This research demonstrates improved operational revenue in tramp shipping through a novel scheduling algorithm, highlighting the impact of stochastic factors on decision-making.
Abstract The operational decisions faced by tramp shipping fleet operators are crucial, particularly in the current volatile global economic and trade environment. Navigating through uncertain market conditions poses a significant challenge for operators striving to optimize the operational decisions of their tramp shipping fleets. This paper delves into the tramp shipping fleet scheduling and bunkering problem, incorporating stochastic demand and bunker prices, with the objective of maximizing operational revenue while adhering to specified service levels for shippers. We introduce the service level for shippers as the probability of demand being served and formulate our research problem as a stochastic mixed-integer programming model with a service level constraint. To address this model, we transform it into a multi-objective reachability problem, defining operational revenue and service level requirements while setting a total operational revenue target. Concurrently, we propose a Debt Related Tramp Shipping Fleet Scheduling and Bunkering (DRTSFSB) algorithm framework to guide fleet scheduling and bunkering decisions in each scenario. The numerical experiments conducted conclusively demonstrate that the proposed approach significantly reduces computation time while maintaining comparable performance to the Sample Average Approximation (SAA) method. This underscores the superiority of our approach in addressing operational decision problems for tramp shipping fleets under stochastic conditions.
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Gao et al. (2025) studied this question.
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