Open AccessOptimised Genetic Algorithm Crossover and Mutation Stage for Vehicle Routing Problem Pick-Up and Delivery with Time Windows
Author(s) -
Muhammad Faisal Ibrahim,
F R Nurhakiki,
Dana Marsetiya Utama,
A A Rizaki
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1071/1/012025
Subject(s) - vehicle routing problem , crossover , genetic algorithm , point (geometry) , mathematical optimization , computer science , routing (electronic design automation) , operations research , mutation , service (business) , engineering , mathematics , computer network , artificial intelligence , business , biochemistry , chemistry , geometry , marketing , gene
Some problems happen in transportation and distribution. Vehicle Routing Problem (VRP) can be applied in some systems above. Deciding the optimal route for every vehicle will impact increasing economic interests and expected logistical planning results. This research will raise the problem of a shipping and logistics company. There are 45 branch offices with one main depot to serve a certain area that will be a transit point before all packages will be sent to the destination. The vehicle will depart from the depot to all branch offices to delivers and pick the package up at certain hours. In every route, planning should be considered to the amount of load when loading and unloading. Every vehicle has carrying capacity, and every branch office has various loading and unloading service time windows. Based on the problem’s description, this research was conducted to find the optimal solution in the Vehicle Routing Problem Pick-up and Delivery with Time Windows (VRPPDTW). An optimised genetic algorithm was developed to solve these problems by adjusting the crossover and mutation stages. The result informs that the route proposed from optimised genetic algorithms is better than the company’s existing route in all aspects. On the other hand, we carried out an analysis effect of the number of iterations on distance traveled, the number of penalties, and the fitness value. This algorithm can be applied in VRPPDTW and produces an optimal solution.