An eXplainable Multi-Stage Stochastic Optimisation for Bunker Procurement Planning

49 Pages Posted: 20 Aug 2023

See all articles by Wei Li

Wei Li

National University of Singapore (NUS) - Institute of Operations Research and Analytics

Qinghe Sun

National University of Singapore (NUS) - Institute of Operations Research and Analytics

Ying Chen

National University of Singapore (NUS) - Department of Mathematics

Mabel C. Chou

National University of Singapore (NUS) - Sustainable & Green Finance Institute (SGFIN)

Date Written: August 18, 2023

Abstract

Bunker procurement decisions significantly impact shipping industry operating expenses. However, several challenges hinder obtaining optimal decisions, including the need to forecast future fuel prices, incorporate uncertainties and capacity constraints, and understand feature importance for transparent solutions. To address these difficulties and foster confidence in the conservative business sector, we propose an eXplainable multi-stage bunker procurement planning (X-BPP) framework for the maritime industry. Our study develops a machine learning forecaster for accurate and robust price predictions from short to long term, considering multiple bunker ports on the route. We demonstrate coherent forecast integration into operation optimization through tractable reformulation. Kernel Shapley is employed to reveal feature importance in non-linear and multi-stage stochastic planning. In a real-world implementation, we evaluate the unified planning framework’s practical knowledge and awareness. Results show an average operating cost reduction of $257,541.51 for a fleet of six vessels during a 42-day Asia-North America trip, based on data from July 2020 to December 2021. During the Russia-Ukraine war, the framework still achieved $351,247.71 in savings. Additionally, we identify varying important features between short- and long-term forecasting.

Keywords: Bunker procurement planning, Bunker price forecasting, Machine learning, eXplainable AI, OR practice

JEL Classification: R42, G13, G53

Suggested Citation

Li, Wei and Sun, Qinghe and Chen, Ying and Chou, Mabel C., An eXplainable Multi-Stage Stochastic Optimisation for Bunker Procurement Planning (August 18, 2023). Available at SSRN: https://ssrn.com/abstract=4544710 or http://dx.doi.org/10.2139/ssrn.4544710

Wei Li (Contact Author)

National University of Singapore (NUS) - Institute of Operations Research and Analytics ( email )

Singapore

Qinghe Sun

National University of Singapore (NUS) - Institute of Operations Research and Analytics ( email )

Innovation 4.0, #04-01, 3 Research Link
117602
Singapore

Ying Chen

National University of Singapore (NUS) - Department of Mathematics ( email )

119076
Singapore

Mabel C. Chou

National University of Singapore (NUS) - Sustainable & Green Finance Institute (SGFIN) ( email )

Singapore

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