with member design and overall model calculation with the new user experience and transparent load transfer functions of the structural analysis interface Analysis + Design from the SOFiSTiK FEM Packages . Advise building owners using the data from the Life Cycle Assessment Task in the SOFiSTiK Structural Desktop , combining statics and sustainability made easy. 

At the interface to Detailing, the Rebar Check functionality provides you with the optimum connection for loss-free transfer of the design results for 3D reinforcement modeling. While the derivation of reinforcement plans for bridges and infrastructure structures was previously difficult, it is now possible both precisely with tendons in the Bridge + Infrastructure Modeler and consistently with the proposal for orderable and sensible simplifications to the 2D reinforcement plan in Revit with SOFiSTiK Reinforcement . 

Discover the highlights of the new version in our focus areas:

Analytical Model Generator: Automated creation and effortless optimization of the analytical model in Autodesk® Revit®

• Use the created analytical model with third party software solutions other than SOFiSTiK
• Optimization of curved elements, more user control for vertical alignments of floors and beams 
• New tool set for enhanced usability, providing commands to modify existing analytical models and add multiple loads and boundary conditions in just a few clicks

The Analytical Model Generator is now available as a stand-alone product , as well as part of all FEM packages .*

Bridge + Infrastructure Modeler: Modeling axis-based, parametric bridge and tunnel structures in Autodesk® Revit® 

• Add Tendons to your model with the new Tendon Generator 
• Custom Styles for Axis/Edge Dimensions 

For more information on the Bridge + Infrastructure Modeler and a free trial, please click here .

SOFiPLUS /-X: AutoCAD® add-on for graphical modeling 

• New Tendon Manager for efficient handling of generated tendons in your model 
• Fast modeling of curves between reference lines 
• Improvement of Design Element definition for Composite Cross Sections and Shear Walls 
• New tool for easy renumbering of structural elements  

SOFiPLUS is part of all three FEM packages .

SOFiSTiK Rhinoceros interface: Rhinoceros from Robert McNeel & Associates / Grasshopper® add-on for graphical and parametric modeling

• Optimized workflow for multi-girder bridges with new components and a comprehensive tutorial
• New methods for defining prestress geometry using 2D projected views
• Introducing an automatic loader for controlling and managing installed versions of the SOFiSTiK Interface 

You can find more information on the Rhinoceros Interface here .

Discover the new SOFiSTiK Viewer for a modern visual post-processing experience 

• Explore and control the FE model with ease 
• Visualize and animate the deformed FE structure
• Filter the model by primary and secondary groups in real time
• Visually inspect generated towers and storey levels
• Experience high performance powered by state-of-the-art modern graphic technology

The Viewer is now the default visualization in the SOFiSTiK Structural Desktop and part of all three FEM packages .

Analysis + Design for Revit: Structural analysis of buildings and member design within Autodesk® Revit® 

• New Main System command as base camp of the project to never get lost in the BIM model 
• Generate in a click subsystems from any part of the model to analyse individually
• Manage easily and efficiently many subsystems with the Analysis Browser
• Bring speed and efficiency to your analyses with the calculate immediately concept
• Connect easily SOFiSTiK result tables of your choosing to the BIM model by accessing the GUID of its host member in Result Viewer 

Analysis + Design for Revit is part of all three FEM packages .* More information here . 

New Load Take Down Workflow in SOFiSTiK Analysis+Design for Revit

• For more transparency and more flexibility when transferring loads between subsystems
• Supports any project layout including the more complex e.g. presenting transfer floors or split levels
• Dedicated commands to evaluate the Load Take Down results are seamlessly integrated to existing post-processing tools (Reports, Graphic, SOFiLOAD module etc.)

Analysis+Design for Revit is part of all three FEM packages .* More information here .

New Cross Sections

• New standard cross section for one sided T-beam on edges
• Define parametric cross sections with elliptic boundary e.g. for bridge piers as well as asymmetric chamfer through two radii

The new cross sections are part of all three FEM packages .

Beam Design improved for member design workflow in 3D models

• Grouping of geometric similar beams
• Concise representation of decisive beam members and load case combinations in the report
• Manual definition of the reinforcement layout for SLS checks
• Improved calculation of deflection within the main model according to DIN EN 1992-1-1 ch. 7.4.3

The Beam Design Workflow is part of all three  FEM packages .*

Life Cycle Assessment

• New task Life Cycle Assessment allows to estimate the environmental impact of your structural model
• Definition of Global Warming Potential (GWP) values per life cycle phase for the materials in the system
• GWP assessment for the different phases with consideration of calculated reinforcement from the design
• Result output for: material, groups, secondary groups and design element types
• Connection to the database ÖKOBAUDAT from the German Federal Ministry for Housing, Urban Development and Building (BMWSB) (DIN EN 15804+A1 and +A2)
• Default values for Concrete and Reinforcement Steel
• Compare the results of different variants by exporting them into one Excel file 

The Life Cycle Assessment is part of all three  FEM packages .*

Composite Bridges: Elastic ULS Design Checks 

• Module COMPOSITE (available since Version 2024) now also able to do elastic ULS design checks
• Cross-section classification
• Stress checks structural steel - section class 1 to 4
• Stress checks reinforcement and concrete

The module COMPOSITE is part of the package  3D FEM ultimate/ultimate 50 .*

New Graphical User Interface for Dynamic Analysis of Railway Bridges

• New graphical task for Dynamic Moving Load Train Analysis
• Improved automatic selection of time step size

For the Dynamic Moving Load Train Workflow, the package  3D FEM ultimate/ultimate 50 * in combination with either the license LIN-DYN or NONL-DYN is required.  

Seismic Response Spectrum Analysis (RSA) for Buildings

• Alternative modeling option: Maintain rigid diaphragms from COR computation consistently throughout the whole analysis workflow
• Refined stiffness modifiers provide more flexibility in engineering judgement for adequately resolving structural behavior (stiffness reduction due to concrete cracking)
• Alternative mode scaling methods (normalization) are convenient, e.g. for calibration in context of wind tunnel experiments

For the RSA Workflow the package  3D FEM premium * in combination with either the license BASE-DYN, LIN-DYN or NONL-DYN is required. 

Performance-Based Seismic Design of Bridge Piers (Pushover)

• New concept of performance categories and limit-strain based criteria​
• Full plastic hinge support for concrete, structural steel and composite cross sections​
• Support for nonlinear soil-pile interaction (P-Y curves)​

For the Pushover Workflow, the package  3D FEM ultimate/ultimate 50 * in combination with either the license BASE-DYN, LIN-DYN or NONL-DYN is required. 

Rebar Check

• Live display of reinforcement quantities read from the BIM model (provided reinforcement) and from a SOFiSTiK data source (required reinforcement)
• Checks available for Slabs, Columns and Beams
• Down stand beams also account for reinforcement from the slab

The Rebar Check is available in Analysis + Design  and Reinforcement . 

Reinforcement: Create 2D reinforcement sheets out of 3D-Rebar models in Autodesk® Revit®

• Improvements for infrastructure projects and complex formwork geometry
• Warnings appear now sooner, with detailed description and proposal for fix
• New functionality to override or simplify the rebar set geometry as a two-dimensional shape
• 3D Spacers are supported by the Schedule tool also in hosts with varying height
• Explode tool will now create Shape Driven sets for varying distributions

For more information on Reinforcement and a free trial, please click here .

* The function described requires products of the newest price- and licensing model. Our sales team will be happy to inform you about upgrade options: [email protected] .