A new model proposal for reverse logistics network design

Reverse logistics has received increasing attention in the last decades because of the increasing environmental concern and also depletion of natural resources. The design of product recovery networks including the collection, reprocessing, redistribution and disposal of the returned products is one of the important and challenging reverse logistics problems. The aim of this thesis is to introduce a new perspective of network design to strategic planning in reverse logistics environment. For this purpose, a new mixed integer programming model for a reverse logistics network including the ?incremental incentive? payment to customers and a Steady-State Genetic Algorithm hybridized with GAMS-Cplex as a heuristic solution methodology are proposed. Different scenarios are generated using different parameter settings such as demands at secondary markets, returned product quantities, acceptance rates determining the availability of returned products in terms of quality for recovery and incentive payment. Performance of the mathematical model and proposed solution method has been tested through different size test problems. Results demonstrated that the proposed methodology performs very well in terms of both quality of solutions and computational time.