Overview Discourse on Inherent Distinction of Multiobjective Optimization in Routing Heuristics for Multi-Depot Vehicle Instances


  • Farid Morsidi Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, Malaysia https://orcid.org/0000-0001-9812-1078
  • Shir Li Wang Data Intelligent and Knowledge Management, Universiti Pendidikan Sultan Idris, Malaysia https://orcid.org/0000-0003-4417-3213
  • Haldi Budiman Fakultas Teknologi Informasi, Universitas Islam Kalimantan Muhammad Arsyad Al-Banjar, Indonesia https://orcid.org/0000-0003-4369-4922
  • Theam Foo Ng Centre for Global Sustainability Studies, Universiti Sains Malaysia, Malaysia




vehicle routing problem, multi-depot VRP, routing heuristics, scheduling routing, logistics problem


This paper reviews the research methodologies used in earlier years on the benefits and traits reflected by multi-depot vehicle routing problem (MDVRP) instances and assesses the efficacy of various improvised techniques to improve the current recurrent problems in routing procedures. Management of logistics involves moving finished goods from depots to end-user clients. Routing and scheduling systems that are improved will be able to serve a more significant number of customers in a shorter amount of time while also increasing customer satisfaction. To thoroughly discuss the current state of MDVRP implementation in routing heuristics, an analysis of the selected approaches involving multi-depot task distribution under VRP incorporations is further extrapolated. These approaches address the most common routing issues involving constraints like cost optimality, time window impositions, and load capacity flexibility. Recent research focuses on the advantages, proficiency, problem magnitude, and adaptability in MDVRP. The MDVRP framework can still be significantly improved by reducing routing costs with efficient heuristics to generate optimized solutions.


Received: 27 February 2023 | Revised: 11 April 2023 | Accepted: 19 April 2023


Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.


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How to Cite

Morsidi, F. ., Wang, S. L., Budiman, H. ., & Ng, T. F. (2023). Overview Discourse on Inherent Distinction of Multiobjective Optimization in Routing Heuristics for Multi-Depot Vehicle Instances. Artificial Intelligence and Applications, 2(3), 179–187. https://doi.org/10.47852/bonviewAIA3202806