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
Fast and reliable delivery service is essential for customer satisfaction. This study examines how food delivery platforms assign customer orders to riders. Although the most efficient method is to assign an order to the rider closest to the store, many platforms instead allow riders to self-select orders due to operational constraints. We compare the rider self-selection method with a centralized shortest-distance assignment method to evaluate efficiency. In addition, we investigate how variations in the exponential distribution of order arrivals influence assignment outcomes.
Specifically, peak and off-peak hours are analyzed, and the shortest-distance assignment consistently outperforms rider self-select. It significantly reduces both rider assignment time and travel distance to the store. These results suggest that adopting improved assignment strategies can enhance delivery efficiency, optimize platform operations, and provide better service quality, offering practical implications for platform managers and policymakers. Experimental results show that in all scenarios with exponentially distributes order arrivals, the centralized shortest-distance assignment method outperforms the rider.
Specifically, peak and off-peak hours are analyzed, and the shortest-distance assignment consistently outperforms rider self-select. It significantly reduces both rider assignment time and travel distance to the store. These results suggest that adopting improved assignment strategies can enhance delivery efficiency, optimize platform operations, and provide better service quality, offering practical implications for platform managers and policymakers. Experimental results show that in all scenarios with exponentially distributes order arrivals, the centralized shortest-distance assignment method outperforms the rider.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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