Load Optimization with Shortest Distance Approach

Yusria, Lenitasari and Tri Basuki, Kurniawan and Edi Surya, Negara and Tata, Sutabri and Misinem, . (2022) Load Optimization with Shortest Distance Approach. Journal of Data Science, 2022 (21). pp. 1-13. ISSN 2805-5160

[img] Text
Vol.2022_21.pdf - Published Version
Download (275kB)
Official URL: http://ipublishing.intimal.edu.my/jods.html

Abstract

The most beneficial results or values are produced through an optimization technique. Load optimization is a problem that the logistics sector faces, despite the fact that there are many other optimization-related concerns. This problem has a connection to the knapsack problem, which is the combination of the number of items that can fit into a container with a capacity when one set of items has both weight and volume. The following problem is referred to as "Bin packing," which is an optimization problem in which objects of various sizes must be packed into a finite number of bins or containers, each of which has a specific capacity, while utilizing the fewest number of bins. By merging these two issues, the best payload value will be produced. In order to optimize the volume and weight of product preparation and arrangement based on delivery destinations (in terms of distance) on previously operational vehicles, a program will simulate the combination of these concerns. The initial load item carried by the original driver was compared in this study using experimental data to the outcomes of the load optimization approach. Results after the shortest distance approach's improvement were compared to the results obtained before. According to the comparison, the shortest-distance approach led to better outcomes.

Item Type: Article
Uncontrolled Keywords: Load Optimization, Shortest Distance, Bin-Packing, Knapsack Problem, Artificial Intelligence
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Depositing User: Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited: 27 Dec 2022 02:21
Last Modified: 27 Dec 2022 02:21
URI: http://eprints.intimal.edu.my/id/eprint/1696

Actions (login required)

View Item View Item
INTI Institutional Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.