دانلود رایگان مقاله انگلیسی طراحی شبکه لجستیک معکوس برای بازیابی و بازسازی محصول - الزویر 2018

Abstract

Due to environmental concerns, reverse logistics now is becoming an important strategy to increase customer satisfaction. This research develops a generic mixed integer nonlinear programming model (MINLP) for reverse logistics network design. This is a multi-echelon reverse logistics model. It maximizes total profit by handling products returned for repair, remanufacturing, recycling, reuse, or incineration/landfill. A hybrid genetic algorithm (GA) is proposed to solve the problem. The designed model is validated and tested by using a real-life example of recycling bulk waste in Taoyuan City, Taiwan. Sensitivity analyses are conducted on various parameters to illustrate the capabilities of the proposed model. Post-optimality analysis and comparison show that the proposed model performs better than current reverse logistic operations and the proposed hybrid GA demonstrates the efficiency of solving the complex reverse logistics problem.

7. Conclusion

By using a multiple echelon network, it is shown that the proposed MINLP model can be used to represent complex reverse logistics processes in handling product recovery and remanufacturing. The objective of the mathematical model is profit maximization with consideration of multi-product and multi-module returnable products and a variety of recycling channels for the returned products. The numerical experiments reflect the real recovery processes of the used bulk waste products in a city of Taiwan, and the results show some distinctive features.

The proposed model is a generic model and can represent current reverse logistics operated by some industries using existing distribution centers, dismantling centers, warehouses, and factories for returned products. Facility location and available capacity are important issues in reverse logistics networks. By identifying the critical activities and related requirements involved in the processes of reverse logistics operations, the proposed model can determine the optimization of facility locations, their state of operation (open or closed), capacity utilization and the optimal flow of returned products and dismantled modules in the reverse network. The designed model is validated and tested through the proposed hybrid GA by using a real-life example of recycling bulk waste in Taoyuan City, Taiwan. Also, the post-optimality analysis and comparison show the proposed model performs better than current reverse operations in the city.