A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem.
This paper presents a binary particle swarm optimization algorithm for the lot sizing problem. The problem is to find order quantities which will minimize the total ordering and holding costs of ordering decisions. Test problems are constructed randomly, and solved optimally by Wagner and Whitin Alg...
| Publicado en: | Journal of Economic & Social Research Vol. 5; no. 2; pp. 1 - 21 |
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| Autores principales: | , |
| Formato: | Artículo |
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Department of Economics at Fatih University
2003
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=21972498&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 21972498 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 13021060 KXM jtl: Journal of Economic & Social Research issn: 13021060 maglogo: N pubinfo: dt: 2003 vid: 5 iid: 2 pid: 14222 pub: Department of Economics at Fatih University artinfo: ui: 21972498 ppf: 1 ppct: 20 formats: tig: atl: A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem. aug: au: Taşgetiren, M. Fatih Yun-Chia Liang affil: Management Department, Fatih University, 34500 Buyukcekmece, Istanbul, Turkey. Department of Industrial Engineering and Management, Yuan Ze University No 135 Yuan-Tung Road, Chung-Li, Taoyuan County, Taiwan 320, R.O.C. su: Economic lot size Genetic algorithms Cost control Binary number system Inventory control Combinatorial optimization sug: subj: All Other Support Services Process, Physical Distribution, and Logistics Consulting Services Economic lot size Genetic algorithms Cost control Binary number system Inventory control Combinatorial optimization keyword: Evolutionary Algorithms. Genetic algorithm Lot sizing Particle swarm optimization Evolutionary Algorithms. Genetic algorithm Lot sizing Particle swarm optimization ab: This paper presents a binary particle swarm optimization algorithm for the lot sizing problem. The problem is to find order quantities which will minimize the total ordering and holding costs of ordering decisions. Test problems are constructed randomly, and solved optimally by Wagner and Whitin Algorithm. Then a binary particle swarm optimization algorithm and a traditional genetic algorithm are coded and used to solve the test problems in order to compare them with those of optimal solutions by the Wagner and Whitin algorithm. Experimental results show that the binary particle swarm optimization algorithm is capable of finding optimal results in almost all cases. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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