Optimizing relief truck loading using a multiple constrint knapsack model
Date
6-2024
Pagination
40 pages
Academic Strand
Science, Technology, Engineering, and Mathematics (STEM) Strand
Adviser
John Cedric Gaza
Principal
Buela, Mabel S.
Abstract
The Philippines, ranking as the world's most di saster-prone country according to the 2023 World Risk Index, faces significant challenges in disaster risk management, particularly in relief operations. Past relief operations have been hindered by issues such as oversupply, undersupply, and logistical inefficiencies, resulting in delayed and damaged relief goods. By modeling the truck loading problem with constraints on weight and volume, the study seeks to maximize the value of delivered goods. This research optimized the loading of relief trucks using a multiple constraint knapsack model, employing both greedy and genetic algorithms to enhance the efficiency of relief distributi on. In this study, the researchers devel oped an algorithm that ensures efficient delivery of essential relief items, minimizes costs, and improves decision-making in relief operations. The researchers found out that the genetic algorithm found a solution that contained more varied item combinations, while the greedy algorithm found a higher fitness value within a lower execution time. For future studies that want to use this study as a reference. the researchers recommend adding item variety to the objective function to get a more realistic solution. The expected outcome is a coding project for the SPARK11 program, demonstrating a practical application of optimization algorithms to improve disaster response efforts.
Language
English
LC Subject
Capstone
Location
UP Rural High School
Recommended Citation
Elepaño, Joshua B. and Huertas, Vijay Nikolai N., "Optimizing relief truck loading using a multiple constrint knapsack model" (2024). Capstones. 137.
https://www.ukdr.uplb.edu.ph/etd-capstone/137
Document Type
Capstone
