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Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 1: Modelling and representation

Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 2: Genetic operators and results

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Knowledge and Smart Technology (KST)

Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 2: Genetic operators and results

Content Provider	IEEE Xplore Digital Library
Author	Mahmudy, Wayan F. Marian, Romeo M. Luong, Lee H. S.
Copyright Year	2013
Description	Author affiliation: School of Advanced Manufacturing & Mechanical Eng., University of South Australia, Australia (Mahmudy, Wayan F.; Marian, Romeo M.; Luong, Lee H. S.)
Abstract	This paper as the continuation of the first part addresses two NP-hard and strongly related problems in flexible manufacturing system (FMS), part type selection problem and loading problem. This first part of the paper detailed a modeling of the problems and discussed how the chromosome representation of the real coded genetic algorithms (RCGA) can handle various flexibilities of operations in the FMS. Hybridizing the RCGA with variable neighborhood search (VNS) and a strategy to maintain population diversity were implemented. This second part of the paper discusses the effectiveness of this hybrid approach to solve several test bed problems. This approach improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could produces promising results and the hybridization can improve the performance of the RCGA.
Starting Page	81
Ending Page	85
File Size	670415
Page Count	5
File Format	PDF
ISBN	9781467348508
e-ISBN	9781467348539
DOI	10.1109/KST.2013.6512792
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-31
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Part type selection problem Statistics Biological cells Genetic algorithms Hybrid genetic algorithms Loading Sociology Flexible manufacturing system Production planning Loading problem Alternative production plans Production Flexible manufacturing systems
Content Type	Text
Resource Type	Article

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