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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.
|
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|
|
|
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---|---|---|---|---|---|---|
| 0 | 6 | 4 | 0 | 0 | 12 |
| 6 | 0 | 8 | 12 | 3 | 0 |
| 4 | 8 | 0 | 0 | 11 | 0 |
| 0 | 12 | 0 | 0 | 5 | 0 |
| 0 | 3 | 11 | 5 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 |
| ||
---|---|---|
|
| |
|
| |
| 5 | 10 |
| 2 | infinity |
| 4 | 4 |
| 6 | 3 |
| 5 | 2 |
| infinity | 4 |
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
|
|
---|---|
t1 | n1 |
t2 | n1 |
t3 | n1 |
t4 | n2 |
t5 | n2 |
t6 | n2 |
Cost of Execution in Serial Assignment:
Cost of Communication in Serial Assignment:
Case2: Optimal Assignment
|
|
---|---|
t1 | n1 |
t2 | n1 |
t3 | n1 |
t4 | n1 |
t5 | n1 |
t6 | n2 |
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.
![task assignment model Optimal Assignment using Minimal Cut set](https://media.geeksforgeeks.org/wp-content/uploads/20220502134640/Taskassignmentindistributedsystem.jpg)
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- 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
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- 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.
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The future of the labor force: higher cognition and more skills
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Robots, labor markets, and universal basic income
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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, \(a^{\frac{1}{\alpha }} \le \beta\) .
![task assignment model figure 1](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig1_HTML.png)
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 ).
![task assignment model figure 2](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig2_HTML.png)
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.
![task assignment model figure 3](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig3_HTML.png)
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.
![task assignment model figure 4](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig4_HTML.png)
Lower bound when θ L = θ 1 .
![task assignment model figure 5](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig5_HTML.png)
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.
![task assignment model figure 6](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig6_HTML.png)
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.
![task assignment model figure 7](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig7_HTML.png)
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.
![task assignment model figure 8](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-023-01622-0/MediaObjects/41599_2023_1622_Fig8_HTML.png)
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.
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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
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DOI : https://doi.org/10.1057/s41599-023-01622-0
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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.
Worker | Task 0 | Task 1 | Task 2 | Task 3 |
---|---|---|---|---|
90 | 80 | 75 | 70 | |
35 | 85 | 55 | 65 | |
125 | 95 | 90 | 95 | |
45 | 110 | 95 | 115 | |
50 | 100 | 90 | 100 |
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.
![](http://charunivedita.online/777/templates/cheerup2/res/banner1.gif)
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.
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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
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$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.
Asana | Todoist | |
---|---|---|
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
Plan | Personal | Starter | Advanced |
---|---|---|---|
Todoist Pricing
Plan | Beginner | Pro | Business |
---|---|---|---|
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.
Templates | Budgeting | Integrations | Free Plan | Starting Price | |
---|---|---|---|---|---|
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.
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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.
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This work was supported in part by JSPS KAKENHI Grant Numbers JP19K12266, JP22K18006.
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Gundidza Florence, Masato Kikuchi & Tadachika Ozono
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Correspondence to Gundidza Florence .
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Ajith Abraham
Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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Thomas Hanne
National University of Kaohsiung, Kaohsiung, Taiwan
Tzung-Pei Hong
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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
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Finite Elemente
- Highlights SOFiSTiK 2025
- Highlights SOFiSTiK 2024
- Highlights SOFiSTiK 2023
- Bridge Design
- Building Design
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- Geotechnics/Tunneling
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- Rhinoceros Interface
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- Reinforcement
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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:
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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 .*
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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 .
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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 .
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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 .*
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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 .*
![V2025 Teaser Composite task assignment model](https://www.sofistik.com/fileadmin/_processed_/3/1/csm_V2025_teaser_composite_61fd5c3e64.png)
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.
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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 .
![V2025 Rebar Check task assignment model](https://www.sofistik.com/fileadmin/_processed_/b/6/csm_V2025_Rebar_Check_8817a27ef1.jpg)
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] .
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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 ...
The Task Assignment Model. In this work, we assume that each task is assigned to one team, and each team can be assigned with no tasks or several tasks. Moreover, each team can do no more than one task at once, and the tasks are carried out continuously. For the task assignment model, it has one objective, which is minimizing the final ...
First, a dynamic task assignment model is constructed using multi-agent reinforcement learning to depict the relevant elements of aviation emergency rescue and its organizational structure. Second, a training framework based on the PPO algorithm is built to train the model. Finally, experiments based on a simulation example reveal that the ...
The model can be applied to human-machine task assignment decisions in industry and services. Humanities and Social Sciences Communications - Machine endowment cost model: task assignment ...
