A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem

Pan, Quan-Ke; Taşgetiren, M. Fatih; Liang, Yun-Chia

doi:10.1016/j.cor.2006.12.030

Cited by 381 publications

(144 citation statements)

References 32 publications

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“…Recently, a few researches applied PSO for discrete combinatorial optimization problems [26][27][28][33][34][35][36][37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The discrete PSO (DPSO) algorithm used here is first proposed by Pan et al [37] for the no-wait flowshop scheduling problem. We employed the DPSO algorithm for UFL problem.…”

Section: Discrete Pso Algorithm For Ufl Problemmentioning

confidence: 99%

“…, y in ], where y ik represents opening or closing kth facility of the ith particle. For an nfacility problem, each particle contains n number of dimen- The open facility vector (Y i ) of the particle i at iteration t can be updated as follows [37]:…”

Section: Discrete Pso Algorithm For Ufl Problemmentioning

confidence: 99%

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A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Güner

Sevkli

2008

Journal of Artificial Evolution and Applications

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A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

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“…Recently, a few researches applied PSO for discrete combinatorial optimization problems [26][27][28][33][34][35][36][37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The discrete PSO (DPSO) algorithm used here is first proposed by Pan et al [37] for the no-wait flowshop scheduling problem. We employed the DPSO algorithm for UFL problem.…”

Section: Discrete Pso Algorithm For Ufl Problemmentioning

confidence: 99%

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A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Güner

Sevkli

2008

Journal of Artificial Evolution and Applications

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“…Different genetic algorithms (GA) are applied by Chen and Neppalli [20], Aldowaisan and Allahverdi [21]. Among the other metaheuristics, one could refer the reader to particle swarm optimization (PSO) by Pan et al [22], simulated annealing (SA) by Fink and Voß [23], ant colony optimization (ACO) by Shyu et al [24] and tabu search (TS) by Grabowski and Pempera [25]. Khalili [26] proposed an iterated local search algorithm for flexible flow lines with sequence dependent setup times to minimize total weighted completion and also studied multiobjective no-wait hybrid flowshop scheduling problems to minimise both makespan and total tardiness [27].…”

Section: Literature Reviewmentioning

confidence: 99%

An electromagnetism-inspired method for a generalized flowshop problem

Khalili

2014

Manufacturing Rev.

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-There are two common simplifying assumptions made in the scheduling of operations. One is that machines are available continuously, and the other is that once a job completes one process it is immediately available for its next. Moreover, in flowshops, it is commonly assumed that jobs must visit all machines. In practice, however, these assumptions may not be justifiable in some applications. This paper deals with a generalized flow shop (GFS) problem with machine availability constraints, transportation times between stages, and machine skipping. The electromagnetism-like method (EM) has been successfully applied to some NP-hard problems and this has motivated us to apply and assess the effectiveness of an EM algorithm in the GFS scenarios. Simulated annealing (SA) and a number of other well-recognized heuristics to the given GFS scheduling problem that minimizes two independent objective functions, namely the total tardiness and the total completion time also has been applied. In order to evaluate the performance of the proposed EM and SA, a set of practical instances has been considered. The related results are analyzed in two parts; in term of the objective functions, and the observed effects on variables in our instances. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficient than SA and other heuristics with regards to the objective functions considered in this paper.

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“…Comparison of DSOMA is done with the Discrete Particle Swarm Algorithm (DPSOVND) of [5]. From current literature it has been shown as the most promising algorithm.…”

Section: Comparison With Discrete Particle Swarm Algorithmmentioning

confidence: 99%

Discrete Self-Organising Migrating Algorithm for Flow Shop Scheduling With No Wait Makespan

Davendra¹,

Zelinka²,

Šenkeřík³

et al. 2011

AIP Conference Proceedings

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A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem

Cited by 381 publications

References 32 publications

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

An electromagnetism-inspired method for a generalized flowshop problem

Discrete Self-Organising Migrating Algorithm for Flow Shop Scheduling With No Wait Makespan

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