A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages

Jabbari, Mona; Tavana, Madjid; Daneshamooz, Fatemeh

doi:10.1016/j.susoc.2021.09.002

Cited by 5 publications

(1 citation statement)

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“…Jabbari et al [25] proposed the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Production Assembly Scheduling with Hybrid Flowshop in Assembly Stage

2022

IJE

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A new three stage production-assembly problem is considered in this paper. To the best of our knowledge, considering parallel machines in the third stage, identical parallel factories including the three stage production-assembly system and identical parallel factories with parallel machines in the third stage of the production-assembly system, has been specifically investigated in this paper. To minimize the maximum completion time (Makespan) of all jobs in the all factories, jobs assignment to factories and their processing sequence should be done properly. A Mixed Integer Linear Programming (MILP) model is presented to solve small size problem by using cplex solver. According to the problem computational complexity, large size of problem is not possible to solve using the cplex, so to solve it and to control the computational complexity, a new improved genetic algorithm (GA) is proposed by combining GA and Longest Proseccing Time (LPT) method that is called Hybrid Genetic Algorithm Longest Proseccing Time (HGALPT). The problem parameters values are determined using one-way analysis of variance (ANOVA). Finally, in order to evaluate the efficiency and effectiveness of the proposed algorithm, and to specify each parameter impact on the objective function, sensitivity analysis is performed on the problem parameters.

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Section: Literature Reviewmentioning

confidence: 99%