Optimizing multi-objective dynamic facility location decisions within green distribution network design
Author | Afshari, Hamid |
Author | Sharafi, Masoud |
Author | ElMekkawy, Tarek |
Author | Peng, Qingjin |
Available date | 2016-06-21T13:29:05Z |
Publication Date | 2014 |
Publication Name | Procedia CIRP: Variety Management in Manufacturing Proceedings of the 47th CIRP Conference on Manufacturing Systems |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1016/j.procir.2014.01.147 |
Citation | Afshari, H., Sharafi, M., ElMekkawy, T., Peng, Q. "Optimizing multi-objective dynamic facility location decisions within green distribution network design" (2014) Procedia CIRP, 17, pp. 675-679. |
ISSN | 2212-8271 |
Abstract | Despite the surge of sustainable economic development which inspires corporations to optimize investment on environmental-friendly infrastructural facilities, limited pragmatic evidences are observed in product-service networks. The importance of such optimized decisions would be highlighted where various stakeholders' objectives are involved in network design. This paper addresses the joint study of optimization for profitability, customer satisfaction, and sustainability by optimizing facility location decisions in distribution-service network with forward and reverse streams. The contribution is minimizing establishment, transportation and inventory management costs and simultaneously maximizing customer satisfaction with sustainable perspective. The presented model is the optimum approach for multiobjective, multi-period, multi-commodity, distribution-service system. The applicability of the proposed model is validated by a real case study. Provided solution proved that a green distribution-service network will increase the efficiency in terms of profitability and customer satisfaction. |
Language | en |
Publisher | Elsevier |
Subject | Dynamic optimization Facility location Green distribution network Mathematical modeling |
Type | Conference Paper |
Pagination | 675-679 |
Volume Number | 17 |
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Mechanical & Industrial Engineering [1342 items ]