Exact resolution of the two-stage hybrid flow shop with dedicated machines

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

doi:10.1007/s11590-014-0741-y

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…Wang et al (2015) propose a B&B algorithm for a two-stage hybrid flow shop with no-wait constraints, capable of handling up to 20 jobs. Other B&B approaches can also be found in the literature (see, for instance, Néron et al, 2001;Lee and Kim, 2004;Allaoui and Artiba, 2006;Hadda et al, 2014). The instance size that B&B methods can solve is typically quite small, but the methods can also serve as the basis for other algorithms, such as beam search and A-star search.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hybrid flow shops occur widely in current-day manufacturing, and are often enriched with complex and practical constraints such as limited buffers (Baumann and Trautmann, 2013;Liu et al, 2017), no buffers (Grabowski and Pempera, 2000;Gicquel et al, 2012), limited-waiting restrictions (Gicquel et al, 2012), no-wait constraints (Grabowski and Pempera, 2000;Berghman et al, 2014;Berghman and Leus, 2015), machine eligibility constraints (Liu et al, 2017;Shahvari and Logendran, 2018), re-entrant flows (Chamnanlor et al, 2014;Schulze et al, 2016), batch processing (Liu et al, 2017;Tan et al, 2018), release dates (Shahvari and Logendran, 2018), sequence-dependent setup times (Bang and Kim, 2011), dedicated machines (Hadda et al, 2014), and so on. These technical constraints often arise with multiple stages (Almada-Lobo et al, 2008;Gicquel et al, 2012;Baumann and Trautmann, 2013;Chamnanlor et al, 2014) and large-scale instances (Almada-Lobo et al, 2008;Baumann and Trautmann, 2013;Schulze et al, 2016;Liu et al, 2017), resulting in a complicated and difficult problem.…”

Section: Literature Reviewmentioning

confidence: 99%

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Scheduling hybrid flow shops with time windows

Yang

Leus

2019

J Heuristics

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Hybrid flow shops can be encountered in various industrial settings. In this paper we develop methods for scheduling hybrid flow shops with hard time windows. Specifically, we study a two-stage hybrid flow shop scheduling problem with time windows to minimize the total weighted completion times. Each stage consists of one or more identical parallel machines, and each job visits two processing stages in series. Finding a feasible schedule with hard time windows is a challenging task in this setting, because it is NP-complete in the strong sense even for a single machine in a single stage. We propose two matheuristics to find an initial feasible solution by local branching. We also develop two schedule improvement procedures, one based on stage-by-stage decomposition, and one using adapted local branching. The performance of our methods is validated via extensive computational experiments.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Scheduling hybrid flow shops with time windows

Yang

Leus

2019

J Heuristics

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“…Some studies have proposed exact methods [7][8][9]. Riane et al [7] developed a dynamic program in order to solve a twostage hybrid flow shop problem with two dedicated machines in the second stage.…”

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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“…For the HFS problem with dedicated machines, most literature is focused on the two-stage configurations. Some studies have proposed exact methods (Riane et al, 2002;Mosheiov & Sarig, 2010;Hadda et al, 2014). Mosheiov and Sarig (2010) proposed an integer programming model and a polynomial dynamic programming algorithm for the two-stage HFS with m dedicated machines and a common job's due date.…”

Section: Introductionmentioning

confidence: 99%

“…Mosheiov and Sarig (2010) proposed an integer programming model and a polynomial dynamic programming algorithm for the two-stage HFS with m dedicated machines and a common job's due date. A branch and bound method has also been proposed to minimize the makespan for the case of m dedicated machines in the second stage (Hadda et al, 2014). Riane et al (2002) studied the HFS with two dedicated machines and developed a dynamic program.…”

Section: Introductionmentioning

confidence: 99%

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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Exact resolution of the two-stage hybrid flow shop with dedicated machines

Cited by 11 publications

References 10 publications

Scheduling hybrid flow shops with time windows

Scheduling hybrid flow shops with time windows

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

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

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