نشریه بین المللی مهندسی صنایع و تحقیقات تولید
دانشگاه علم و صنعت ایران
پنجشنبه 22 آذر 1403
[Archive]
جلد 23، شماره 2 - ( 3-1391 )
جلد 23 شماره 2 صفحات 123-113 |
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A Complex Design of the Integrated Forward-Reverse Logistics Network under Uncertainty. IJIEPR 2012; 23 (2) :113-123
URL: http://ijiepr.iust.ac.ir/article-1-443-fa.html
URL: http://ijiepr.iust.ac.ir/article-1-443-fa.html
Babazadeh Reza، Tavakkoli-Moghaddam Reza، Razmi Jafar. A Complex Design of the Integrated Forward-Reverse Logistics Network under Uncertainty. نشریه بین المللی مهندسی صنایع و تحقیقات تولید. 1391; 23 (2) :113-123
1- M.S. Student in Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2- Professor in Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran ،tavakoli@ut.ac.ir
3- Associate Professor in Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2- Professor in Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran ،
3- Associate Professor in Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
چکیده: (31464 مشاهده)
Design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. This paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. First, an efficient complex mixed-integer linear programming (MILP) model by considering some real-world assumptions is developed for the integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then, the stochastic counterpart of the proposed MILP model is used to measure the conditional value at risk (CVaR) criterion, as a risk measure, that can control the risk level of the proposed model. The computational results show the power of the proposed stochastic model with CVaR criteria in handling data uncertainty and controlling risk levels.
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