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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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The fleet deployment problem is a strategic planning problem in which shipping liner allocates its owned and chartered vessels to routes it service to meet uncertain demand for shipping services. According to the fleet deployment plan, the capacity and cost of services provided on each route are determined for several months, making it crucial for shipping liner to determine the optimal deployment to meet demand and reduce costs during this period. In this problem, transport demand on each route being a key input parameter. However, knowing the exact transport demand at the planning stage is impossible, and a deterministic model for this problem can incur additional costs if the actual demand differs from the planned stage. Therefore, robust decision-making that accounts for such demand uncertainty is necessary. Therefore, many studies have been conducted in this regard, and they have proposed models that consider the distribution of the maximum demand between two consecutive ports on each route. In this study, we propose a distributionally robust optimization (DRO) model that considers the demand between all ports on each route and an approximate model to find the optimal solution. Through experiments, we compare the solutions derived from the proposed model with the optimal solutions proposed by the deterministic model and previous studies, demonstrating the necessity of considering all transportation demands.
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