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Optimizing Multimodal Transportation Systems Using the Teaching–Learning-Based Algorithm

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Abstract

Multimodal transportation systems (MTS) represent a cornerstone of modern logistics and transportation planning. At its core, MTS involves orchestrating the movement of goods through various transportation modes such as rail, road, air, and sea. The inherent challenges of MTS arise from the need to seamlessly integrate these modes to ensure timely and cost-effective transport. Decision-makers are often confronted with dilemmas such as determining the best mode transitions, minimizing transshipment costs, and ensuring timely deliveries amidst varying mode-specific constraints. These complexities necessitate advanced optimization techniques to find effective solutions. In response to these challenges, this article delves into the application of the Teaching–Learning-Based Optimization (TLBO) algorithm to unravel the intricate challenges of multimodal transportation systems (MTS). In our exploration, the TLBO algorithm emerges as a groundbreaking method. We detail the TLBO process wherein multiple MTS solutions, stemming from varied transport mode combinations, are evaluated on their cost metrics. Adopting a systematic pairing, where a more efficient “teacher” guides a less efficient “learner,” the TLBO algorithm iteratively refines its search for the optimal solution. Two illustrative numerical examples demonstrate the robustness of the TLBO approach. Moreover, for a comprehensive understanding, we juxtaposed the TLBO results against those obtained using the genetic algorithm on the same numerical problems. The comparative analysis revealed that the genetic algorithm yielded higher costs, accentuating the superiority and potential of the TLBO in optimizing MTS challenges.

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Acknowledgements

The second author acknowledges receiving financial assistance from the University Grants Commission (UGC).

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  1. Department of Mathematics, Chaudhary Charan Singh University, Meerut, U.P., 250004, India

    Tanisha Brar, Tarun Kumar & M. K. Sharma

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  1. Tanisha Brar
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  2. Tarun Kumar
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All authors whose names appear on the submission made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; drafted the work or revised it critically for important intellectual content; approved the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to M. K. Sharma.

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Brar, T., Kumar, T. & Sharma, M.K. Optimizing Multimodal Transportation Systems Using the Teaching–Learning-Based Algorithm. Int. J. Appl. Comput. Math 10, 18 (2024). https://doi.org/10.1007/s40819-023-01655-8

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