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Vessel Deployment with Limited Information: A Robust Chance Constrained Model

Yue Zhao(yuezhao***at***u.nus.edu)
Zhi Chen(zhi.chen***at***cityu.edu.hk)
Andrew Lim(isealim***at***nus.edu.sg)
Zhenzhen Zhang(isezz***at***nus.edu.sg)

Abstract: This paper studies the important vessel deployment problem in the liner shipping industry, which decides the numbers of mixed-type ships and their sailing frequencies on the fixed routes to provide sufficient vessel capacity for fulfilling stochastic shipping demands with high probability. In reality, it is difficult (if not impossible) to acquire a precise joint distribution of shipping demands, as they may fluctuate heavily due to the fast-changing economic environment or unpredictable events such as the global pandemic. To address this challenge, we leverage the recent advances in distributionally robust optimization and propose a distribution-free robust joint chance constrained model. In particular, we only assume support, mean, and lower-order dispersion information of the shipping demands and provide high-quality solutions via a sequential convex optimization algorithm. Comparing with existing literature that chiefly studies individual chance constraints based on concentration inequalities and the union bound, our approach yields solutions that are less conservative and less vulnerable to the magnitude of demand dispersion.

Keywords: maritime transportation, fleet deployment, liner shipping, robust optimization, joint chance constraints, sequential convex optimization

Category 1: Applications -- OR and Management Sciences (Transportation )

Category 2: Robust Optimization


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Entry Submitted: 01/15/2021
Entry Accepted: 01/15/2021
Entry Last Modified: 01/15/2021

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