Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

Researchers who investigate in any area related to computational algorithms (both dening new algorithms or improving existing ones) usually nd large diculties to test their work. Comparisons among dierent researches in this eld are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results. On many occasions, the replication of the work conducted by other researchers is required, which leads to a waste of time and a delay in the research advances. The authors of this study propose a procedure to introduce new techniques and their results in the eld of routing problems. In this paper this procedure is detailed, and a set of good practices to follow are deeply described. It is noteworthy that this procedure can be applied to any combinatorial optimization problem. Anyway, the literature of this study is focused on routing problems. This eld has been chosen because of its importance in real world, and its relevance in the actual literature

Crossover vs. Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

Since their first formulation, genetic algorithms (GA) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GA is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test

Introducción al problema de enrutamiento de vehículos en la logística de distribución

Este libro presenta un modelo matemático flexible y una metodología metaheurística que puede ser adaptada a diferentes variantes del problema de enrutamiento. Este se basa en el problema clásico del agente viajero, travel salesman problem (TSP), donde se necesita definir una ruta que visite todos los nodos una única vez. En algunas aplicaciones no se permite que la longitud de ruta exceda un límite de tiempo o de distancia, lo que hace necesaria la confección de varias trayectorias o ciclos para visitar todo el conjunto de clientes, esto configura el problema de múltiples agentes viajeros, multitravel salesman problem (M-TSP). En ambos problemas, el objetivo es visitar todos los clientes, minimizando la sumatoria de los arcos activos

LOCATION-ALLOCATION-ROUTING APPROACH TO SOLID WASTE COLLECTION AND DISPOSAL

Various studies have indicated that the collection phase of solid wastes, which comprises of the initial col- lection at the source of generation and the transportation to the disposal sites, is by far the most expensive. Two fundamental issues of concern in solid waste collection are the locations of initial collection and the period of collection by the dedicated vehicles. However, considering the prevailing conditions of adhoc lo- cation of waste containers and the faulty roads in many developing countries, this research was conducted to develop two e�ective models for solid waste collection and disposal such that new parameters measuring the capacity of waste ow from each source unit and road accessibility were introduced and incorporated in the mathematical formulations of the models. To formulate the problems, two classes of integer pro- gramming problems namely, Facility Location Problem (FLP) and the Vehicle Routing Problem (VRP), were used for the collection and disposal respectively. The clustering process involved in the model for the collection phase was based on the Euclidean distance relationship among the various entities within the study area. In this model, the study area was considered as a universal set and simply partitioned with each element representing a cluster. At this stage, a threshold distance was de�ned as the maximum allowable distance between a cluster and the potential collection sites. In the VRP formulation of the disposal model, two new parameters, called the accessibility ratio and road attribute, were introduced and included in the formulation. The inclusion of these parameters ensure that a waste collection vehicle uses only roads with high attributes. The solution to the model on the collection phase was based on the Lagrangian re- laxation of the set of constraints where decision variables are linked, while in the model on waste vehicle routing, the assignment constraints were relaxed. Both resulting Lagrangian dual problems were solved using sub-gradient optimization algorithm. It was shown that the resulting Lagrangian dual functions were non-di�erentiable concave functions and thus the application of the sub-gradient optimization method was justi�ed. By applying these techniques, strong lower bounds on the optimal values of the decision variables were obtained. All model implementations were based on randomly generated data that mimic real-life experience of the study area (Eti-Osa Local Government Area of Lagos State, Nigeria), as well as large-scale standard benchmark data instances in literature. These computational experiments were carried out using the CPLEX and MINOS optimization solvers on AIMMS and AMPL modeling environments. Results from the computational experiments revealed that the models are capable of addressing the challenge of solid waste collection and disposal. For instance, more than 60% reductions were obtained for the number of collection points to be activated and the container allocations for the different wastes considered. Numerical results from the disposal model showed that there is a general reduction in the total distance covered by a vehicle and a slight improvement in the number of customers visited. Result comparison with those found in literature suggested that our models are very efficient