Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

Son Tung Ngo
* ORCID logo ,
Jafreezal Jaafar
,
Izzatdin Abdul Aziz
ORCID logo ,
Muhammad Umar Aftab
* ORCID logo ,
Giang Hoang Nguyen
,
Ngoc Anh Bui
ORCID logo
Version 1 : Received: 31 December 2021 / Approved: 10 January 2022 / Online: 10 January 2022 (18:38:55 CET)

A peer-reviewed article of this Preprint also exists.

Ngo, T.S.; Jaafar, J.; Aziz, I.A.; Aftab, M.U.; Nguyen, H.G.; Bui, N.A. Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem. Entropy 2022, 24, 388. Ngo, T.S.; Jaafar, J.; Aziz, I.A.; Aftab, M.U.; Nguyen, H.G.; Bui, N.A. Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem. Entropy 2022, 24, 388.

Abstract

The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers' expectations, shipper considerations as goals, and the common goal like transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the Genetic algorithm with the Local Search algorithm to solve the proposed problem. We evaluate the effectiveness of the proposed algorithm with the Tabu Search algorithm, the original Genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

Keywords

multi objective optimization; VRP; compromise programming; genetic algorithm; tabu search; local search; metaheuristics; combinatorial optimization

Subject

Computer Science and Mathematics, Data Structures, Algorithms and Complexity

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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