A fuzzy multi-objective approach for a meat supply chain design
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
The global demand of food may be doubled by 2050 making food supply chains as one of the largest sectors in economy. Thus, a robust design of a food supply chain network is essential for a success in a competitive market and this has been increasingly becoming one of major issues for decision makers in supply chain sectors. This article presents a multi-objective model for solving an issue of a three-echelon meat supply chain (MSC) design and its distribution problem. The objectives of the developed model are aimed at minimizing the total transportation cost and CO2 emissions, and maximizing the average delivery rate in satisfying product quantity as requested by abattoirs and retailers. Furthermore, the model is formulated in terms of a fuzzy multi-objective linear programming model (FMOLPM) to handle the uncertainties associated with costs and demands in product quantity within the considered MSC. To optimize the three objectives under varying conditions, two solution methods were investigated and used. These include the method of LP-metrics and the method of ϵ-constraint in order to compare the obtained Pareto solutions. The best solution was determined using the Max-Min method. Computational results demonstrate the effectiveness of the developed model that helps tackle a number of issues for a meat supply chain design.
|Title of host publication||2016 22nd International Conference on Automation and Computing (ICAC 2016)|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|Publication status||Published - Nov 2016|
|Event||22nd International Conference on Automation and Computing, ICAC 2016 - Colchester, United Kingdom|
Duration: 7 Sep 2016 → 8 Sep 2016
|Conference||22nd International Conference on Automation and Computing, ICAC 2016|
|Period||7/09/16 → 8/09/16|
- A fuzzy multi-objective approach for a meat supply chain design
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Accepted author manuscript (Post-print), 282 KB, PDF document