Designing a Fuzzy Mathematical Model for a Two-Echelon Allocation-Routing Problem by Applying Route Conditions: A New Interactive Fuzzy Approach

Zahra Yadegari; Seyyed Mohammad Hadji Molana; Ali Husseinzadeh Kashan; Seyyed Esmaeil Najafi

doi:10.31181/dmame7220241029

Authors

Zahra Yadegari Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0009-0003-2654-1980
Seyyed Mohammad Hadji Molana Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-3674-2626
Ali Husseinzadeh Kashan Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran https://orcid.org/0000-0002-6004-6882
Seyyed Esmaeil Najafi Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-8734-5436

DOI:

https://doi.org/10.31181/dmame7220241029

Keywords:

Two-echelon allocation-routing model, Reliability, Multi-objective optimization, Interactive fuzzy approach

Abstract

In vehicle routing problems (VRP), the optimal allocation of transportation by considering factors such as route hardness, driver experience and vehicle worn-out has a significant effect on costs reduction and approaching real-world conditions. In this paper, a novel fuzzy mixed integer non-linear mathematical model to address the two-echelon allocation-routing problem under uncertainty is proposed by applying route and fleet conditions. The cost of allocating drivers to diverse vehicles is computed at the first echelon of the problem, considering factors such as vehicle type, vehicle wear-out, and driver experience. Additionally, different routes are defused with varying levels of hardness. The goal of the second echelon of the model is to improve reliability by defining the reliability of routes within each section. To solve the model, the Torabi and Hessini (TH), the Selimi and Ozkarahan (SO) methods, and a newly proposed approach (PIA) were utilized to transform the multi-objective model into a single-objective one. Numerical tests and performance indicators were used to validate the effectiveness of both the multi-objective mathematical model and the proposed solution method. The validation computation results indicate that the proposed solution approach outperforms both the TH and SO approaches.

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