Hybride Particle Swarm Optimization to Solve Fuzzy Multi-Objective Master Production Scheduling Problems with Application

Authors

  • Raghad M. Jasim Department of Veterinary Public Health , College of Veterinary Medicine, University of Mosul, Mosul, Iraq

Keywords:

Fuzzy Logic, Particle Swarm Optimization, Master production scheduling, Multi-Objective Optimization

Abstract

Multi-objective Master production scheduling problem is  NP-hard problem, therefore because there is no algorithm that can identify the proper solution to this problem, the processing time required to solve it grows exponentially as the size of the problem increases, So, finding optimal solution is considered very difficult, and that is why meta-heuristics algorithm such as genetic algorithm (GA), simulated annealing (SA) and memetic algorithm (MA)are used to obtain the optimal solution. This article presented a hybrid particle swarm optimization (HPSO) technique for the purpose of solving fuzzy multi-objective master production schedules (FMOMPS). The fundamental concept is to integrate PSO and GA mutation operations. The purpose of this work is to apply the FMOMPS to an industrial case study involving a textile facility in Mosul, Iraq. The application includes decide the gross requirements by forecasting of demand using artificial neural networks, in addition to locate available production rate of every production line by using geometric process model for all stops and failures, and calculate availability values. The proposed method confirmed its usefulness in locating optimal solutions for MPS issues when compared to genetic algorithms for fuzzy and non-fuzzy models, as the results clearly demonstrated HPSO's superiority over GA, SA, and MA across all objectives.

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MPS Particle Structure

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Published

14.01.2023

How to Cite

Jasim, R. M. . (2023). Hybride Particle Swarm Optimization to Solve Fuzzy Multi-Objective Master Production Scheduling Problems with Application. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 201–208. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2493

Issue

Vol. 11 No. 1s (2023): Advances and Innovations in Deep Learning Applications

Section

Research Article

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Copyright (c) 2023 Raghad M. Jasim

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