No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms

Moradinasab, Nazanin; Shafaei, Rasoul; Rabiee, Meysam; Ramezani, Pezhman

doi:10.1080/0952813x.2012.682752

Cited by 30 publications

(7 citation statements)

References 26 publications

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“…That is, once a job is commenced on the first machine, it has to be continuously processed through the subsequent machine without interruption. Therefore, it may take place a condition that the starting time of a job on the first machine must be delayed in order to meet the no-wait requirement (Moradinasab et al 2012). There are three principal causes for no-waiting the operations.…”

Section: Introductionmentioning

confidence: 99%

No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach

2013

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According to the state of the art of no-wait scheduling problem, practitioners have mostly concentrated on pure no-wait flow shop scheduling problem. In the most real world production cases, flow shops operate with uniform parallel machines at each stage to eliminate or reduce the bottleneck stages with aim of enhancing the efficiency of production. This paper deals with a no-wait scheduling problem considering anticipatory sequence-dependent setup times on the flexible flow shop environment with uniform parallel machines. The objective is to find the sequence which minimizes maximum completion time of jobs (i.e. makespan). Since this problem is known to be NP-hard, we introduce a novel approach to tackle the problem. In the solution approach, firstly a heuristic formulation is used for objective function evaluation. Afterwards, principles of meta-heuristic algorithms namely invasive weed optimization, variable neighborhood search and simulated annealing algorithms are hybridized as solution method of the problem. In addition, a Taguchi method is employed for calibration of parameters and operators of the proposed hybrid metaheuristic. Various computational experiments in two scales of small and large are established to illustrate the effectiveness

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Section: Introductionmentioning

confidence: 99%

No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach

2013

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“…Rahmanidoust and Zheng 6) presented three multi-objective metaheuristic methods to minimize makespan and maximum tardiness for the case with multiple identical machines at both stages. For the same machine environment in Rahmanidoust and Zheng 6) with setup times, Moradinasab et al 27) applied an adaptive imperialism competition algorithm and GA to minimize the total completion time; Huang et al 28) studied the total earliness and tardiness problem with time window constraints and presented an improved ant colony optimization algorithm. Considering unrelated parallel machines at each stage, Rabiee et al 29) designed an intelligent hybrid algorithm based on imperialist competitive algorithm so as to minimize makespan where it is combined with SA, variable neighborhood search and GA. For multi-stage NWHFP with sequence-dependent setup times and identical machines, Asefi et al 30) developed a metaheuristic approach so that the bi-objective of makespan and mean tardiness are minimized and Jolai et al 2) proposed three metaheuristic algorithms to minimize makespan.…”

Section: Introductionmentioning

confidence: 99%

A Novel Genetic Simulated Annealing Algorithm for No-wait Hybrid Flowshop Problem with Unrelated Parallel Machines

Xuan

Zheng

et al. 2021

ISIJ Int.

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This paper studies the problem of scheduling N jobs in a hybrid flowshop with unrelated parallel machines at each stage. Considering the practical application of the presented problem, no-wait constraints and the objective function of total flowtime are included in the scheduling problem. A mathematical model is constructed and a novel genetic simulated annealing algorithm so-called GSAA are developed to solve this problem. In the algorithm, firstly a modified NEH algorithm is proposed to obtain the initial population. A two-dimensional matrix encoding scheme for scheduling solutions is designed and an insertion-translation approach are employed for decoding in order to meet no-wait constraints. Afterwards, to avoid GA premature and enhance search ability, an adaptive adjustment strategy is imposed on the crossover and mutation operators. In addition, a SA procedure is implemented for some better individuals from the GA solutions to complete re-optimization, where five neighborhood search structures are constructed including job based exchange, gene based exchange, gene based insertion, job based mutation, and gene based mutation. Finally, various simulation experiments in two scales of small-medium and large are established. Computational results show that the presented algorithm performs much more effectively compared with several heuristic algorithms reported in the literature. KEY WORDS: hybrid flowshop; unrelated parallel machines; no-wait constraints; total flowtime; genetic simulated annealing algorithm.

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“…Shokrollahpour et al [17] and Seidgar et al [18] both exploited the ICA in assembly flow shop problem while, respectively, using the Taguchi method and neural network as their own tools in regulating the parameters. Additionally, in the no-wait twostage hybrid flow shop, Moradinasab et al [19] introduced a new procedure called global war in ICA to avoid the local optima. This step helps to transfuse some new empires in a certain extent and achieves desirable performance in the experiment.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

Zhang

et al. 2017

Mathematical Problems in Engineering

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Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms

Cited by 30 publications

References 26 publications

No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach

No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach

A Novel Genetic Simulated Annealing Algorithm for No-wait Hybrid Flowshop Problem with Unrelated Parallel Machines

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

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