A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

Abstract

The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government’s reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders.

Forging Pathways to Circular Economy Excellence: Integrating Industry 4.0 with Quality Management

“…Berlin, et al [162] investigated the interconnectedness of open-and closed-loop supply chains in a CE, thereby advocating for the adoption of closed-loop systems to achieve resource sustainability. Regarding the most important factor in sustainability, carbon emissions, Li, et al [163] and Abbasi and Erdebilli [164] proposed a necessity-based optimization approach and explored the resilience of green closed-loop supply chains under the uncertainty of carbon policies, respectively. Scholars have also discussed critical factors in closed-loop supply chains, such as the strategic dimensions of pricing and advertising decisions in direct sales [165] and hybrid meta-heuristic algorithms tailored for dual-channel networks [166].…”

A Routing Model for the Distribution of Perishable Food in a Green Cold Chain

“…A very important contribution to the topic is found in [44]: a vehicle-routing problem with time windows (VRPTW) with compatibility matching constraints and the total completion time as the objective function is proposed, with applications in home healthcare routing and scheduling. An approach close to ours is found in [45]. Here, the problem of recycling waste products through closed-loop logistics networks is analyzed by using a fuzzy programming model to minimize the total network cost and the sum of carbon rewards and penalties when selecting recycling locations, facilities and transportation routes among network nodes.…”