Fuzzy Integrated Cell Formation and Production Scheduling Considering Automated Guided Vehicles and Human Factors

In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems including the scheduling of parts within cel...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 29; no. 12; pp. 3686 - 3695
Main Authors: Goli, Alireza, Tirkolaee, Erfan Babaee, Aydin, Nadi Serhan
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Ant colony optimization
Automated guided vehicles
Automation
Business competition
Cellular manufacture
Cellular manufacturing
Cellular manufacturing system
Charge transfer
fuzzy linear programming
GA/heuristic algorithm
Genetic algorithms
Human factors
Integer programming
inter-cellular automated guided vehicle (AGV)
Linear programming
Manufacturing
Materials handling
Mixed integer
Mixed integer linear programming
Optimization
Particle swarm optimization
Production
Production scheduling
Remotely guided vehicles
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems including the scheduling of parts within cells in a cellular manufacturing system (CMS) where several automated guided vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs' collision as well as parts pickup/delivery. This article investigates the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and intercellular movements of parts. Due to the NP-hardness of the problem, a hybrid genetic algorithm (GA/heuristic) and a whale optimization algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and the other two well-known algorithms, i.e., particle swarm optimization and ant colony optimization, in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2021.3053838