Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

Hassan Jafarzadeh, Nazanin Moradinasab, Ali Gerami

Abstract


Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem.

Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm.

Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm.

Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.


Keywords


No-wait two-stage flexible flow shop; Setup time of the machinery; Probable rework; Adjusted discrete multi-objective invasive weed optimization

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DOI: http://dx.doi.org/10.3926/jiem.2348


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2017

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience