Optimization for routing vehicles of seafood product transportation

10th International Seminar on Industrial Engineering and Management "Sustainable Development In Industry and Management"

Soenandi, I.A.^1 ; Juan, Y.^1 ; Budi, M.^1

Department of Industrial Engineering Krida Wacana, Christian University, Tanjung Duren Raya No. 4, West Jakarta

11470, Indonesia^1

关键词: Ant Colony Optimization (ACO);    Marine products;    Routing problems;    Scientific method;    Seafood products;    Stochastic vehicle routing;    Time constraints;    Travel time data;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/277/1/012048/pdf
DOI  :  10.1088/1757-899X/277/1/012048

来源: IOP

【 摘 要 】

Recently, increasing usage of marine products is creating new challenges for businesses of marine products in terms of transportation that used to carry the marine products like seafood to the main warehouse. This can be a problem if the carrier fleet is limited, and there are time constraints in terms of the freshness of the marine product. There are many ways to solve this problem, including the optimization of routing vehicles. In this study, this strategy is to implement in the marine product business in Indonesia with such an expected arrangement of the company to optimize routing problem in transportation with time and capacity windows. Until now, the company has not used the scientific method to manage the routing of their vehicle from warehouse to the location of marine products source. This study will solve a stochastic Vehicle Routing Problems (VRP) with time and capacity windows by using the comparison of six methods and looking the best results for the optimization, in this situation the company could choose the best method, in accordance with the existing condition. In this research, we compared the optimization with another method such as branch and bound, dynamic programming and Ant Colony Optimization (ACO). Finally, we get the best result after running ACO algorithm with existing travel time data. With ACO algorithm was able to reduce vehicle travel time by 3189.65 minutes, which is about 23% less than existing and based on consideration of the constraints of time within 2 days (including rest time for the driver) using 28 tons capacity of truck and the companies need two units of vehicles for transportation.