Méthode heuristique pour le problème de flow shop hybride avec machines dédiées

Dridi, Najoua; Hadda, Hatem; Hajri-Gabouj, Sonia

doi:10.1051/ro/2009024

Cited by 11 publications

(11 citation statements)

References 9 publications

(15 reference statements)

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“…The first three lower bounds are derived from flow shop problems. The last one is a generalization of the lower bound previously proposed by Dridi et al (2009) for the two-stage HFS with dedicated machines in the second stage.…”

Section: Fig2the Illustration Of the Proof For Property 2 4 Lower mentioning

confidence: 93%

“…Riane et al (2002) studied the HFS with two dedicated machines and developed a dynamic program. Other studies have proposed heuristic approaches (Oguz et al, 1997;Dridi et al, 2009;Wang & Liu, 2013). Oguz et al (1997) proposed an efficient heuristic, based on the Johnson's algorithm, to minimize the makespan in a twostage flow shop with two dedicated machines in the first stage.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have also studied the case where the processing sequences of the two types of jobs are given and fixed: a polynomial-time dynamic programming algorithm (Huang & Lin, 2013) and a linear-time algorithm (Lin, 2015) was developed to minimize the makespan and the maximum lateness, respectively. Dealing with the makespan minimization for the case of m dedicated machines in the second stage, several heuristics were proposed (Dridi et al, 2009;Wang & Liu, 2013). For the same problem, Hadda et al (2012) presented a new dominance rule.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing makespan in a three-stage hybrid flow shop with dedicated machines

Bedhief¹,

Dridi²

2019

10.5267/j.ijiec

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In recent years, many studies on scheduling problems with dedicated machines have been carried out. But, few of them have considered the case of more than two stages. This paper aims at filling this gap by addressing the three-stage hybrid flow shop scheduling problem with two dedicated machines in stage 3. Each job must be processed, consecutively, on the single machines of stages 1 and 2, and depending on its type, it will be further processed on one of the two dedicated machines of stage 3. The objective is to find an optimal schedule that minimizes the maximum completion time (makespan). Since this problem is strongly NP-hard, we first provide some basic results including solutions for several variations of the problem. Then, for the general case we adapt a set of lower bounds from the literature and propose a heuristic approach that is based on the dynamic programming technique, which uses a local search procedure. Finally, various experimentations on several problems with different sizes are conducted and the computational results of the heuristic show that the mean percentage deviation value from the lower bound was lower than 0.8 percent for some instances with 40 to 200 jobs in size.

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Section: Fig2the Illustration Of the Proof For Property 2 4 Lower mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Minimizing makespan in a three-stage hybrid flow shop with dedicated machines

Bedhief¹,

Dridi²

2019

10.5267/j.ijiec

Self Cite

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“…Other studies have proposed heuristic approaches [13][14][15]. For the case of two dedicated machines in the first stage, Oguz et al [15] considered the objective of minimizing the makespan and developed a heuristic approach based on the Johnson's algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

“…For more than two dedicated machines in the second stage, many heuristic approaches are developed in the papers [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for Three-Stage Hybrid Flow Shop Scheduling Problem with Dedicated Machines

Bedhief¹,

Dridi²

2020

JESA

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In recent years, many studies have been carried out to solve scheduling problems with dedicated machines. But, few of them have considered the case of more than two stages. This paper aims at filling this gap by addressing the three-stage hybrid flow shop scheduling problem with two dedicated machines in stage 3. The objective is to minimize the maximum completion time (makespan). Since the genetic algorithm is very effective at finding optimal solutions to a wide variety of problems, this paper proposes an improved genetic algorithm (IGA). It uses more than one crossover operator and is based on a local search method (2-opt). The impact of different control parameters and operators on the outcomes of the basic genetic algorithm has been firstly tested by means of computational experiments, which can help us to undertake some observed shortages when developing our improved genetic algorithm. The results with different problem configurations demonstrate the effectiveness of IGA for solving the three-stage scheduling problem with dedicated machines, with a mean percentage deviation from the lower bound that did not exceed 0.5% and a very reasonable computational time. Besides, the simulation results show that our improved GA model outperforms an existing heuristic approach that has been previously proposed in the academic literature to deal with the same problem.

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