An extensive and systematic literature review for hybrid flowshop scheduling problems

Hybrid flowshop scheduling problem (HFSP) is a mixture of two classical scheduling problems as parallel machine scheduling (PMS) and flowshop scheduling (FS). In the HFSP, a series of jobs are processed respectively in a set of stages that at least one of these stages has more than one parallel machine (identical, uniform or unrelated). HFSP is a widespreadly studied subject in the literature and there are various application areas such as transportation, healthcare management, agricultural activities, cloud computing, and the most common manufacturing. Therefore, it will be useful to present a review study including recent papers and developments related to this problem for researchers. For this aim, in this paper, a systematic literature survey has been conducted with respect to HFSPs by means of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology which enables to realize systematic review and meta-analyses in a specified subject. 172 articles which are published in the 2010-2020 year interval, related to production scheduling and including a mathematical programming model to express scheduling problems have been determined as a result of this methodological review process. These articles have been statistically analyzed according to many features such as year, country, journal, number of stages, type of parallel machines, constraints, objective functions, solution methods, test instances and type of parameters. The results of statistical analyses have been presented through charts so as to provide a visual demonstration to researchers. Furthermore, it has been aimed to answer 14 predetermined research questions by means of analyses realized in the scope of this review study. Consequently, it has revealed the existing literature, recent developments and future research suggestions related to HFSP and therefore it is possible to say that this review paper provides a beneficial road map for researchers studying in this field.

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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

Journal of Central South University ◽

10.1007/s11771-017-3586-6 ◽

2017 ◽

Vol 24 (8) ◽

pp. 1779-1788 ◽

Cited By ~ 2

Author(s):

Ze-wen Sun ◽

Xing-sheng Gu

Keyword(s):

Parallel Machine ◽

Estimation Of Distribution Algorithm ◽

Scheduling Problem ◽

Flowshop Scheduling ◽

Unrelated Parallel Machine ◽

Hybrid Flowshop ◽

Hybrid Estimation ◽

Hybrid Flowshop Scheduling ◽

Estimation Of Distribution ◽

Flowshop Scheduling Problem

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A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown

Mathematical Problems in Engineering ◽

10.1155/2014/621393 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Zhe Cui ◽

Xingsheng Gu

Keyword(s):

Discrete Group ◽

Scheduling Problem ◽

Scheduling Problems ◽

Flowshop Scheduling ◽

Hybrid Flowshop ◽

Random Breakdown ◽

Hybrid Flowshop Scheduling ◽

Discrete Operators ◽

Flowshop Scheduling Problem ◽

Group Search

The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.

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