This section presents an example that shows how to solve an assignment problem using both the MIP solver and the CP-SAT solver. Example. In the example there are five workers (numbered 0-4) and four tasks (numbered 0-3).
needed is a process by which the task doer has a clear understanding of the task they are to do, can commit themselves to doing it and so are in a position to accept their manager's judgement of their performance as fair. A simple, powerful, and useful tool in Systems Leadership is the task assignment model
Thirdly, the mathematical model of task assignments, with its solution and dynamic task re-assignment policy for minimizing the cycle time of business processes are developed. Fourthly, a set of simulation experiments and a practical application are conducted to verify the effectiveness, advantage and practicability of the proposed approach. ...
Brent Lagesse. University of Texas at Arlington Department of Computer Science and Engineering Box 19015 Arlington, TX 76013. Abstract- The assignment of tasks to employees is one of the most essential aspects of a project manager's job. A situation with employees working on tasks that they are not well-suited for can lead to a significant loss ...
A task assignment model and its application for crowdsourcing project factored multi-objective and risks. Applied computing. Operations research. Decision analysis. Multi-criterion optimization and decision-making. Recommendations. Crowdsourcing usage, task assignment methods, and crowdsourcing platforms: A systematic literature review.
Furthermore, the task assignment algorithm based on the organizational evolution model is proposed. Finally, the simulation results demonstrate the rationality of the proposed task assignment model. It can be employed to perform the quantitative analysis of tasks assignment and help managers make decisions.
Worked example of assigning tasks to an unequal number of workers using the Hungarian method. The assignment problem is a fundamental combinatorial optimization problem. In its most general form, the problem is as follows: The problem instance has a number of agents and a number of tasks.Any agent can be assigned to perform any task, incurring some cost that may vary depending on the agent ...
Based on typical task assignment model, Jiang et al. [] modeled the UAV logistics task assignment problem as a model for vehicle routing problems with time window.The model can achieve an effective combination of logistics system and task assignment strategies by considering various constraints such as weight coefficient, time window constraint, and UAVs.
The performance of task assignment model is evaluated against a dynamic programming method, and a First-In-First-Out heuristic which serves as the baseline. A case study based on the City of London is proposed to demonstrate the applicability of the integrated model. Results demonstrate the effectiveness of the mixed-integer approach in ...
An optimization model of task assignment problem, which incorporates some factors not previously considered together (multiskilled employees and their skill levels, task iteration and the precedence constraints), is proposed. The objective function considered is to minimize the duration of the project and balance the workloads of the employees ...
Then, based on the discrete time finite state space mean field game, a task assignment model in the discrete task space is constructed, and the minimum costs obtained in the trajectory planning are coupled into the model as a reference, the task assignment strategies are finally obtained. Moreover, we give a specific example of the proposed ...
overcomes the lack of role-based task assignment in current workflow management systems. Similarly, Eiselt and Marianov (2008) developed a mathematical model for the assignment of tasks to ...
PDF | On Jan 1, 2014, Cansu Kandemir and others published Employee Task Assignments for Organization Modeling: A Review of Models and Applications | Find, read and cite all the research you need ...
Task assignment An intuitive task assignment strategy is Round-Robin (RR) which overlooks the reliability model and randomly assigns tasks for the tasks [21], [22], [23]. Recently, some quality-aware task assignment mechanisms that incorporate reliability model into the design of online task assignment mechanisms have been proposed to assign ...
Mobile crowd sensing (MCS) is an emerging approach to collect data using smart devices. In MCS, task assignment is described as assigning existing tasks to known workers outside the constraints of task demand attributes and worker attributes, and maximizing the profit of the platform. However, workers and tasks often exist in different environments and heterogeneous features such as workers ...
The performance of task assignment model is evaluated against a dynamic programming method, and a First-In-First-Out heuristic which serves as the baseline. A case study based on the City of London is proposed to demonstrate the applicability of the integrated model. Results demonstrate the effectiveness of the mixed-integer approach in ...
Specifically, it considers area mapping task requirements, UAV kinematics constraints, resource constraints (maximum endurance and sensor capabilities), and task number ceiling constraints; and optimises the task sequence based on the objective function that considers UAVs coverage path planning characteristics.
In actual multitask assignment scenarios in the crowdsensing(CS) systems, the sensing task can be divided into several subtasks. However, the similarity between the sub tasks is not considered in the existing methods, and its overall utility needs to be improved. To solve this problem, we propose TSMA, a double auction model based on task similarity for multitask crowdsensing systems. First, a ...
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 ...
To maximize the number of global task assignments, the model first applies an improved greedy assignment algorithm to convert the user-knowledge task maximum assignment problem into the maximum weight problem in bipartite graphs. Based on the matching value between a task and a user, a task is assigned to the user with a high matching value. ...
The model was exposed to a curated dataset, which allowed its parameters to be adjusted to the specific task at hand. Model Integration: After training and fine-tuning, the GPT-3 model is seamlessly integrated into the RCDRDS system. It becomes an integral part of the system and generates answers based on user input.
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