인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
In the wake of post-pandemic shifts toward e-commerce, customer orders have become increasingly fragmented and complex, especially in B2C environments. These orders often involve a wide variety of SKUs in small quantities, posing significant challenges for automated warehouse operations. Among various systems, Goods-to-Person (GTP) solutions offer efficiency in picking and packing. However, ensuring balanced task distribution across multiple GTP stations remains a critical issue to avoid bottlenecks and optimize throughput. This study explores four task allocation methodologies to address load balancing and processing time minimization in GTP systems. Using 30 days of real customer order data, we apply (1) a baseline Greedy Heuristic, (2) a combination of Hierarchical Clustering (by product category) with Greedy Heuristic, (3) Constraint Programming (CP-SAT) based on equal item quantity distribution, and (4) CP-SAT based on equal order line distribution. Each method is evaluated in terms of load balance (standard deviation among stations) and maximum station processing time. The experimental results reveal that CP-based methods significantly improve workload balancing without increasing total processing time. In contrast, the clustering-based method showed limited effectiveness due to the heterogeneous nature of B2C orders. These findings underscore the importance of constraint-based strategies for achieving operational efficiency in modern, high-variability fulfillment centers.
본문·목차
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