Due to the depletion of primary resources, the increase in environmental pollutants, an‎d the growing volume of production waste, revisiting production processes an‎d the recovery of used products has become one of the main concerns of governments, decision-makers, an‎d industries. If these trends are ignored an‎d traditional production patterns continue, they may lead to a severe reduction in non-renewable resource reserves, higher raw material procurement costs, an‎d intensified environmental crises. In this context, the design an‎d implementation of closed-loop supply chain (CLSC) networks is considered an efficient approach. Simultaneous attention to different sales an‎d collection channels, along with strengthening the reverse supply chain an‎d improving the return rate through the application of consumer incentive policies, plays a significant role in achieving both economic an‎d environmental objectives. In this thesis, a dual-channel closed-loop supply chain network is designed. The proposed network consists of seven echelons, where consumer incentive policies are considered as a mechanism to enhance the reverse flow of products. Within this framework, a multi-period, multi-product mixed-integer nonlinear programming (MINLP) model is developed with the objective of minimizing the total network cost. These costs include facility establishment, production an‎d remanufacturing, transportation, collection an‎d disposal of returned products, an‎d consumer incentive payments. The model incorporates decisions such as facility location, production quantities, inventory management, product flows in the network, the amount of consumer incentives, an‎d product return rates. To solve the problem, a hybrid solution approach is developed based on decomposing the problem into smaller subproblems. Specifically, a genetic algorithm is employed for binary decision variables related to facility location, while the golden section search algorithm is used to determine the values of continuous decision variables associated with consumer incentives. The performance of the proposed hybrid approach is compared with the BARON solver on a set of ran‎domly generated instances of different sizes. For small-scale problems, the maximum gap between the solutions obtained by the hybrid approach an‎d BARON is 0.87%, an‎d in four instances the solutions generated by the hybrid approach are of higher quality than those of BARON. Moreover, computational time analysis shows that, as the problem size increases, the solution time of BARON grows by about 224 times, whereas the solution time of the hybrid approach increases by only 71 times. For large-scale problems, the maximum improvement of the hybrid approach over BARON reaches 15.7%, the minimum improvement is 0.06%, an‎d on average the hybrid approach provides solutions that are 4.49% better than those of BARON. In addition, the average solution times for large-scale instances are 9357.41 seconds for BARON an‎d 131.66 seconds for the hybrid approach, confirming the efficiency of the proposed method. Finally, sensitivity analysis on deman‎d an‎d production capacity parameters revealed that the cost components of the objective function are more sensitive to changes in deman‎d than to changes in production capacity. Furthermore, variations in the price of the new product, compared to changes in distance, have a greater impact on three key decision variables—the offline an‎d online return rates an‎d the level of consumer incentives—with the offline return rate exhibiting higher sensitivity than the online return rate.

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محمد رئيسي نافچي , مهدي علينقيان