A multi-commodity location-inventory problem in a closed-loop supply chain with commercial product returns

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

“…In the literature, many LIPs are solved by heuristic and meta-heuristic algorithms because of their great efficiency to solve such problems. Differential evolution [37] is an important approach that is widely applied to solve LIPs [31,32]. Although DE is a powerful tool to solve nonlinear problems, it has some drawbacks such as prematurity and local optimism.…”

“…Li, Guo, and Zhang [31] studied an integrated LIP in a CLSC with third-party logistics. Guo et al [32] proposed a new multi-commodity LIP by incorporating commercial product returns in CLSCs. As an extension, Guo et al [33] developed a more comprehensive model by considering the secondary market of used and refurbished products, and present a more powerful algorithm to solve the extended model.…”

“…As an extension of FLP, LIP which integrates location and inventory decisions is more complicated. Given its complexity, many heuristic algorithms have been developed to solve LIPs, such as simulated annealing [10], hybrid genetic algorithm [25] [28], particle swarm optimization [29], differential evolution [31] [32], hybrid harmony search [35], and tabu search [36]. In addition to those algorithms, differential evolution (DE) [37] has attracted much attention because of its good capability of solving NP-hard optimization problems.…”

Understanding product returns: A systematic literature review using machine learning and bibliometric analysis

“…It is clear that the papers in the earlier years focussed on various aspects of operations for production and inventory problems in both planning and control in manufacturing/remanufacturing operations (de Brito & van der Laan, 2009;DeCroix & Zipkin, 2005;Guide Jr, 2000;Mahadevan, Pyke, & Fleischmann, 2003;Minner & Kleber, 2001;Niknejad & Petrovic, 2014;Van Der Laan, Salomon, Dekker, & Van Wassenhove, 1999;Zhou, Tao, & Chao, 2011). Until recently, while the stream of inventory and production are still trending, the research focus has evolved to some contemporary 9 combinations such as trade-in and refurbished service level (Jiang et al, 2019;Shin et al, 2020), inventory policy via PR forecasting (Chou et al, 2020), location-inventory problems (Guo et al, 2020a(Guo et al, , 2020b, repairable inventory system (Lin, Leung, Zhang, & Gu, 2020), quality-grading scheme (Ponte et al, 2021), and capital investment (Reddy and Kumar, 2021). In terms of production, researchers have prominently addressed production issues along with machinery problems, failures and maintenance (Assid et al, 2020;Ndhaief et al, 2020;V.…”

Carbon emission-compliance green location-inventory problem with demand and carbon price uncertainties

“…Therefore, to fill this gap, we study the carbonemission compliance ILIP to investigate the low-carbon sustainable inventory control decisions by considering inventory storage CEC in our problem. As shown in Table 1.1, most of ILIP studies adopted (r, Q) inventory policy, and calculated the replenish quantity Q with the economic order quantity (EOQ) model (Daskin et al, 2002;Miranda & Garrido, 2004;Shen, 2007;Park et al, 2010;Escalona et al, 2015;Ahmadi-Javid & Hoseinpour, 2015;Puga & Tancrez, 2017;Shahabi et al, 2018;Tapia-Ubeda et al, 2018;Guo et al, 2019). Amiri-Aref et al (2018) reported that the periodic reorder point order-up-to level (T, s, S) inventory policy has been shown to be an optimal policy under stationary and non-stationary demand, with the capability of balancing the fixed ordering cost, the inventory holding cost and the shortage cost.…”