A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery

Abstract

Product recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) in a closed-loop supply chain (CLSC) is investigated to optimize facility location and inventory control decisions by considering product recovery. The objective is to optimize facility location and inventory control decisions to minimize the total cost of business operations in a closed-loop supply chain system. We formulate this problem as a mixed-integer nonlinear programming model and design a modified hybrid differential evolution algorithm (MHDE) to solve it efficiently. Finally, numerical results are presented to validate the performance of the new algorithm. The results show that MHDE is more efficient and effective than Lingo and other algorithms for the research problem under study. Managerial insights are also derived for business managers to improve their supply chain performance.

Optimization of “vehicle-UAV” joint distribution routing for cold chain logistics considering risk of epidemic spreading and green cost

“…According to the above discussion, we introduce the key details, subroutines and algorithm flow of MACO as follows [ 45 ].…”

“…Eq (43) represents that the goods weight of a large UAV team starting from the logistics center cannot exceed its capacity. Eq (45) is the formula of the waiting time the team of large UAVs needs at a housing estate, where v 3 is the flying speed and φ 3 is the total capacity of a large UAV team.…”

“…The parameter settings of these algorithms do have obvious effects on the solution quality. Thus, we first refer to the relevant literature [15,[42][43][44][45] which focused on the intelligent optimization algorithms for solving LPR. According to the above literature, we set the maximum iterations is 300 for GA and MACO.…”

Modeling the Decision and Coordination Mechanism of Power Battery Closed-Loop Supply Chain Using Markov Decision Processes

“…For example, Ali P et al (2023) [10] incorporated the vehicle path problem into the optimization model of a closed-loop supply chain network, considered factors such as product demand, recycling volume, transportation cost, and environmental impacts, and developed a mixed-integer linear programming model to minimize the total cost of the closed-loop supply chain network and proposed an effective solution algorithm; Mehrnaz B et al [11] designed a new closedloop supply chain network with a location-allocation and routing model that considers simultaneous recycling and distribution and optimizes under uncertainty. The model involves problems in transportation scheduling, such as how to determine appropriate location, allocation, and routing schemes according to the recycling and distribution demands in different regions and time periods, so as to optimize the transportation cost, transportation time, transportation distance, and other metrics; Hao G et al [12] proposed a hybrid differential evolutionary algorithm for solving the location-inventory problem in a closed-loop supply chain with product recycling. The problem involves transportation scheduling aspects, such as how to determine the appropriate location, inventory level, and transportation resource allocation scheme according to the recycling and distribution demand in different regions and time periods, so as to optimize the transportation cost, transportation time, transportation distance, and other metrics.…”