Distributionally Robust Model and Metaheuristic Frame for Liner Ships Fleet Deployment

Mihaela Bukljaš, Kristijan Rogić and Vladimir Jerebić
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Mihaela Bukljaš: Department of Water Transport, Faculty of Transportation and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia
Kristijan Rogić: Department of Transport Logistics, Faculty of Transportation and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia
Vladimir Jerebić: Faculty of Transportation and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia

Sustainability, 2022, vol. 14, issue 9, 1-18

Abstract: The container shipping industry market is very dynamic and demanding, economically, politically, legally, and financially. Considering the high cost of core assets, ever rising operating costs, and the volatility of demand and supply of cargo space, the result is an industry under enormous pressure to remain profitable and competitive. To maximize profits while maintaining service levels and ensuring the smooth flow of cargo, it is essential to make strategic decisions in a timely and optimal manner. Fleet deployment selection, which includes the profile of vessel hire, as well as their capacity and port rotation, is one of the most important strategic and tactical decisions container shipping operators must make. Bearing in mind that maritime business is inherently stochastic and uncertain, the key aims of this paper are to address the problem of fleet deployment under uncertain operating conditions, and to provide an integrated and optimized tool in the form of a mathematical model, metaheuristic algorithm, and computer program. Furthermore, this paper will show that the properties of the provided solutions exceed those offered in the literature so far. Such a solution will provide the shipping operator with a decision tool to best deploy its fleet in a way that responds more closely to real life situations and to meet the maximum demand for cargo space with minimal expense. The final goal is to minimize the operating costs while managing cargo flows and reducing the risks of unfulfilled customer demands.

Keywords: liner shipping; fleet deployment; distributional robust optimization; chance constrained optimization; metaheuristics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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