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ID 20748
file
creator
Perkgoz, Cahit
Azaron, Amir
Kato, Kosuke
subject
Queueing
Genetic algorithms
Multiple objective programming
Production
NDC
Biology
abstract
In this paper, we develop a multi-objective model to optimally control the lead time of a multistage assembly system, using genetic algorithms. The multistage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The optimal service control is decided at the beginning of the time horizon. The transport times between the service stations are independent random variables with generalized Erlang distributions. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total operating costs of the system per period (to be minimized), the average lead time (min), the variance of the lead time (min) and the probability that the manufacturing lead time does not exceed a certain threshold (max). Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed genetic algorithm approach.
journal title
European Journal of Operational Research
volume
Volume 180
issue
Issue 1
start page
292
end page
308
date of issued
2007-07-01
publisher
Elsevier Science B.V.
issn
0377-2217
ncid
publisher doi
language
eng
nii type
Journal Article
HU type
Journal Articles
DCMI type
text
format
application/pdf
text version
author
rights
Copyright (c) 2006 Elsevier B.V.
relation url
department
Graduate School of Engineering