New multi-objective method to solve reentrant hybrid flow shop scheduling problem

Dugardin, Frédéric; Yalaoui, Farouk; Amodeo, Lionel

doi:10.1016/j.ejor.2009.06.031

Cited by 134 publications

(76 citation statements)

References 32 publications

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“…Its solution performance was compared with the existing multi-objective heuristics and get more efficient and better than other methods. Dugardin 88 focuses on the multi-objective resolution of a reentrant hybrid flow-shop scheduling problem (RHFS) and objectives are: the maximization of the utilization rate of the bottleneck and the minimization of the maximum completion time and solve this problem with a new multi-objective genetic algorithm called L-NSGA which uses the Lorenz dominance relationship. The results of L-NSGA are compared with NSGA2, SPEA2 and an exact method.…”

Section: Computational Evaluationmentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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Section: Computational Evaluationmentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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“…Dugardin et al [10] considered the multi-objective RHFS scheduling problem. The scheduling objective consists of two parts: minimization of the cycle time and the maximization of the utilization rate of the bottleneck.…”

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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“…An interested reader is referred to [43][44][45][46][47] where he may find details about the other two trends.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Stochastic Linear Programming: An Overview

Adeyefa¹,

Luhandjula²

2011

AJOR

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Many Optimization problems in engineering and economics involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ideas from optimization, probability theory and multicriteria decision analysis are interwoven to address situations where the presence of several objective functions and the stochastic nature of data are under one roof in a linear optimization context. In this way users of these models are not bound to caricature their problems by arbitrarily squeezing different objective functions into one and by blindly accepting fixed values in lieu of imprecise ones.

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New multi-objective method to solve reentrant hybrid flow shop scheduling problem

Cited by 134 publications

References 32 publications

Review on Job-Shop and Flow-Shop Scheduling Using

Review on Job-Shop and Flow-Shop Scheduling Using

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

Multiobjective Stochastic Linear Programming: An Overview

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