Optimizing Make-to-order Supply-Chain Networks Considering Customer-Service Level in a Metaverse Environment

메타버스 환경에서 고객서비스 수준을 고려한 주문생산 공급망 네트워크 최적화

Purpose: This study proposes a customer-service level (CSL)-based make-to-order supply-chain network (MO-SCN) for a metaverse environment to customise and diversify supply and segment customer needs. Research design, data, and methodology: To efficiently represent the proposed MO-SCN model, three types of customisable products and transportation routes were divided based on their CSL values. MO-SCN was formulated as a nonlinear integer programming model, and single (genetic algorithm (GA), JAYA) and hybrid metaheuristic algorithms combining GA, JAYA, and a fuzzy logic controller were compared over three stages. Additionally, three scenarios were considered for the numerical experiments. Results: The results showed that the hybrid metaheuristic algorithms (right-JAYA (RJA) and left-JAYA (LJA)) are superior than the single algorithms. Additionally, the hybrid metaheuristic algorithms that combined the three approaches (GJF and GFJ) exhibited better performance than both RJA and LJA. From the perspective of developing management strategies, profitability increases as the CSL increases, thereby confirming the significance and efficiency of MO-SCN. Implications: The proposed model can help businesses develop targeted strategies and earn the loyalty of customers, leading to improved revenues and customer satisfaction.