Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

Byeong Ju Jo, Young Kwan Ko, Yonghui Oh and Young Dae Ko ()
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Byeong Ju Jo: Department of Hotel and Tourism Management, College of Hospitality and Tourism, Sejong University, 209 Neungdong-ro, Seoul 05006, Republic of Korea
Young Kwan Ko: Department of Hotel and Tourism Management, College of Hospitality and Tourism, Sejong University, 209 Neungdong-ro, Seoul 05006, Republic of Korea
Yonghui Oh: Department of Industrial and Management Engineering, Daejin University, 1007 Hoguk-ro, Pocheon-si 11159, Gyeonggi-do, Republic of Korea
Young Dae Ko: Department of Hotel and Tourism Management, College of Hospitality and Tourism, Sejong University, 209 Neungdong-ro, Seoul 05006, Republic of Korea

Sustainability, 2024, vol. 16, issue 10, 1-18

Abstract: As society continues to age, people are becoming more concerned about their health care. This has led to an increase in the demand for medical products in urban areas, emphasizing the need for regular and prompt deliveries. However, the existing logistics centers are located in the suburbs of Seoul, a metropolitan city, which makes it challenging to ensure timely delivery. To address this issue, this study aims to establish new logistics centers in urban areas, particularly in Seoul, while minimizing CO 2 emissions from delivery vehicles in alignment with sustainability efforts. The scientific gap addressed in and the novelty of this paper is that the input parameters are prepared based on actual data from a medical company in Korea to reflect reality, and the mathematical model-based optimization technique is applied to determine the optimal location of a new logistics center. The genetic algorithm is developed to solve the proposed mathematical model by deriving optimal or near-optimal solutions. Furthermore, the numerical experiment examined the impact of establishing a new logistics center in one of the candidate areas of local governments in Seoul by considering environmental impact. As a result, the new logistics network can reduce CO 2 emissions by approximately 66.74% compared to the existing logistics network.

Keywords: logistics center; mathematical model; vehicle routing problem; genetic algorithm; sustainable transportation; CO 2 emission (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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