Combined Nearest Greedy Algorithm With Randomized Iterated Greedy Algorithm to Solve Waste Collection Problem

The waste collection considers as one of the common transportation problems in the operation research and management area and also it is a significant activity in each city. In this kind of the paper, we study a version of a real-life waste collection and try to find an efficient way to reduce the costs such as the cost of the operation such as fuel and maintenances, the cost of the environment such as noise and traffic congestions, the cost of the investment such as vehicles fleet. The waste collection problem can be formulated as a well-known Vehicle Routing Problem (VRP). The basic idea is to attempt to develop a daily truck routing which will improve the efficiency of the vehicle distribution in Riyadh. The solution will be done in a good way that it can serve all the customers, while in the meantime, it will attempt to improve the total cost. The main contribution of this paper is to improve the solution of total costs for the waste collection while using the existing resources through the combination of the Nearest Greedy algorithm with both Iterated Greedy (IG) and Randomized Iterated Greedy (RIG). We execute our proposed method with real data that collect waste from more than 100 customers in Riyadh city. In terms of the experiments, the results received by those methods are successfully implemented and improved the overall waste collection in Riyadh. In conclusion, these algorithms able to reduce the total costs to this kind of case study with the same number of vehicles.