Multiobjective optimization for complex flexible job-shop scheduling problems

Tamssaouet, Karim; Dauzère‐Pérès, Stéphane; Knopp, Sebastian; Bitar, Abdoul; Yugma, Claude

doi:10.1016/j.ejor.2021.03.069

Cited by 40 publications

(10 citation statements)

References 39 publications

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“…Moreover, for the simultaneous optimization of the makespan and the TCE , we construct the Pareto optimal solution set. For basic information on multiobjective optimization concepts such as the Pareto optimal solution set and domination, one can refer to Tamssaouet et al 35 . In each iteration, the sparrows look for workable answers, discover nondominant solutions, and include them in the Pareto optimal solution set.…”

Section: Improved Multiobjetive Sparrow Search Algorithm (Imossa)mentioning

confidence: 99%

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Li¹,

Chen²

2023

Sci Rep

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One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algorithm (IMOSSA) is developed to find the optimal solution. Finally, we conduct computational experiments, including a comparison between IMOSSA and the nondominated sorting genetic algorithm II (NSGA-II), Jaya and the mixed integer linear programming (MILP) solver of CPLEX. The results demonstrate that IMOSSA has high precision, good convergence and excellent performance in solving the GFJSP in low-carbon manufacturing systems.

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Section: Improved Multiobjetive Sparrow Search Algorithm (Imossa)mentioning

confidence: 99%

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Li¹,

Chen²

2023

Sci Rep

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“…Algorithms start by building a high-quality permutation solution, which is then improved in the second stage, creating nonpermutation solutions by changing the order of jobs on some machines. Tamssaouet et al [16] have proposed a scheduling framework for solving the complex multiobjective job-shop scheduling problem resulting from production. To produce practical and meaningful industrial schedules, this study extends the proposed batch approach by considering periods of unavailability and minimum time delays and simultaneously optimizing various industry-related criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Solving the Flow‐Shop Scheduling Problem with Sequence‐Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry)

Mohammadi

Shayannia

Lotfi

et al. 2022

Discrete Dynamics in Nature and Society

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Progress in today’s modern industry requires a lot of knowledge, one of which is scheduling. Flow-shop scheduling is one of the most widely used optimization problems. In this research, considering the importance of simultaneously order in different stages of production in the automotive industry, and also in order to make the problem more practical, we have investigated the problem of scheduling the flow-shop, taking into account the lead time and the costs of each order. Due to the fact that in most research studies, the lead time and costs of an order have been ignored because they made it difficult to find the initial solution to the problem. In this research, using the firefly meta-heuristic method, a suitable solution is provided to overcome this problem. Therefore, considered objective function is to minimize the total completion time. Absolute relative error (ARE) has been used to validate the model in a deterministic and meta-heuristic mode. According to the ARE result, the difference in the results between the two algorithms is negligible. Then, the sequence results are determined according to the desired algorithm for 5 tasks considered for automobile parts. The results show that the completion time of job 1 is 1397.85; job 2 is 771.44; job 3 is 608.65; job 4 is 1163.87; and job 5 is 479.45.

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“…Moreover, for the simultaneous optimization of the makespan and the TCE, we constructed the Pareto optimal solution set. To get a basic knowledge of multi-objective optimization concepts like Pareto optimal solution set and domination, one can refer to Tamssaouet et al 30 . In each iteration, sparrows look for workable answers, discover nondominant solutions, and include them into the Pareto optimal solution set.…”

Section: Improved Multi-objective Sparrow Search Algorithm(imossa)mentioning

confidence: 99%

Minimizing the Makespan and the Carbon Emissions in Green Flexible Job Shop Scheduling Problem with Learning Effect

Chen

2022

Preprint

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One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objectives to simultaneously minimze the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multi-objective optimization model. Then the improved multi-objective sparrow search algorithm (IMOSSA) is developed for the optimal solution. Finally, this paper conducted computational experiments, including the the comparison between IMOSSA and the algortithm of GA and Jaya. The resluts demonstrate that the IMOSSA has a high precision, good convergence and excellent performance to solve the GFJSP in low-carbon manufacturing systems.

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Multiobjective optimization for complex flexible job-shop scheduling problems

Cited by 40 publications

References 39 publications

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Modeling and Solving the Flow‐Shop Scheduling Problem with Sequence‐Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry)

Minimizing the Makespan and the Carbon Emissions in Green Flexible Job Shop Scheduling Problem with Learning Effect

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