Sustainable closed-loop supply chain model with stochastic demand and return

The closed-loop supply chain (CLSC) represents the supply chain that contains recovery plans for used products to be reused later in the industry. The research gap in the previous CLSC literature regarding the sustainability aspects for the CLSC was the lack of consideration for the social aspect due to its complexity. Also, the effect of demand and product returns uncertainties on the sustainability aspects of CLSC have not been considered in the previous CLSC studies under centralized management and synchronized ordering policy (SOP). Moreover, the analysis of stochastic sustainable CLSC has not been investigated in industries such as home appliances. Finally, the CLSC sustainability has not been analyzed with previous consideration of CLSC tiers coordination. Thus, the objective of this research was to model a stochastic sustainable CLSC that included the environmental, economic, and social aspects while considering uncertainties in demand and returns quantities for home appliance products under SOP and centralized management. For the proper stochastic CLSC design and mathematical modeling of the sustainable stochastic CLSC, the mathematical model was developed as a multiproduct, multi-echelon single period CLSC model under the SOP and centralized management. The stochastic sustainable CLSC model added constraints related to inventory management for the SOP, production capacity, transportation capacity, and carbon emission. The sustainable stochastic CLSC model was described as a mixed-integer non-linear programming model. The sustainability objectives were optimized using the Pareto-based constrained optimization of nonlinear approximation algorithm (Pareto-based COBYLA) by identifying the optimal quantities of parts and products for each CLSC tier and solving the optimization problem using the PYTHON program which was then compared with the nondominated sorting genetic algorithm (NSGA). The optimization results showed the significance of the stochastic demand and product return parameters on the sustainability objectives in the stochastic sustainable home appliance CLSC following SOP. The current research has contributed to the CLSC studies by developing a stochastic sustainable CLSC mathematical model following the SOP assumption for home appliance products considering the economic, environmental, and social objectives and adding the Pareto-based COBYLA solution algorithm that provided a better optimal solution for stochastic sustainable CLSC optimization in comparison with the NSGA

A Review on Remanufacturing Reverse Logistics Network Design and Model Optimization

Remanufacturing has gained great recognition in recent years due to its economic and environmental benefits and effectiveness in the value retention of waste products. Many studies on reverse logistics have considered remanufacturing as a key node for network optimization, but few literature reviews have explicitly mentioned remanufacturing as a main feature in their analysis. The aim of this review is to bridge this gap. In total, 125 papers on remanufacturing reverse logistics network design have been reviewed and conclusions have been drawn from four aspects: (1) in terms of network structure, the functional nodes of new hybrid facilities and the network structure combined with the remanufacturing technologies of products are the key points in the research. (2) In the mathematical model, the multi-objective function considered from different aspects, the uncertainty of recovery time and recovery channel in addition to quantity and quality, and the selection of appropriate algorithms are worth studying. (3) While considering product types, the research of a reverse logistics network of some products is urgently needed but inadequate, such as medical and furniture products. (4) As for cutting-edge technologies, the application of new technologies, such as intelligent remanufacturing technology and big data, will have a huge impact on the remanufacturing of a reverse logistics network and needs to be considered in our research