Multi-Objective Optimization for Intermodal Freight Transportation Planning: A Sustainable Service Network Design Approach

Alexander Chupin (), Abdelaal Ahmed Mostafa Ahmed Ragas, Marina Bolsunovskaya, Alexander Leksashov and Svetlana Shirokova
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Alexander Chupin: Department of International Economic Relations, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
Abdelaal Ahmed Mostafa Ahmed Ragas: Accounting and Finance Department, United Arab Emirates University (UAE), Al Ain P.O. Box 15551, United Arab Emirates
Marina Bolsunovskaya: Graduate School of Intelligent Systems and Supercomputing Technologies, Peter the Great St. Petersburg Polytechnic University (SPbPU), 29 Polytechnicheskaya Street, 195251 St. Petersburg, Russia
Alexander Leksashov: Graduate School of Intelligent Systems and Supercomputing Technologies, Peter the Great St. Petersburg Polytechnic University (SPbPU), 29 Polytechnicheskaya Street, 195251 St. Petersburg, Russia
Svetlana Shirokova: Graduate School of Business Engineering, Peter the Great St. Petersburg Polytechnic University (SPbPU), 29 Polytechnicheskaya Street, 195251 St. Petersburg, Russia

Sustainability, 2025, vol. 17, issue 12, 1-22

Abstract: Modern logistics requires effective solutions for the optimization of intermodal transportation, providing cost reduction and improvement of transport flows. This paper proposes a multi-objective optimization method for intermodal freight transportation planning within the framework of sustainable service network design. The approach aims to balance economic efficiency and environmental sustainability by minimizing both transportation costs and delivery time. A bi-criteria mathematical model is developed and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which is well-suited for handling complex, large-scale optimization problems under multiple constraints. The aim of the study is to develop and implement this technology that balances economic efficiency, environmental sustainability and manageability of operational processes. The research includes the development of a two-criteria model that takes into account both temporal and economic parameters of the routes. The optimization method employs the NSGA-III, a well-known metaheuristic that generates a diverse set of near-optimal Pareto-efficient solutions. This enables the selection of trade-off alternatives depending on the decision-maker’s preferences and specific operational constraints. Simulation results show that the implementation of the proposed technology can reduce the costs of intermodal operators by an average of 8% and the duration of transportation by up to 50% compared to traditional planning methods. In addition, the automation of the process contributes to a more rational use of resources, reducing carbon emissions and increasing the sustainability of transportation networks. This approach is in line with the principles of sustainable economic development, as it improves the efficiency of logistics operations, reduces pressure on infrastructure and minimizes the environmental impact of the transport sector. Route optimization and digitalization of transport processes can increase resource efficiency, improve freight flow management and contribute to the long-term stability of transport systems. The developed technology of automated planning of intermodal transportation is oriented to application in large-scale production systems, providing effective management of cargo flows within complex logistics chains. The proposed method supports the principles of sustainable development by providing a formal decision-making framework that balances transportation cost, delivery time and environmental objectives. Instead of optimizing for a single goal, the model enables the identification of efficient trade-offs between economic performance and ecological impact. Moreover, by generating multiple routing scenarios under varying operational constraints, the approach enhances the adaptability and robustness of freight transportation systems in dynamic and uncertain environments.

Keywords: intermodal freight transport; multi-objective optimization; NSGA-III; service network design; sustainable logistics; transportation planning; Pareto efficiency; carbon emissions reduction (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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