10.57647/j.ijic.2025.1602.07

A Multi Objective Data-Driven Chemical-Aware Distribution Network Design Model Under Uncertainty (Case Problem: Bonakchi)

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  • Mohammad Amirahmadi1,
  • Hamid Esmaeeli*1,
  • Kia Parsa1,
  • Amin Mostafaee2,
  1. Department of Industrial Engineering, NT.C., Islamic Azad University, Tehran, Iran
  2. Department of Mathematics, NT.C., Islamic Azad University, Tehran, Iran

Received: 2025-03-10

Revised: 2025-08-22

Accepted: 2025-09-11

Published in Issue 2025-09-30

Copyright (c) 2025 Mohammad Amirahmadi, Hamid Esmaeeli, Kia Parsa, Amin Mostafaee (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Amirahmadi, M., Esmaeeli, H., Parsa, K., & Mostafaee, A. (2025). A Multi Objective Data-Driven Chemical-Aware Distribution Network Design Model Under Uncertainty (Case Problem: Bonakchi). International Journal of Industrial Chemistry, 16(3). https://doi.org/10.57647/j.ijic.2025.1602.07
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Abstract

In this research, we aim to address the gaps in past studies by presenting a data-driven network
design model for the distribution of edible oils, a critical sector with unique chemical and
logistical challenges. This model begins with a thorough analysis of customer demand and the specific requirements of edible oils (e.g., perishability, storage conditions, and transportation constraints) using data mining and machine learning tools. Based on the insights gained from analyzing customer behavior, the demand amounts across different geographical areas and their changing patterns will be identified and used as inputs for the network design model. For this purpose, the KNN method will be employed for data classification and analysis, and customer demand will be estimated for network design using new dimensions. Subsequently, taking into account real-world constraints and obstacles, a new mathematical model will be developed with environmental considerations. It is worth noting that during the modeling phase, in addition to optimizing the number, location, and capacity of facilities and flow in the network, the optimization of fleet type and its composition will also be addressed. Finally, to solve the model, the multi-objective nature of the problem will first be addressed using the e-constraint method. Then, considering the model's dimensions and complexities, an appropriate solution method will be proposed, and the model will be solved using real data from the Bonakchi company, extracting management insights.

Keywords

  • Chemical-Aware Distribution Design,
  • Data-mining,
  • E-constraint method,
  • Uncertainty,
  • Demand management
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