Multi-depot dispatch deployment analysis on classifying preparedness phase for flood-prone coastal demography in Sarawak

Authors

  • Farid Morsidi Universiti Pendidikan Sultan Idris

DOI:

https://doi.org/10.37134/jictie.vol9.2.13.2022

Keywords:

vehicle routing problem, MDVRP, genetic computation, optimization, routing complexity

Abstract

Multi-Depot VRP (MDVRP) is a metaheuristic approach with concurrent vehicle rendezvous across various depots within a demanded regulation, where the task assignment would eventually end up at the same initial depot. A review of the relief commodities distribution patterns among flood-prone areas in the underlying layouts of Sarawak residential areas has been conducted in retrospect and in light of common real-world routing problems.  The purpose of this research is to demonstrate the benefits of multi-path route selection in task distribution to cater to simultaneous demands for adhering to strict constraint settings, including load dispatch dynamism and deployed vehicle quantities. Shortest path algorithms are improvised as an alternative to select the most optimum traveled routes during relief commodity distribution. This is done by determining critical allocation nodes, where solution steps are optimized using a genetic algorithm with predefined parameters.  The experimental output displays the strong correlation between the number of prioritized customers and assigned depots to optimize the route complexity and natural affluence on generated final solution cost.  The approach is seen as viable for further addressing problem-specific instances in vehicle routing problems such as adjusting parameter settings to generate rapid solution steps, including pathfinding shortest coverage distance and sorting out trade-offs between space covered and the time limitations of task distribution efforts.

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Published

2022-12-10

How to Cite

Morsidi, F. (2022). Multi-depot dispatch deployment analysis on classifying preparedness phase for flood-prone coastal demography in Sarawak. Journal of ICT in Education, 9(2), 175–190. https://doi.org/10.37134/jictie.vol9.2.13.2022

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