선대배치문제를 위한 분포적 강건 최적화 모형 연구
A Study on Distributed Robust Optimization Model for Fleet Deployment Problem
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초록
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.
Abstract
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.
참고문헌 (22)
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