Fuzzy Mathematical Programming and Self-Adaptive Artificial Fish Swarm Algorithm for Just-in-Time Energy-Aware Flow Shop Scheduling Problem With Outsourcing Option

Flow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear progra...

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Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 28; no. 11; pp. 2772 - 2783
Main Authors Babaee Tirkolaee, Erfan, Goli, Alireza, Weber, Gerhard-Wilhelm
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive algorithms
Algorithms
Energy consumption
Energy management
Energy-conservation
flow shop scheduling (FSS)
Frequency selective surfaces
fuzzy mathematical programming
Genetic algorithms
Integer programming
Job shop scheduling
Linear programming
Mathematical programming
Mixed integer
Multiple objective analysis
Outsourcing
outsourcing option
Parameter sensitivity
Production planning
self-adaptive artificial fish swarm algorithm (SAAFSA)
Sensitivity analysis
Subcontractors
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Summary:Flow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear programming (MILP) model is proposed for FSS with an outsourcing option and just-in-time delivery in order to simultaneously minimize the total cost of the production system and total energy consumption. Each job is considered to be either scheduled in-house or to be outsourced to one of the possible subcontractors. To efficiently solve the problem, a hybrid technique is proposed based on an interactive fuzzy solution technique and a self-adaptive artificial fish swarm algorithm (SAAFSA). The proposed model is treated as a single objective MILP using a multiobjective fuzzy mathematical programming technique based on the ϵ-constraint, and SAAFSA is then applied to provide Pareto optimal solutions. The obtained results demonstrate the usefulness of the suggested methodology and high efficiency of the algorithm in comparison with CPLEX solver in different problem instances. Finally, a sensitivity analysis is implemented on the main parameters to study the behavior of the objectives according to the real-world conditions.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.2998174