نبذة مختصرة : 학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2023. 8. 문일경. ; With the growth of both communications technology and contact-free delivery demand, e-commerce has grown significantly during the past few years. However, fierce competition and the inherent uncertainty in the e-commerce marketplace have made retailers suffer from high operations costs. Because of such circumstances, the concept of the sharing economy has been confirmed as an innovative business model that can answer the need for more flexible logistics. Therefore, we aim to develop decision-making frameworks considering logistics resources sharing under uncertainty. In this thesis, we address three problems in the supply chain management field: (1) the perishable inventory problem, (2) the supply chain network design, and (3) the omnichannel retail operations. In addition, we consider the three different services or strategies to share logistic resources in the abovementioned problems. First, we address the perishable inventory problem considering transshipment and online-offline channel system. We present a Markov decision process model by accommodating key attributes of the online-offline channel system. We develop the hybrid deep reinforcement learning algorithm based on the soft actor-critic algorithm to overcome the curse of dimensionality in the large-scale Markov decision process. In addition, we found that transshipment substantially reduces the outdating cost by allowing the offline channel to make good use of the old products that will be discarded in the online channel, which is new to the literature. Second, we propose the supply chain network design problem considering the on-demand warehousing system. 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 ...
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