Optimization modeling for the operation of closed-loop supply chains

Environmentally conscious manufacturing and remanufacturing/recycling of end-of-life products are steadily growing in importance. The problem of managing the waste generated due to the disposal of many types of products has many aspects. The main driving forces for solving this growing problem are the rapid diminishment of raw material resources, decreasing space in landfills and increasing levels of pollution. The drivers associated with these forces are governmental regulations which require that the manufacturers take back the end-of-life products and customer perspectives on environmental issues.; This research considers the problem of increasing levels of electronic and electrical equipments waste. The implementation of closed-loop supply chains can be beneficial both economically and ecologically for these problems. Relevant literature to understand various issues involved in the operation of reverse logistics systems and closed-loop supply chains is reviewed.; Upon reviewing the issues involved in closed-loop supply chains, the problem is considered as an ill-structured problem. A problem structuring technique called Why-What's Stopping Analysis is used to analyze the problem from various perspectives. Also, since a closed-loop supply chain involves multiple objectives, two techniques for categorizing the objectives into fundamental and means objectives are presented: Fundamental Objective Hierarchy and Means Objective Network techniques, respectively.; A Goal Program (GP) modeling approach is used to handle many of the objectives identified by the previously mentioned techniques. In this research a consolidated objective function is defined which includes all of the deviational variables considered in various goals defined in the model. The consolidated goal is to minimize the weighted sum of all deviational variables. A non preemptive goal programming approach has been used with goals being assigned different weights according to their priorities. The values of the deviational variables help the decision maker to see which of the different goals are satisfied with the existing values of parameters and which of the goals aren't.; The goal program has been run with both uniform and variable demand values in all the periods. In the absence of real data, all the parameter values considered for this research have been assumed. The major contributions of the research are as follows: each member of the supply chain has its own individual objective and the related constraints which is a more realistic approach, the model considers multiple products, and the model considers operations at the product, subassembly, part, and material levels. All the above contributions make this research as the first approach of its kind which has never been attempted (based on literature reviewed) and the goal programming methodology used is also a well accepted approach among all the multi-objective programming approaches.; Results show the effect of varying the priority/weight associated with a goal. Results also show that values of the deviational variables (positive or negative) help a decision maker to analyze the model. The goal programming approach is considered to be the most effective approach in terms of defining the mathematical model, analyzing the output, and modifying the model (if needed).