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Non-dominated sorting genetic algorithm III with stochastic matrix-based population to solve multi-objective solid transportation problem

  • Optimization
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Abstract

The transportation problems, which consist of multiple objectives with heterogeneous conveyances, are the pragmatic representation of the transportation occurring in the real world. However, situations do exist where the solutions obtained by the classical optimization techniques do not reflect the acumen of the decision-maker. Against this backdrop, a systematic algorithm is proposed in this paper to generate an initial population to tackle the multi-objective solid transportation problem efficiently. In addition, the non-dominated sorting genetic algorithm (NSGA) III is carried out to acquire solutions that can demonstrate the varying degrees of objectives. Furthermore, two problems of different sizes are framed, and their performance is compared via NSGA II, hybrid genetic algorithm, and fuzzy programming technique.

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Authors and Affiliations

  1. Department of Applied Mathematics and Humanities, S.V. National Institute of Technology, Surat, (Gujarat), 395 007, India

    Shubha Agnihotri & Jayesh M. Dhodiya

Authors
  1. Shubha Agnihotri
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  2. Jayesh M. Dhodiya
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Contributions

Jayesh M. Dhodiya has proposed solving MOSTP by NSGA III, and Shubha Agnihotri has developed the algorithm to generate population in view to apply NSGA III and is responsible for the writing of the paper.

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Correspondence to Shubha Agnihotri.

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None of the authors performed experiments with human participants or animals to carry out this research work.

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Shubha Agnihotri and Jayesh M. Dhodiya agree to submit this version and state that no part of the paper has been previously published or submitted. We thank you for taking the time to consider our work, and we eagerly await the reviewers’ feedback as soon as possible.

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Agnihotri, S., Dhodiya, J.M. Non-dominated sorting genetic algorithm III with stochastic matrix-based population to solve multi-objective solid transportation problem. Soft Comput 27, 5641–5662 (2023). https://doi.org/10.1007/s00500-022-07646-z

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