Title: An approximate dynamic programming algorithm for multi-period maritime fleet renewal problem under uncertain market

Authors: Tingsong Wang; Xuecheng Tian; Xu Guan

Addresses: School of Management, Shanghai University, Shanghai 200444, China ' School of Economics and Management, Wuhan University, Wuhan, 430072, China ' School of Management, Huazhong University of Science and Technology, Wuhan, 430000, China

Abstract: This paper studies the maritime fleet renewal problem under an uncertain market, including various uncertain factors such as ship prices, shipping demand, and freight rates. A dynamic programming model for this problem is proposed. However, the traditional methods to solve the dynamic programming model may encounter the challenge of 'curses of dimensionality' due to their backward iterations used in these methods, which makes them cumbersome to be applied in practice. Therefore, an approximate dynamic programming (ADP) algorithm is employed to solve the proposed model in this paper. Finally, a case study is implemented to evaluate the applicability and performance of the ADP algorithm and analyse the impact of the uncertain market on fleet renewal. The fleet renewal plan obtained from our proposed methodology is more robust and adaptable to the uncertain market than that obtained from the deterministic model in which the various uncertain parameters are replaced by their predicted values.

Keywords: maritime fleet renewal; dynamic programming; approximate dynamic programming algorithm; uncertainty.

DOI: 10.1504/IJSTL.2023.128551

International Journal of Shipping and Transport Logistics, 2023 Vol.16 No.1/2, pp.96 - 119

Received: 09 Jul 2020
Accepted: 26 Feb 2021
Published online: 26 Jan 2023 *

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