Optimasi Capacitated Vehicle Routing Problem with Pickup and Delivery (CVRPPD) Menggunakan Pendekatan Metaheuristik dengan Mempertimbangkan Cuaca dan Kepadatan Lalu Lintas

Abstract

This study aims to develop and evaluate the Adaptive Heuristic-Based Ant Colony Optimization (AHB-ACO) algorithm as a dynamic optimization solution in the Capacitated Vehicle Routing Problem with Pickup and Delivery (CVRPPD). The AHB-ACO algorithm was developed in response to changes in external variables, such as traffic congestion and weather conditions, which often affect the travel time and distance of logistics routes in big cities. AHB-ACO is designed to generate more adaptive and efficient routes by taking into account penalties based on traffic and weather conditions. Through a series of computer-based simulations, this study compares the performance of AHB-ACO with the traditional ACO algorithm, and finds that AHB-ACO is able to reduce travel distance and time significantly. The results show that AHB-ACO is superior in avoiding routes that are predicted to experience congestion or bad weather, and is able to provide more flexible and efficient solutions. Thus, this study contributes to the development of adaptive optimization algorithms, opening up opportunities for application in logistics systems that require real-time route adjustments. The implementation of this algorithm is expected to improve operational efficiency in the dynamic logistics sector.