Optimization of direct transshipment scheduling for river–sea intermodal transport with vessel arrival time matching

Yuan, J.; Wu, S.; Zhang, Y.; Cheng, C.; Wang, S.; Ma, F.; Liu, Z.; Ji, Y.

Growing Science » International Journal of Industrial Engineering Computations » Optimization of direct transshipment scheduling for river–sea intermodal transport with vessel arrival time matching

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 17 Issue 1 pp. 163-184 , 2026

Optimization of direct transshipment scheduling for river–sea intermodal transport with vessel arrival time matching Pages 163-184 Download PDF

Authors: Jiashan Yuan, Shuang Wu, Yong Zhang, Cheng Cheng, Shuaiqi Wang, Feiyang Ma, Zhiyuan Liu, Yihuan Ji

DOI: 10.5267/j.ijiec.2025.10.005

Keywords: Dry Bulk River–sea Intermodal Transport, Direct Transshipment Scheduling, Vessel Arrival Time Matching, Multi-objective Optimization, Genetic Algorithm

Abstract: Dry bulk river–sea intermodal transport is a critical consideration when connecting inland waterways and oceanic shipping, yet its efficiency hinges on precise vessel arrival time matching. The challenge of vessel arrival time matching has been exacerbated by existing research gaps. Current studies often focus on single vessel types or static scenarios, lacking integrated optimization of dynamic coordination between sea-going and river vessels, and failing to unify time and cost objectives. To address this, we develop a multiobjective scheduling model incorporating real-time arrival data from the dry bulk river–sea intermodal information platform to minimize total port time and operational costs. A heuristic genetic algorithm with adaptive weight adjustment (λ) is designed, achieving convergence within 200 iterations and a solution time of 33 seconds. This algorithm is validated under balanced conditions (λ=0.5) and is shown to yield 108.53 hours of total port time and 278,165.2 yuan in operational costs. Sensitivity analysis reveals a significant tradeoff: λ is reduced from 0.9 to 0.1, leading to an increase in port time by 1.42% but a reduction in costs of 3.03%. This reflects an improved flexibility in cost optimization as a result of resource manipulability. In contrast, port time is constrained by physical limits, such as loading/unloading efficiency. The framework developed provides practical decisional support for ports, with higher λ values (0.7–0.9) enabling rapid turnover in congestion and lower values (0.1–0.3) prioritizing cost economy. Future work should extend this approach to stochastic environments and incorporate multistakeholder coordination using game theory approaches.

How to cite this paper

 Yuan, J., Wu, S., Zhang, Y., Cheng, C., Wang, S., Ma, F., Liu, Z & Ji, Y. (2026). Optimization of direct transshipment scheduling for river–sea intermodal transport with vessel arrival time matching.International Journal of Industrial Engineering Computations , 17(1), 163-184.

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