A Genetic Algorithm Approach for Waste Collection Using Multi-trip Multi-period Capacitated Vehicle Routing Problem with Time Windows (MCVRPTW)

Abstract

Waste collection and transportation processes are accounted for more than 50% of all total waste management costs then there is an urgency for a waste recycling company to minimize this activities-related total cost by any means. Route optimization in collecting those plastics wastes can be a good solution to addressing the problem. This research focuses on determining waste collection routes for multilayer plastic with the objective function of minimizing total transportation costs. The waste collection problem is modeled as Vehicle Routing Problem (VRP) with a certain capacity and constrained time windows but here the vehicle is allowed to travel multiple times, this model said as the Multi-Trip Multi-period Capacitated Vehicle Routing Problem with Time Windows (MCVRPTW). The model was solved by an exact method using mixed integer linear programming and a metaheuristic approach using the Genetic Algorithm (GA). The results showed that the exact method was able to solve problems on small data instances and required almost 100% higher computation time than of metaheuristic approach. The best GA solution with mutation and crossover rates of 0.75 and 0.1 provides savings of 30.2% compared to the total transportation cost for existing conditions.

GreenRoute Ballari: A Hybrid Approach for Optimizing Waste Collection Routes Using K-Means Clustering and Genetic Algorithm

“…Its objective is to minimize the distance traveled by carrier vehicle which in turn minimizes fuel consumption and maximize waste collection by utilizing historical data and predictive algorithms. [5] Interactions of residential waste composition and collection truck compartment design on GIS route optimization Hoang Lan Vu, Kelvin Tsun, Wai Ng, Bahareh Fallah, Amy Richter, Golam Kabir Waste density and collection frequency significantly impact travel distances and time in waste collection. Increasing truck capacity and using dual-compartment trucks can lead to substantial savings.…”

“…Traditional methods often rely on static routes, leading to inefficiencies such as unnecessary travel distances, increased fuel consumption, and higher operational costs [4]. While GIS-based route optimization offers promise [2], a deeper understanding of how factors like waste composition, collection frequency, and truck design interact with each other is essential for even greater optimization [3,5]. The GreenRoute Ballari proposes a novel system to address these challenges by leveraging machine learning and advanced route optimization techniques.…”