https://orcid.org/0000-0002-7072-1351
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Abstract
During the COVID-19 pandemic, e-commerce retailers have had trouble satisfying the growing demand because of limited warehouse capacity constraints. Fortunately, an on-demand warehousing system has emerged as a new alternative to mitigate warehouse capacity issues. In recent years, several studies have focused on the supply chain problem considering on-demand warehousing. However, there is no study that deals simultaneously with inherent uncertainties and the property of commitment, which is the main advantage of on-demand warehousing. To fill these research gaps, this paper presents an e-commerce supply chain network design problem considering an on-demand warehousing and decisions for commitment periods. We propose the two-stage stochastic programming model that captures the inherent uncertainties to formulate the presented problem. We solve the proposed model utilizing sample average approximation combined with the Benders decomposition algorithm. Of particular note, we develop a method to generate effective initial cuts for improving the convergence speed of the Benders decomposition algorithm. Computational results show that the developed method could find an effective feasible solution within a reasonable computational time for problems of practical size. Furthermore, we show the significant cost-saving effects, based on experiment results, that occur when an on-demand warehousing system is used for designing supply chain networks.
Acknowledgements
The authors are grateful for the valuable comments from the associate editor and two anonymous reviewers.
Data availability statement
The data that support the findings of this study are available from the author, Junhyeok Lee ([email protected]), upon reasonable request.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Junhyeok Lee
Junhyeok Lee is currently a Ph.D. student in the Department of Industrial Engineering at Seoul National University in Korea. He received the B.S. in School of Air Transportation and Logistics from Korea Aerospace University, Korea, and M.S. in Department of Industrial Engineering from Seoul National University, Korea, in 2019 and 2021, respectively. His research interests include SCM, reinforcement learning and stochastic programming.
Changseong Ko
Chang Seong Ko is a Professor of the Department of Industrial & Management Engineering, Kyungsung University, Busan, Korea. He received his PhD in Industrial Engineering from the KAIST. His research interests include logistics system and supply chain management. He has published numerous papers in various scholarly journals such as International Journal of Production Research, International Journal of Production Economics, Computers and Industrial Engineering and Omega.
Ilkyeong Moon
Ilkyeong Moon is a Professor of Industrial Engineering at Seoul National University in Korea. He received his B.S. and M.S. in Industrial Engineering from Seoul National University, and Ph.D. in Operations Research from Columbia University. His research interests include supply chain management, logistics, and inventory management. He published over 150 papers in international journals. He was a former Editor-in-Chief of Journal of the Korean Institute of Industrial Engineers which is a flagship journal of Korean Institute of Industrial Engineers (KIIE). He was a president of KIIE in which he had served from 2019 to 2020. He currently serves as associate editors for several journals including European Journal of IE and Flexible Services and Manufacturing. He is a fellow of Asia Pacific Industrial Engineering and Management Society and a vice chair of WG 5.7 of IFIP.