Solving the Team Orienteering Problem: Developing a Solution Tool Using a Genetic Algorithm Approach

Abstract

Nowadays, the collection of separated solid waste for recycling is still an expensive process, specially when performed in large-scale. One main problem resides in fleet-management, since the currently applied strategies usually have low efficiency. The waste collection process can be modelled as a vehicle routing problem, in particular as a Team Orienteering Problem (TOP). In the TOP, a vehicle fleet is assigned to visit a set of customers, while executing optimized routes that maximize total profit and minimize resources needed. The objective of this work is to optimize the waste collection process while addressing the specific issues around fleet-management. This should be achieved by developing a software tool that implements a genetic algorithm to solve the TOP. We were able to accomplish the proposed task, as our computational tests have produced some challenging results in comparison to previous work around this subject of study. Specifically, our results attained 60% of the best known scores in a selection of 24 TOP benchmark instances, with an average error of 18.7 in the remaining instances. The usage of a genetic algorithm to solve the TOP proved to be an efficient method by outputting good results in an acceptable time.

A Multi-Criteria Decision Support System for a Routing Problem in Waste Collection

“…Other algorithms followed, as well as new variants of the TOP and CTOP. Promising algorithms based on other methodologies are also found in the literature of routing problems, such as genetic algorithms [15,16,27,28,36,37] and cellular genetic algorithms [1].…”

Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm

“…At present, there are many heuristic algorithms used to solve the TOP. Among them, the GA has been proven to be an effective heuristic algorithm for solving the TOP [12]. It is very effective in solving standard benchmark instances and can obtain better results by adjusting the corresponding parameter configuration.…”

“…Through analyzing the results of solving 24 standard TOP benchmark instances using a heuristic algorithm, Ferreira et al believed that the GA's results for 60% of the benchmark instances were better than those from other heuristic algorithms [12] and proved that using GA to solve the TOP within the acceptable time can produce good results. When solving the OP with time windows [52] or OP with stochastic profits [47], the GA results have advantages over those from other heuristic algorithms.…”

An efficient evolutionary algorithm for the orienteering problem

“…There are several Evolutionary Algorithms (EA) proposed in the literature to solve OP and TOP [5,9,17,18,23], among others. In all of these approaches the solutions are initialized with constructive methods which add a new node to the route while the distance limitation constraint is satisfied and codified based on the visiting sequence of nodes.…”

“…Particularly, all of them use an adaptation of the single-point crossover or its generalization, the n-point crossover. Approaches [5,9,17] and [23], have been tested in the benchmark instances proposed by [7] (40 instances involving up to 66 nodes) and [23] (49 instances involving up to 33 nodes). Approach [18] has been tested in 90 TSPLib-based instances and 15 VRP-based instances proposed by [10].…”