Maximizing the Supply Chain Profit in Multimodal Transportation Problem with Transfer Part using Two-Echelon Genetic Algorithm

Farhana Johar; Nur Shuwaibah Mohd Zawawi; Syarifah Zyurina Nordin

doi:10.11113/mjfas.v20n1.3184

Authors

Farhana Johar Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
Nur Shuwaibah Mohd Zawawi Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
Syarifah Zyurina Nordin Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.11113/mjfas.v20n1.3184

Keywords:

Multimodal Transportation, Two-echelon Genetic Algorithm, Supply Chain Profit

Abstract

Multimodal transportation is a highly effective method for optimizing deadlines and reducing inventory costs, both of which are crucial in a supply chain environment. This study employs a mathematical programming model to optimize the supply chain profit for multimodal transportation distribution within a specified time window. The model considers five factors, such as production cost, transportation cost, transport time, penalty cost, and sales price. Additionally, a Two-Echelon Genetic Algorithm (TEGA) is proposed to solve the optimization problem, and a numerical example is provided to validate the model and algorithm. The study compares the performance of the proposed algorithm with the exact solution from a previous study, presents implementation details and numerical experiment results, and analyses the findings. The results demonstrate the efficiency and robustness of the algorithm, making it a significant contribution to transportation planning for freight transportation and supply chain management.

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