Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

Model and solution methods for some hub location problems

In this thesis we study some hub location problems in the context of transportation networks. These are combinatorial optimization problems appearing in situations where there is a need of transporting some traffic, like items, people, and information, from many origins to many destinations. Instead of sending these flows using a direct shipment between all pairs of nodes in the network, a subset of these nodes is selected to use as hubs, with the aim of consolidating and distribute the flows. Thus, hubs induce a subnetwork that sends the traffic more efficiently and at a cheaper cost, allowing economies of scale when large amounts of traffic between nodes on this subnet are transported. We study different variants of hub location problems that try to model several real world situations and characteristics. In all of them, we aim to minimize the cost of sending traffic through the transportation network.In this thesis we study some hub location problems in the context of transportation networks. These are combinatorial optimization problems appearing in situations where there is a need of transporting some traffic, like items, people, and information, from many origins to many destinations. Instead of sending these flows using a direct shipment between all pairs of nodes in the network, a subset of these nodes is selected to use as hubs, with the aim of consolidating and distribute the flows. Thus, hubs induce a subnetwork that sends the traffic more efficiently and at a cheaper cost, allowing economies of scale when large amounts of traffic between nodes on this subnet are transported. We study different variants of hub location problems that try to model several real world situations and characteristics. In all of them, we aim to minimize the cost of sending traffic through the transportation network

Solving the waste collection problem from a multiobjective perspective: New methodologies and case studies

Fecha de lectura Tesis Doctoral: 19 de marzo de 2018.Economía Aplicada ( Matemáticas) Resumen tesis: El tratamiento de residuos es un tema de estudio por parte de las administraciones locales a nivel mundial. Distintos factores han de tenerse en cuenta para realizar un servicio eficiente. En este trabajo se desarrolla una herramienta para analizar y resolver el problema de la recogida de residuos sólidos en Málaga. Tras un análisis exhaustivo de los datos, se aborda el problema real como un problema de rutas multiobjetivo con capacidad limitada. Para los problemas multiobjetivo, no suele existir una única solución óptima, sino un conjunto de soluciones eficientes de Pareto. Las características del problema hacen inviable su resolución de forma exacta, por lo que se aplican distintas estrategias metaheurísticas para obtener una buena aproximación. En particular, se combinan las técnicas de GRASP, Path Relinking y Variable Neighborhood Search, que son adaptadas a la perspectiva multicriterio. Se trata de una aproximación en dos fases: una primera aproximación de la frontera eficiente se genera mediante un GRASP multiobjetivo. Tres son los métodos propuestos para la primera aproximación, dos de ellos derivados de la publicación de Martí et al. (2015) y el último se apoya en la función escalarizada de logro de Wierzbicki (Wierzbicki, 1980) para distintas combinaciones de pesos. A continuación, esta aproximación es mejorada con una versión de Path Relinking o Variable Neighborhood Search, con un punto de referencia diseñado para problemas multiobjetivo. Una vez generada la aproximación de la frontera eficiente, el proceso de obtención de la solución que más se adecúa a las preferencias de los gestores se basa en el desarrollo de un método interactivo sin trade – off, derivado de la filosofía NAUTILUS (Miettinen et al. 2010). Para evitar gastos de cómputo extensos, esta metodología se apoya en una pre - computación de los elementos de la frontera eficiente

Solving the bi-objective capacitated p -median problem with multilevel capacities using compromise programming and VNS

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.A bi‐objective optimisation using a compromise programming (CP) approach is proposed for the capacitated p‐median problem (CPMP) in the presence of the fixed cost of opening facility and several possible capacities that can be used by potential facilities. As the sum of distances between customers and their facilities and the total fixed cost for opening facilities are important aspects, the model is proposed to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal location of open facilities with their optimal capacity. Two approaches are designed to deal with the bi‐objective CPMP, namely CP with an exact method and with a variable neighbourhood search (VNS) based matheuristic. New sets of generated instances are used to evaluate the performance of the proposed approaches. The computational experiments show that the proposed approaches produce interesting results

Logic learning and optimized drawing: two hard combinatorial problems

Nowadays, information extraction from large datasets is a recurring operation in countless fields of applications. The purpose leading this thesis is to ideally follow the data flow along its journey, describing some hard combinatorial problems that arise from two key processes, one consecutive to the other: information extraction and representation. The approaches here considered will focus mainly on metaheuristic algorithms, to address the need for fast and effective optimization methods. The problems studied include data extraction instances, as Supervised Learning in Logic Domains and the Max Cut-Clique Problem, as well as two different Graph Drawing Problems. Moreover, stemming from these main topics, other additional themes will be discussed, namely two different approaches to handle Information Variability in Combinatorial Optimization Problems (COPs), and Topology Optimization of lightweight concrete structures

