Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment.
As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service lev...
| Publicado en: | Scientific World Journal pp. 916057 - 916058 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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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=ccm&AN=104122004&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104122004 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104122004 NLM24302880 2012403424 10.1155/2013/916057 NLM24302880 PMC3835819 104122004 ppf: 916057 ppct: 1 formats: tig: atl: Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment. aug: au: Wang, Lin Qu, Hui Liu, Shan Dun, Cai-Xia affil: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China. sug: subj: Equipment and Supplies Statistics Algorithms ab: As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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