Abstract
In this paper, we address a multi-objective residential waste collection problem with an integrated territory planning and vehicle routing approach. Dividing the problem into territories enables drivers to carry out the same route every week so they get familiar with it and residents put out their bins at the appropriate time. Another benefit is to reduce the computation time for large problems, since the complex characteristics of the involved vehicle routing problem make it otherwise difficult to solve. There are three characteristics that are important for good territory planning: minimum overlap, minimum travel time, and balanced workload. The purpose of this paper is to investigate the influence these three objectives have on each other, since they might be contradictory. Moreover, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed for this specific problem which uses a K-means algorithm to generate the initial solution for territories. The results with the three objectives are shown to be useful for planners seeking to make informed decisions through the trade-off across different solutions with the Pareto frontiers provided. Moreover, the ALNS algorithm is shown to find good quality solutions in a reasonable computational time.
Highlights
In this paper, we address a multi-objective residential waste collection problem with an integrated territory planning and vehicle routing approach
The exact approach is analyzed based on Solomon’s instances and Adaptive Large Neighborhood Search (ALNS) is tested compared with the exact approach and a real-life case is presented
The Mixed Integer Quadratic Programming Problem and ALNS were implemented in Python and the exact solution obtained by Gurobi optimizer
Summary
The consideration of waste collection problems has are two main focus points: developing algorithms to solve the optimization problem; and capturing the reality as much as possible using realistic data and constraints (2). According to Ombuki-Berman et al (26), a solution vector u = 1⁄2x1, x2, :::, xn is said to dominate another vector v = 1⁄2y1, y2, :::, yn if and only if: 8i 2 1⁄21, :::n : ui ł vi and 9i 2 1⁄21, :::, n : ui\vi: In this research, such a solution vector u would look like 1⁄2tu, ou, wu, in which tu represents the travel time, ou the overlap and wu the workload balance value This results in solutions in which one of the objectives might decrease, while the others increase. To make sure solutions are feasible, capacity constraints of the vehicle and maximum routing times are taken into account when reinserting the nodes.
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