A genetic algorithm and particle swarm optimization for no-wait flow shop problem with separable setup times and makespan criterion

Samarghandi, Hamed; ElMekkawy, Tarek Y.

doi:10.1007/s00170-011-3766-8

Cited by 24 publications

(5 citation statements)

References 47 publications

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“…Schaller (2001) proposed a new lower bound for the flow shop group scheduling problem. Samarghandi and Elmekkawy (2012) proposed two metaheuristic algorithms based on genetic and particle swarm optimization algorithms for solving no-wait flow shop problem with separable setup times and makespan criterion.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving group scheduling problem in no-wait flexible flowshop with random machine breakdown

Adressi

Hassanpour²,

Azizi³

2016

10.5267/j.dsl

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Section: Literature Reviewmentioning

confidence: 99%

Solving group scheduling problem in no-wait flexible flowshop with random machine breakdown

Adressi

Hassanpour²,

Azizi³

2016

10.5267/j.dsl

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“…As this problem is well-known for being NP-hard, they presented a new constructive heuristic, named QUARTS, in order to obtain good approximate solutions in a short CPU time. Samarghandi and ElMekkawy (2012) studied the problem of no-wait flow shop and proposed two frameworks based on genetic algorithm and particle swarm optimization to deal with the problem. Samarghandi and ElMekkawy (2014) proposed PSO algorithm to solve the problem of scheduling a no-wait flow-shop system with sequence-dependent set-up times.…”

Section: Introductionmentioning

confidence: 99%

A bi-objective simulation-optimization approach for solving a no-wait two stages flexible flow shop scheduling problem with rework ability

Rahmanidoust

Zheng²

2017

IBR

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The paper suggests a new rule; called no-wait process. The rule has two stages, and is a flexible flow shop scheduling. The process is the subject to maximize tardiness while minimizing the makespan. This hybrid flow shop problem is known to be NP-hard. Therefore, we come to first, Non-dominated Sorting Genetic Algorithm (NSGA-II), then, Multi-Objective Imperialist Competitive Algorithm (MOICA) and finally, Pareto Archive Evolutionary Strategy (PAES) as three multi-objective Pareto based metaheuristic optimization methods. They are developed to solve the problem to approximately figure out optimal Pareto front. The method is investigated in several problems that differed in size and terms of relative percentage deviation of performance metrics. The conclusion, developed by this method is the most efficient and practicable algorithm at the end.

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“…The results showed that the GAs generate better results than local searches. Su and Lee (2008) and Samarghandi and ElMekkawy (2011) introduced the concept of single server in the two-machine context, where setup operations on both machines cannot be overlapped due to the involvement of the server in these operations. Su and Lee (2008) studied the F 2, S1|no-wait, s ij | C i problem.…”

Section: The Literature On Nwfsp With Setup Timesmentioning

confidence: 99%

“…They compared their algorithms with those of Aldowaisan (2001) and showed that the new heuristic has superior solution quality. Samarghandi and ElMekkawy (2011) studied the same problem but with a different objective function. To solve the F 2, S1|no-wait, s ij |C max problem, the authors proposed a hybrid of variable neighborhood search (VNS) and tabu search (TS).…”

Section: The Literature On Nwfsp With Setup Timesmentioning

confidence: 99%

“…The most recent papers on the NWFSP with sequence-independent setup times addressed the m-machine case and applied different heuristics and metaheuristics to minimize some performance measures. Samarghandi and Elmekkawy (2012) optimized the makespan using a GA and a hybrid algorithm composed of a GA and a particle swarm optimization (PSO) algorithm. In order to demonstrate the efficiency and effectiveness of the developed algorithms, the authors applied the new methods to several instances from the literature and compared the results with those of the 2-opt algorithm, which was modified to this problem.…”

Section: The Literature On Nwfsp With Setup Timesmentioning

confidence: 99%

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The no-wait flow shop scheduling problem with sequence-dependent setup times: a comprehensive review and an application of the ALNS algorithm

Reis

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One of the most important decisions in any manufacturing company is how to schedule the operations on the available machines. In several industries, the nature of the job imposes certain constraints to operations scheduling. In a no-wait flow shop, once a job starts on the first machine, it has to continue being processed on the next ones, without any interruptions. As an extension of the flow shop scheduling, the no-wait version is also very difficult to be solved to optimality within a reasonable time, and many heuristics have been proposed for this problem. This work aims to classify existing solution algorithms proposed to solve the no-wait flow shop scheduling problem with setup times and some of its variants. We show how combining a heuristic to generate a good initial solution, local search procedures, insertion and swapping of job positions and techniques developed originally to solve transportation problems are among the popular and efficient techniques for the problem at hand. We also propose a new solution method based on the well-known Adaptive Large Neighborhood Search (ALNS) algorithm from transportation science. The use of this algorithm aims to minimize the total flow time as a performance measure. As this is a problem with high complexity, to achieve high solution quality in a reasonable time, an acceleration method was also adapted and applied into local search procedures with swapping operations. The results of the new method were compared to the best results in the literature for widespread instances, validating the quality of the method.

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A genetic algorithm and particle swarm optimization for no-wait flow shop problem with separable setup times and makespan criterion

Cited by 24 publications

References 47 publications

Solving group scheduling problem in no-wait flexible flowshop with random machine breakdown

Solving group scheduling problem in no-wait flexible flowshop with random machine breakdown

A bi-objective simulation-optimization approach for solving a no-wait two stages flexible flow shop scheduling problem with rework ability

The no-wait flow shop scheduling problem with sequence-dependent setup times: a comprehensive review and an application of the ALNS algorithm

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