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Optimised search heuristics: combining metaheuristics and exact methods to solve scheduling problems

Tese dout., Matemática, Investigação Operacional, Universidade do Algarve, 2009Scheduling problems have many real life applications, from automotive industry to air traffic control. These problems are defined by the need of processing a set of jobs on a shared set of resources. For most scheduling problems there is no known deterministic procedure that can solve them in polynomial time. This is the reason why researchers study methods that can provide a good solution in a reasonable amount of time. Much attention was given to the mathematical formulation of scheduling problems and the algebraic characterisation of the space of feasible solutions when exact algorithms were being developed; but exact methods proved inefficient to solve real sized instances. Local search based heuristics were developed that managed to quickly find good solutions, starting from feasible solutions produced by constructive heuristics. Local search algorithms have the disadvantage of stopping at the first local optimum they find when searching the feasible region. Research evolved to the design of metaheuristics, procedures that guide the search beyond the entrapment of local optima. Recently a new class of hybrid procedures, that combine local search based (meta) heuristics and exact algorithms of the operations research field, have been designed to find solutions for combinatorial optimisation problems, scheduling problems included. In this thesis we study the algebraic structure of scheduling problems; we address the existent hybrid procedures that combine exact methods with metaheuristics and produce a mapping of type of combination versus application and finally we develop new innovative metaheuristics and apply them to solve scheduling problems. These new methods developed include some combinatorial optimisation algorithms as components to guide the search in the solution space using the knowledge of the algebraic structure of the problem being solved. Namely we develop two new methods: a simple method that combines a GRASP procedure with a branch-and-bound algorithm; and a more elaborated procedure that combines the verification of the violation of valid inequalities with a tabu search. We focus on the job-shop scheduling problem

An iterated greedy heuristic for a market segmentation problem with multiple attributes

[EN] A real-world customer segmentation problem from a beverage distribution firm is addressed. The firm wants to partition a set of customers, who share geographical and marketing attributes, into segments according to certain requirements: (a) customers allocated to the same segment must have very similar attributes: type of contract, type of store and the average difference of purchase volume; and (b) compact segments are desired. The main reason for creating a partition with these features is because the firm wants to try different product marketing strategies. In this paper, a detailed attribute formulation and an iterated greedy heuristic that iteratively destroys and reconstructs a given partition are proposed. The initial partition is obtained by using a modified k-means algorithm that involves a GRASP philosophy to get the initial configuration of centers. The heuristic includes an improvement method that employs two local search procedures. Computational results and statistical analyses show the effectiveness of the proposed approach and its individual components. The proposed metaheuristic is also observed very competitive, faster, and more robust when compared to existing methods. (C) 2017 Elsevier B.V. All rights reserved.This research has been supported by the Mexican National Council for Science and Technology (CONACYT) through grants CB2005-01-48499Y and CB2011-01-166397, and a scholarship for graduate studies, and by the Universidad Autonoma de Nuevo Leon through its Scientific and Technological Research Support Program (PAICYT), grants CA1478-07, CE012-09, IT511-10, and CE331-15. Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds. We would like to thank Rafael Frinhani, Richard Fuchshuber, and their corresponding research teams for providing us the source code of their algorithms to carry out the corresponding tests. Furthermore, we are grateful to the editor and the four anonymous reviewers for their careful reading of our manuscript and their constructive comments and suggestions which helped us improve its quality.Huerta-Muñoz, D.; Ríos-Mercado, RZ.; Ruiz García, R. (2017). An iterated greedy heuristic for a market segmentation problem with multiple attributes. European Journal of Operational Research. 261(1):75-87. https://doi.org/10.1016/j.ejor.2017.02.013S7587261

Solving the bi-objective capacitated p-median problem with multilevel capacities using compromise programming and VNS

A bi-objective optimisation using a compromise programming (CP) approach is proposed for the capacitated p-median problem (CPMP) in the presence of the fixed cost of opening facility and several possible capacities that can be used by potential facilities. As the sum of distances between customers and their facilities and the total fixed cost for opening facilities are important aspects, the model is proposed to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal location of open facilities with their optimal capacity. Two approaches are designed to deal with the bi-objective CPMP, namely CP with an exact method and with a variable neighbourhood search (VNS) based matheuristic. New sets of generated instances are used to evaluate the performance of the proposed approaches. The computational experiments show that the proposed approaches produce interesting results