Non-permutation flowshop scheduling with dual resources

Mehravaran, Yasaman; Logendran, Rasaratnam

doi:10.1016/j.eswa.2013.03.007

Cited by 27 publications

(4 citation statements)

References 30 publications

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“…In these problems, besides the schedule of jobs on machines, the schedule of jobs on other resources and their interactions should be considered. In RCPMSP for a specific sequence of jobs on machines, there is an optimal sequence of jobs on resources and conversely, for a specific sequence of jobs on resources, there is an optimal sequence of jobs on machines (Mehravaran and Logendran 2013). On the other hand, jobs may often not be processed on any of the available machines but rather must be processed on a machine belonging to a specific subset of the machines (Pinedo 1995).…”

Section: Introductionmentioning

confidence: 94%

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Afzalirad

Shafipour

2015

J Intell Manuf

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This study addresses a resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions. Majority of the traditional scheduling problems in parallel machine environment deal with machine as the only resource. However, other resources such as labors, tools, jigs, fixtures, pallets, dies, and industrial robots are not only required for processing jobs but also are often restricted. Considering other resources makes the scheduling problems more realistic and practical to implement in manufacturing environments. First, an integer mathematical programming model with the objective of minimizing makespan is developed for this problem. Noteworthy, due to NP-hardness of the considered problem, application of meta-heuristic is avoidable. Furthermore, two new genetic algorithms including a pure genetic algorithm and a genetic algorithm along with a heuristic procedure are proposed to tackle this problem. With regard to the fact that appropriate design of the parameters has a significant effect on the performance of algorithms, hence, we calibrate the parameters of these algorithms by using the response surface method. The performance of the proposed algorithms is evaluated by a number of numerical examples. The computational results demonstrated that the proposed genetic algorithm is an effective and appropriate approach for our investigated problem.

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Section: Introductionmentioning

confidence: 94%

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Afzalirad

Shafipour

2015

J Intell Manuf

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“…All papers draw similar conclusions regarding the results, where the quality of the solution or the best solution method depends on the characteristics of the flow shop (e.g., number of machines or number and types of resource constraints). Logendran (2013) studies the flow shop scheduling problem where the additional resource is labor. The workforce is multiskilled and sequence-dependent setup times are also considered.…”

Section: Flow Shopmentioning

confidence: 99%

Production, maintenance and resource scheduling: A review

Geurtsen

Didden

Adan

et al. 2023

European Journal of Operational Research

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“…Several heuristic models have also been proposed, such as the variable neighborhood search approach by Deming and Guo (2014). Meanwhile, Mehravaran and Logendran (2013) developed a linear mixed-integer mathematical model and three metaheuristic algorithms to solve a bi-criteria non-permutation flow shop with dual resources and sequencedependent setup times. Zhang et al (2017) proposed a hybrid discrete particle swarm optimization algorithm to solve the DRC job shop scheduling problem with resource flexibility.…”

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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Non-permutation flowshop scheduling with dual resources

Cited by 27 publications

References 30 publications

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Production, maintenance and resource scheduling: A review

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

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