Scheduling a Bi-Objective Hybrid Flow Shop with Sequence-Dependent Family Setup Times Using Metaheuristics

Fadaei, Mohammad Mehdi; Zandieh, M.

doi:10.1007/s13369-013-0611-4

Cited by 18 publications

(3 citation statements)

References 28 publications

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“…A metaheuristic approach based on GA is proposed to minimize the makespan and the total tardiness of jobs. Fadaei and Zandieh (2013) considered group scheduling in the HFS scheduling problem with identical parallel machines within the area of sequence-dependent family setup times and two objectives of minimizing makespan and total tardiness of jobs. They focused on three multi-objective algorithms, multi-objective GA, sub-population GA-II and non-dominated sorting GA-II (NSGA-II), to solve the mentioned prob-lem.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

Mousavi,

Shahnazari-Shahrezaei

2023

Management and Production Engineering Review

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The paper considers the production scheduling problem in a hybrid flow shop environment with sequence-dependent setup times and the objectives of minimizing both the makespan and the total tardiness. The multi-objective genetic algorithm is applied to solve this problem, which belongs to the non-deterministic polynomial-time (NP)-hard class. In the structure of the proposed algorithm, the initial population, neighborhood search structures and dispatching rules are studied to achieve more efficient solutions. The performance of the proposed algorithm compared to the efficient algorithm available in literature (known as NSGA-II) is expressed in terms of the data envelopment analysis method. The computational results confirm that the set of efficient solutions of the proposed algorithm is more efficient than the other algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

Mousavi,

Shahnazari-Shahrezaei

2023

Management and Production Engineering Review

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“…Behnamian and Zandieh [18] developed a hybrid algorithm of PSO, SA and variable neighborhood search (VNS) to solve the SDST HFS scheduling with position-dependent learning effects. Fadaei and Zandieh [19] considered group scheduling in the problem of bi-objective HFS scheduling within the area of sequence-dependent family setup times. They focused on three multi-objective algorithms, multi-objective GA, sub-population GA-II and non-dominated sorting GA-II (NSGA-II), to solve the mentioned problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Bi-objective scheduling for re-entrant hybrid flow shop with learning effect and setup times

et al. 2017

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The production scheduling problem in hybrid flow shops is a complex combinatorial optimization problem observed in many real-world applications. The standard hybrid flow shop problem involves often unrealistic assumptions. In order to address the realistic assumptions, four additional traits were added to the proposed problem. These include re-entrant line, setup times, position-dependent learning effects, and the consideration of maximum completion time together with total tardiness as objective function. Since the proposed problem is non-deterministic polynomial-time (NP)-hard, a meta-heuristic algorithm is proposed as the solution procedure. The solution procedure is categorized as an a priori approach. To show the efficiency and effectiveness of the proposed algorithm, computational experiments were done on various test problems. Computational results show that the proposed algorithm can obtain an effective and appropriate solution quality for our investigated problem.

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“…Solving multi-objective flow shop scheduling problem has been gaining importance in recent years, in fact, many authors have developed diverse hybrid approaches and not hybrid approaches : Genetic local search [3], artificial neural network [4], particle swarm optimization [5], ant colony system [6], GRASP heuristic [7], hybrid TP+PLS [8], pareto approach [9], [10], [11], [12], multi-objective genetic algorithm and subpopulation genetic algorithm-II and non-dominated sorting genetic algorithm-II [13], multi-objective genetic algorithm [14], quantum differential evolutionary algorithm [15], Parallel multiple reference point approach [16], glowworm swarm optimization [17], genetic algorithm [18], genetic algorithm optimization technique [19], memetic algorithm [20], hybrid non-dominated sorting genetic algorithm with variable local search [21], hybrid harmony search [22], Heuristic algorithms [23], lower-bound-based GA [24]. [25] summarizes some contributions to solve flow shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Three Hybridization Categories to Solve Multi-Objective Flow Shop Scheduling Problem

Hakim¹,

Rekiek²,

Saida³

et al. 2021

IJACSA

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The industries must preserve a rate of constant productivity; however, weaknesses appear at the level of production system which engenders high manufacturing costs. Scheduling is considered the most significant issue in the production system, the solution to that problem need complex methods to solve it. The goal of this paper is to establish three hybridization categories of the evolutionary methods ABC and PSO to solve multi-objective flow shop scheduling problem: Synchronous parallel hybridization using the weighted sum method of the fitness function, sequential hybridization using or not using the weighted sum method of the fitness function, and asynchronous parallel hybridization using the weighted sum method of the fitness function. Then to test these methods in an automotive multi-objective flow shop and to perform an in-depth comparison for verifying how the multi hybridization and the hybridization categories influence the resolution of multiobjective flow shop scheduling problems. The results are consistent with other studies that have shown that the multi hybridization improve the effectiveness of the algorithm.

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Scheduling a Bi-Objective Hybrid Flow Shop with Sequence-Dependent Family Setup Times Using Metaheuristics

Cited by 18 publications

References 28 publications

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

Bi-objective scheduling for re-entrant hybrid flow shop with learning effect and setup times

Performance Comparison of Three Hybridization Categories to Solve Multi-Objective Flow Shop Scheduling Problem

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