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
This study addresses the problem of selecting optimal product variants by maximizing market utility relative to the system complexity of an assembly supply chain (ASC) network. On the supply side, ASC system complexity is quantitatively measured by integrating both ASC network complexity and assembly line complexity using Shannon entropy, which captures the degree of uncertainty and structural intricacy inherent in the system. On the market side, a multinomial logit (MNL) model is employed to estimate customer choice probabilities based on product variants and segment preferences. These probabilities are then aggregated across all market segments to compute the expected market utility of a given set of product variants. To effectively solve the resulting combinatorial optimization problem, a genetic algorithm (GA) is utilized to search for the product variant set that maximizes the utility to complexity ratio. A series of market scenarios, defined by variations in both the mean and variance of market utility distributions, are constructed to evaluate the robustness, adaptability, and performance of the proposed approach. Experimental results reveal that shifts in market utility distribution characteristics significantly affect the optimal selection strategy, underscoring the importance of simultaneously considering both supply side complexity and market side benefits in designing an efficient and competitive product line.
본문·목차
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