Algorithms for Variants of Routing Problems

In this thesis, we propose mathematical optimization models and algorithms for variants of routing problems. The first contribution consists of models and algorithms for the Traveling Salesman Problem with Time-dependent Service times (TSP-TS). We propose a new Mixed Integer Programming model and develop a multi-operator genetic algorithm and two Branch-and-Cut methods, based on the proposed model. The algorithms are tested on benchmark symmetric and asymmetric instances from the literature, and compared with an existing approach, showing the effectiveness of the proposed algorithms. The second work concerns the Pollution Traveling Salesman Problem (PTSP). We present a Mixed Integer Programming model for the PTSP and two mataheuristic algorithms: an Iterated Local Search algorithm and a Multi-operator Genetic algorithm. We performed extensive computational experiments on benchmark instances. The last contribution considers a rich version of the Waste Collection Problem (WCP) with multiple depots and stochastic demands using Horizontal Cooperation strategies. We developed a hybrid algorithm combining metaheuristics with simulation. We tested the proposed algorithm on a set of large-sized WCP instances in non-cooperative scenarios and cooperative scenarios

Automatically Produced Algorithms for the Generalized Minimum Spanning Tree Problem

The generalized minimum spanning tree problem consists of finding a minimum cost spanning tree in an undirected graph for which the vertices are divided into clusters. Such spanning tree includes only one vertex from each cluster. Despite the diverse practical applications for this problem, the NP-hardness continues to be a computational challenge. Good quality solutions for some instances of the problem have been found by combining specific heuristics or by including them within a metaheuristic. However studied combinations correspond to a subset of all possible combinations. In this study a technique based on a genotype-phenotype genetic algorithm to automatically construct new algorithms for the problem, which contain combinations of heuristics, is presented. The produced algorithms are competitive in terms of the quality of the solution obtained. This emerges from the comparison of the performance with problem-specific heuristics and with metaheuristic approaches

Automatically Produced Algorithms for the Generalized Minimum Spanning Tree Problem

The generalized minimum spanning tree problem consists of finding a minimum cost spanning tree in an undirected graph for which the vertices are divided into clusters. Such spanning tree includes only one vertex from each cluster. Despite the diverse practical applications for this problem, the NP-hardness continues to be a computational challenge. Good quality solutions for some instances of the problem have been found by combining specific heuristics or by including them within a metaheuristic. However studied combinations correspond to a subset of all possible combinations. In this study a technique based on a genotype-phenotype genetic algorithm to automatically construct new algorithms for the problem, which contain combinations of heuristics, is presented. The produced algorithms are competitive in terms of the quality of the solution obtained. This emerges from the comparison of the performance with problem-specific heuristics and with metaheuristic approaches

Un algoritmo para el Strip Packing Problem obtenido mediante la extracción de habilidades de expertos usando minería de datos

ResumenLa capacidad del ser humano para resolver problemas NP-Duro de forma manual no ha recibido la debida atención por la comunidad científica. Este artículo considera el problema del Strip Packing, que consiste en posicionar ortogonalmente un conjunto de piezas rectangulares dentro de un contenedor de ancho fijo y altura infinita, sin solaparlas, minimizando la altura alcanzada de las piezas dentro del contenedor. Se desarrolló un juego computacional que permite obtener soluciones manuales, propuestas por jugadores expertos, para distintas instancias del problema. La contribución del artículo consiste en presentar un algoritmo que se extrajo mediante patrones y minería de datos aplicada a soluciones encontradas por los jugadores expertos. El algoritmo generado se basa en elementos de árboles y heurísticas presentes en la literatura. Adicionalmente se presentan resultados computacionales, donde se logra encontrar la mejor solución conocida en 94.3% de un conjunto de instancias de la literatura y 79% para instancias generadas aleatoriamente.AbstractThe ability of the humans to manually solve NP-hard problems had not received much attention of the scientific community. This paper considers the Strip Packing Problem (SPP), in which a set of rectangular pieces has to be placed orthogonally in a container with a given width and an infinite length. The pieces are not allowed to overlap (i.e. be stacked one over the other). The aim of the SPP is to minimize the overall length of the strip. In this paper, we have developed a computational game to allow manual solutions by expert gamers for different instances of the problem. The main contribution of the paper is the presentation of an algorithm based on patterns and data mining retrieved from the results achieved by expert gamers. The proposed algorithm is based on decision-trees and heuristics proposed in literature. Finally, the proposed approach is able to find the best-known solutions for the 94.3% of a set of instances proposed in the literature, and 79% for instances generated randomly

Open-pit pushback optimization by a parallel genetic algorithm

ABSTRACT: Determining the design of pushbacks in an open-pit mine is a key part of optimizing the economic value of the mining project and the operational feasibility of the mine. This problem requires balancing pushbacks that have good geometric properties to ensure the smooth operation of the mining equipment and so that the scheduling of extraction maximizes the economic value by providing early access to the rich parts of the deposit. However, because of the challenging nature of the problem, practical approaches for finding the best pushbacks strongly depend on the expert criteria to ensure good operational properties. This paper introduces the Advanced Geometrically Constrained Production Scheduling Problem to account for operational space constraints, modeled as truncated cones of extraction. To find the best solution for this problem, we present a parallel genetic algorithm based on a genotype–phenotype model such that the genotype symbolizes the base block of a truncated cone, and the phenotype represents the cone itself. A central computer node evaluates these solutions, collaborating with various secondary nodes that evolve a population of feasible solutions. The PGA’s efficacy was validated using comprehensive test instances from established research. The PGA solution exhibited a consistent average copper grade across periods, with its incremental phases reflecting real-world mine geometry. Moreover, the benefits of the MeanShift clustering technique were evident, suggesting effective phase-based scheduling. The PGA’s approach ensures optimal resource utilization and offers insights into potential avenues for further model enhancements and fine-tuning

Una aplicación web, para asignación y ruteo de vehículos en caso de desastres

The natural disasters are events that exceed the capacity of covering of a population and generate large losses, both economic and humans, with externalities in many cases not quantified in their entirety. The resources needed to supply the distribution centers are provided both private and government must allocate providers, by the disaster damage. Then, the distribution is performed from the depots, to the different customers or distribution centers. It presents a web application that assigns the super depots, and then establishes the routing that the vehicles must follow to cover the distribution centers, considering different probabilities of populations to be covered. The application is a parametric framework to any geographical area and scenarios, given the existing integration with applications such as Google Maps ®. Computational times are reasonable, and at the software architecture level the product is scalable and extensible. In addition, it complies with a set of good software quality practices present in ISO9126.Los desastres naturales, son eventos que exceden la capacidad de respuesta de una población y generan cuantiosas pérdidas, tanto económicas como humanas, con externalidades en muchos casos no cuantificadas en su totalidad. Los recursos necesarios para abastecer los centros de distribución son provistos tanto por proveedores privados como gubernamentales, deben ser asignados en virtud del daño del desastre. Luego viene la distribución desde los depósitos, hasta los distintos clientes o centros de distribución. Se presenta una aplicación web que asigna los superdepósitos, y luego establece el ruteo que han de seguir los vehículos para cubrir los centros de distribución, considerando diversas probabilidades de poblaciones que han de ser cubiertas. La aplicación es un marco de trabajo paramétrico a cualquier zona geográfica y escenarios, dado la integración existente con aplicaciones como Google Maps ®. Los tiempos computacionales son razonables, a nivel de arquitectura de software el producto es escalable y extensible. Además, cumple con un conjunto de buenas prácticas de calidad de software presentes en la ISO9126

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Automatically generated algorithms for the vertex coloring problem.

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark