Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility
[ X ]
Tarih
2015
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Flexibility is an important task for effectively utilizing resources in a manufacturing system and responding demands rapidly. In manufacturing systems, there exist different types of flexibility levels. In this study, the stochastic flexible job shop scheduling problem is considered to measure the impact of routing flexibility on shop performance. Thus, an integrated genetic algorithm-Monte Carlo method is proposed to analyze the effect of routing flexibility. To make the problem more realistic, system parameters (processing times, operation sequences, etc.) are generated randomly via Monte Carlo. An experimental design is utilized to analyze main and interaction effects of the factors considered (i.e., number of parts, number of machines, number of operations, and flexibility levels) by using a genetic algorithm which is specifically designed for the stochastic flexible job shop scheduling problem. In developed genetic algorithm, different initial strategies which not only improve solution quality but also decrease solution time are used. Makespan is specified as the objective function to be minimized. Results are analyzed with a full factorial analysis of variance. Comprehensive discussions of results are given case by case.
Açıklama
Anahtar Kelimeler
Stochastic flexible job shop scheduling, Routing flexibility, Genetic algorithm, Monte Carlo simulation
Kaynak
International Journal of Advanced Manufacturing Technology
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
81
Sayı
5-8
