A practical method for evaluating worker allocations in large-scale dual resource constrained job shops

Lobo, Benjamin J.; Wilson, James R.; Thoney, Kristin A.; Hodgson, Thom J.; King, Robert E.

doi:10.1080/0740817x.2014.892231

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Yildiz and Eski (2006) worked on the dual-resource-constrained assembly lines using artificial neural networks to get optimum schedules for the arrangement of workers on different workstations, considering production efficiency metrics. Lobo et al (2013Lobo et al ( , 2014 developed heuristics to determine a lower bound to the maximum job lateness and a possible schedule that fits the determined lower bound in a dual-resource-constrained job shop setup. Huang et al Engineering and Operations Management, Melbourne, Australia, November 14-16, 2023 © IEOM Society International (2014) worked on scheduling the optimization strategy for dual-resource job-shop scheduling (DR-JSP) with heterogeneous workers by using a pheromone branch genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Agrawal,

Vital-Soto,

Olivares-Aguila

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Scheduling is a vital function for efficiently operating flexible job-shop systems. Traditionally, that function considers assigning jobs to machines and their sequence. However, machines need to be operated by another set of resources (i.e., workers). Due to the fact that operators are skilled workers, the available pool is limited. Hence, the interaction of machines and humans needs to be studied in an integral manner to address the scheduling problem. In this article, a novel precedence variable-based, mixed-integer linear programming model is developed for the dual-resource flexible job-shop problem. The mathematical formulation deals with the optimal assignment of machines and workers to operations and the operation sequence in both resources by minimizing the makespan. The model gives an exact solution by solving both the assignment and the sequencing problems concurrently. The model was implemented in Docplex and was run on three instances of varying sizes. The model solved the three introduced examples, including a large instance involving 20 operations with 4 workers and 4 machines, using only 1662 variables and 5217 constraints in 156.43 seconds, indicating that the proposed model is adequate. The model can be used to label training examples for machine learning-based techniques as well as help track and compare models developed using heuristics.

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Section: Literature Reviewmentioning

confidence: 99%

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Agrawal,

Vital-Soto,

Olivares-Aguila

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…The ratio of the number of workers to the number of machines expressed as a percentage between 0% and 100% is defined as the "staffing level" (Lobo et al, 2014). Since the staffing level in DRC systems is usually less than 100% (Jaber & Neumann, 2010), workers are transferred between tasks to fulfil the orders and share the workload.…”

Section: Drc Systems With Human Factorsmentioning

confidence: 99%

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

Sadeghigivi¹

2021

Preprint

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Dual-Resource Constrained (DRC) systems consist of two resources: workers and machines (stations). DRCs have become common in manufacturing and service firms that emphasise flexibility, where workers perform different tasks. Although having a flexible workforce is beneficial, it comes at a cost. When workers alternate between different jobs the productivity of the system is affected. On one hand the system becomes more responsive to changes (internal/external), and on the other hand worker productivity and system throughput deteriorate because of the loss of knowledge and workers’ fatigue. This subjects workers to conflicting phenomena. When workers are performing a task they are learning but also accumulating fatigue, which may result in error or injury. When transferred to another task, or on a break, workers may forget what they have learnt but at the same time recover from fatigue, either fully or partially. In particular, forgetting and fatigue are interesting to be considered as they directly affect the quality of products. This research investigates the effects of workers’ learning-forgetting and fatigue-recovery on DRC systems. First, it modifies a known learning-forgetting model by accounting for fatigue and recovery. Second, it assumes that the quality of a production process may deteriorate and generate defective items that require rework. Third, a human error model is developed that considers human learning-forgetting and fatigue-recovery in producing defective items. Fourth, a comprehensive model is developed that integrates learning, forgetting, fatigue, and recovery into a DRC system with quality consideration. This model is investigated for different transfer and flexibility policies. Numerical results provide insights and guidelines that may help operations managers with decisions on how to improve a system’s performance and throughput, while considering worker welfare. Results indicate that it is important to consider workers capabilities and limitations when designing manufacturing systems. They also suggest that ignoring human restrictions and abilities results in unrealistic production planning and erroneous cost estimation.

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“…For example, Lobo et al (2013aLobo et al ( , 2013b studied the assignment of each worker to a specific machine group in a DRC job shop to minimize the maximum lateness of jobs. Lobo et al (2014) then revised the approach of Lobo et al (2013b) for the application of large-scale problems with tractable computational complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

A game theory model based on Gale-Shapley for dual-resource constrained (DRC) flexible job shop scheduling

Renna¹,

Thürer²,

Stevenson³

2020

10.5267/j.ijiec

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Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. "when" and "where" rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a 'couple' made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to "when" and "where" rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined.

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A practical method for evaluating worker allocations in large-scale dual resource constrained job shops

Cited by 12 publications

References 22 publications

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

A game theory model based on Gale-Shapley for dual-resource constrained (DRC) flexible job shop scheduling

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