Sistem Optimalisasi Rute Model Capacitated Vehicle Routing Problem With Time Windows Menggunakan Algoritma Metaheuristic Particle Swarm Optimization pada Perusahaan Kantong Plastik HDPE PT XYZ

Jason Jason, Silvia Rostianingsih, Andreas Handojo

Abstract


Technology has been one of the key factors behind industrial revolution. Companies are now required to use technological assistance and data processing to produce faster and more efficient business processes. This is also the case with Company XYZ. Company XYZ is an HDPE plastic manufacturer domiciled in Surabaya. Currently, the company is trying to handle the increasing frequency of shipments that exist in the company. Due to the increasing frequency of shipments, the company is often overwhelmed in handling its shipments because there is no system that can quickly determine the shipping route for the company. Moreover, there are other route determining factors such as shipment weight, truck capacity, and special delivery hour requests that add to the complexity of the route to be calculated manually. So a system is needed that is able to provide route recommendations quickly. This route optimization system is designed using the PHP programming language and the Bootstrap frontend framework to support the system UI Design. The database used is mySQL database. The system will be created in 2 modules, namely a module for the admin and a module for the driver. For this system to work, firstly the system will run the KMeans Cluster function from the database to cluster all customers in the company. This cluster is one of the factors determining the fitness value in the Particle Swarm Optimization algorithm. After the order data is obtained, the system will use the PSO algorithm to determine the delivery agenda for each truck. The determining factors of PSO include customer location, priority hours of customer requests, order weight, and loading capacity of different types trucks. After obtaining the delivery table of each truck, the system will use the help of Google Waypoints API to determine the routing order from each truck. The final result of this system is a delivery route optimization system that is able to provide route selection recommendations for each truck in the company. The system is also able to sort shipments with various shipping priority restrictions. From the test results, the PSO algorithm in the system is able to produce routes with less total distance traveled and less travel duration than the routes generated manually by the employees in the company.

Keywords


Google Waypoints; Particle Swarm Optimization; Shipment

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