Optimizing the Operational Scheduling of Automaker’s Self-Owned Ro-Ro Fleet

Feihu Diao, Yijie Ren and Shanhua Wu ()
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Feihu Diao: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Yijie Ren: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Shanhua Wu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China

Sustainability, 2025, vol. 17, issue 19, 1-27

Abstract: With the surge in global maritime trade of new energy vehicles (NEVs), the roll-on/roll-off (Ro-Ro) shipping market faces a severe supply–demand imbalance, pushing shipping rates to persistently high levels. To tackle this challenge, NEV manufacturers and other automakers have begun establishing their own Ro-Ro fleets, creating an urgent need for optimized operational scheduling of these proprietary fleets. Against this context, this study focuses on optimizing the operational scheduling of automakers’ self-owned Ro-Ro fleets. Under the premise of deterministic automobile export transportation demands, a mixed-integer programming model is developed to minimize total fleet operational costs, with decision variables covering vessel port call sequence/selection, port loading and unloading quantities, and voyage speeds. A genetic algorithm is designed to solve the model, and the effectiveness of the proposed approach is validated through a real-world case study. The results demonstrate that the optimization method generates clear, actionable scheduling schemes for self-owned Ro-Ro fleets, effectively helping automakers refine their maritime logistics strategies for proprietary fleets. This study contributes to the field by focusing on automaker-owned Ro-Ro fleets and filling the research gap in cargo-owner-centric scheduling, providing a practical tool for automakers’ overseas logistics operations.

Keywords: operational scheduling optimization; Ro-Ro fleet; self-owned fleet; NEV manufacturers (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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