چکیده:
Due to business and environmental issues, the efficient design of an integrated forward/reverse logistics network has recently attracted more attention from researchers. The significance of transportation cost and customer satisfaction spurs an interest in developing a flexible network design model with different delivery paths. This paper proposes a flexible mixed-integer programming model to deal with such issues. The model integrates the network design decisions in both forward and backward logistics networks, and also applies three kinds of delivering modes (normal delivery, direct shipment, and direct delivery) which enrich the model to be able to deliver the products to customers by distribution-skipping the mid-process strategy in order to deliver products in more flexible paths to customer zones. To tackle with such an NP-hard problem, a memetic algorithm (MA) with random path-based direct representation and combinatorial local search methods is developed. Numerical experiments are conducted to demonstrate the significance and applicability of the model as well as the efficiency and accuracy of the proposed solution approach.
خلاصه ماشینی:
Based on the aforementioned considerations, this paper addresses the issue of flexible, integrated, multi-stage forward/reverse logistics network design including suppliers, production, distribution, collection/ inspection, recovery and disposal facilities with limited capacity.
(2008) developed a non-linear model and Tabu search solution approach for determining the locations of collection centers and the optimal purchase price of used products in a simple profit maximizing reverse logistics network.
M. Pishvaee, Torabi, & Razmi (2012) proposed a credibility-based fuzzy mathematical model for a forward supply chain network with three stages.
To avoid the sub-optimality that arises from the separate modelling of forward and reverse networks, many researchers have integrated forward and reverse network design, known as closed-loop SCND (CLSC) (Soleimani, Seyyed-Esfahani, & Kannan, 2014).
Coding of logistics network design research Field Title Symbol Modeling Stochastic mixed integer programming SMIP Fuzzy mixed integer programming FMIP Mixed integer non-linear programming MINLP Mixed integer linear programming MILP Solution approach Exact Branch and bound B& B Lagrangian relaxation-based LR Genetic algorithm GA Simulated annealing SA Tabu search-based TS Interactive fuzzy solution approach F Others heuristics H Outputs Suppliers/orders S Facilities location L Facility capacity FC Allocation Al Production amount PQ Production assignment to production centers PA Utilization of production centers UT Transportation amount TA Transportation mode TM Delivery mode DM Inventory I Price of products P Table 2.
Framework of the flexible integrated forward/reverse logistics network The proposed model is based on the following common assumptions in the literature (Syarif, Yun, & Gen, 2002; H.