Genetic algorithms applied to the continuous flow shop problem

Chen, Chuen-Lung; Neppalli, Ranga V.; Aljaber, Nasser

doi:10.1016/0360-8352(96)00042-3

Cited by 117 publications

(43 citation statements)

References 14 publications

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“…To obtain a near-optimal solution, meta-heuristic algorithms have also been proposed. 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].…”

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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Section: Literature Reviewmentioning

confidence: 99%

An electromagnetism-inspired method for a generalized flowshop problem

Khalili

2014

Manufacturing Rev.

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“…Here, Rouletee wheel selection based on fitness value, partially mapped crossover (PMX), 34 and mutation operator INSERT 28 is adopted.…”

Section: Cqga For the Nwfspmentioning

confidence: 99%

An effective co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem

Deng

Wei

et al. 2015

Advances in Mechanical Engineering

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This article proposes a competitive co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem with the criterion to minimize makespan, which is a renowned NP-hard combinatorial optimization problem. An innovative coding and decoding mechanism is proposed. The mechanism uses square matrix to represent the quantum individual and adapts the quantum rotation gate to update the quantum individual. In the algorithm framework, the store-with-diversity is proposed to maintain the diversity of the population. Moreover, a competitive co-evolution strategy is introduced to enhance the evolutionary pressure and accelerate the convergence. The store-with-diversity and competitive co-evolution are designed to keep a balance between exploration and exploitation. Simulations based on a benchmark set and comparisons with several existing algorithms demonstrate the effectiveness and robustness of the proposed algorithm.

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“…The significance of minimizing the total cost of inventory has been discussed by many researchers, e.g., Warburton [19]. Some of the works on the problem with the performance measure of total completion time include Chen et al [9], Aldowaisan and Allahverdi [4], Chang et al [8], and Pan et al [15].…”

Section: Introductionmentioning

confidence: 99%

Minimizing total tardiness in no-wait flowshops

Aldowaisan

Allahverdi

2012

Foundations of Computing and Decision Sciences

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Abstract. We address the m-machine no-wait flowshop scheduling problem; where the objective is to minimize total tardiness. To the best of our knowledge, the considered problem has not been addressed so far. We propose heuristic solutions since the problem is NP-hard. Initially, we consider a number of dispatching rules commonly used for the considered objective in other scheduling environments. We identify through computational experiments the best performing dispatching rule; and then propose simulated annealing (SA) and genetic algorithms (GA) by using the best performing dispatching rule as an initial solution. This achieves at least 50% improvement in the SA and GA performances. Next, we propose enhanced versions of SA and GA and show through computational experiments that the enhanced versions provide over 90% further improvement. The performance of enhanced GA is slightly better than that of enhanced SA; however, the computation time of enhanced GA is about 10 times that of enhanced SA. Therefore, we conclude that the enhanced SA outperforms the enhanced GA.

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Genetic algorithms applied to the continuous flow shop problem

Cited by 117 publications

References 14 publications

An electromagnetism-inspired method for a generalized flowshop problem

An electromagnetism-inspired method for a generalized flowshop problem

An effective co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem

Minimizing total tardiness in no-wait flowshops

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