| Item type |
デフォルトアイテムタイプ_（フル）(1) |
| 公開日 |
2023-03-18 |
| タイトル |
|
|
タイトル |
A multi-objective lead time control problem in multistage assembly systems using genetic algorithms |
|
言語 |
en |
| 作成者 |
Perkgoz, Cahit
Azaron, Amir
Katagiri, Hideki
Kato, Kosuke
Sakawa, Masatoshi
|
| アクセス権 |
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|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
権利情報 |
Copyright (c) 2006 Elsevier B.V. |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Queueing |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Genetic algorithms |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Multiple objective programming |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Production |
| 主題 |
|
|
主題Scheme |
NDC |
|
主題 |
460 |
| 内容記述 |
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|
内容記述 |
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. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Elsevier Science B.V. |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
AO |
|
出版タイプResource |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1016/j.ejor.2006.04.024 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
http://dx.doi.org/10.1016/j.ejor.2006.04.024 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
0377-2217 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA0017802X |
| 開始ページ |
|
|
開始ページ |
292 |
| 書誌情報 |
European Journal of Operational Research
European Journal of Operational Research
巻 180,
号 1,
p. 292-308,
発行日 2007-07-01
|
| 旧ID |
20748 |
EJOR_180-1_292.pdf (217.1 KB)
