Solving the Task Assignment Problem with a Variable Neighborhood Search

In this paper a variable neighborhood search (VNS) approach for the task assignment problem (TAP) is considered. An appropriate neighborhood scheme along with a shaking operator and local search procedure are constructed specifically for this problem. The computational results are presented for the instances from the literature, and compared to optimal solutions obtained by the CPLEX solver and heuristic solutions generated by the genetic algorithm. It can be seen that the proposed VNS approach reaches all optimal solutions in a quite short amount of computational time.

Variable Neighborhood Search for the File Transfer Scheduling Problem

ACM Computing Classification System (1998): I.2.8, G.1.6.

A Variable Neighborhood Search Approach for Solving the Maximum Set Splitting Problem

This paper presents a Variable neighbourhood search (VNS) approach for solving the Maximum Set Splitting Problem (MSSP). The algorithm forms a system of neighborhoods based on changing the component for an increasing number of elements. An efficient local search procedure swaps the components of pairs of elements and yields a relatively short running time. Numerical experiments are performed on the instances known in the literature: minimum hitting set and Steiner triple systems. Computational results show that the proposed VNS achieves all optimal or best known solutions in short times. The experiments indicate that the VNS compares favorably with other methods previously used for solving the MSSP. ACM Computing Classification System (1998): I.2.8.

Variable Neighborhood Search for Solving the Capacitated Single Allocation Hub Location Problem

In this paper a Variable Neighborhood Search (VNS) algorithm for solving the Capacitated Single Allocation Hub Location Problem (CSAHLP) is presented. CSAHLP consists of two subproblems; the first is choosing a set of hubs from all nodes in a network, while the other comprises finding the optimal allocation of non-hubs to hubs when a set of hubs is already known. The VNS algorithm was used for the first subproblem, while the CPLEX solver was used for the second. Computational results demonstrate that the proposed algorithm has reached optimal solutions on all 20 test instances for which optimal solutions are known, and this in short computational time.

Discussion Paper: Triangulation of Methodology to Solve the Practitioner - Academic Debate Concerning the Value of Research

In support of research in the debate concerning its relevance to hospitality academics and practitioners, the author presents a discussion of how the philosophy of science impacts approaches to research, including a brief summary of empiricism, and the importance of the triangulation of research orientations. Criticism of research is the hospitality literature often focuses on the lack of an apparent philosophy of science perspective and how this perspective impacts the way in which scholars conduct and interpret research. The Validity Network Schema (VNS) presents a triangulation model for evaluating research progress in a discipline by providing a mechanism for integrating academic and practitioner research studies.

Estratégias de busca reativa utilizando aprendizagem por reforço e algoritmos de busca local

Técnicas de otimização conhecidas como as metaheurísticas tem conseguido resolversatisfatoriamente problemas conhecidos, mas desenvolvimento das metaheurísticas écaracterizado por escolha de parâmetros para sua execução, na qual a opção apropriadadestes parâmetros (valores). Onde o ajuste de parâmetro é essencial testa-se os parâmetrosaté que resultados viáveis sejam obtidos, normalmente feita pelo desenvolvedor que estaimplementando a metaheuristica. A qualidade dos resultados de uma instância1 de testenão será transferida para outras instâncias a serem testadas e seu feedback pode requererum processo lento de “tentativa e erro” onde o algoritmo têm que ser ajustado para umaaplicação especifica. Diante deste contexto das metaheurísticas surgiu a Busca Reativaque defende a integração entre o aprendizado de máquina dentro de buscas heurísticaspara solucionar problemas de otimização complexos. A partir da integração que a BuscaReativa propõe entre o aprendizado de máquina e as metaheurísticas, surgiu a ideia dese colocar a Aprendizagem por Reforço mais especificamente o algoritmo Q-learning deforma reativa, para selecionar qual busca local é a mais indicada em determinado instanteda busca, para suceder uma outra busca local que não pode mais melhorar a soluçãocorrente na metaheurística VNS. Assim, neste trabalho propomos uma implementação reativa,utilizando aprendizado por reforço para o auto-tuning do algoritmo implementado,aplicado ao problema do caixeiro viajante simétrico e ao problema escalonamento sondaspara manutenção de poços.

Formulações e algoritmos para o problema das p-medianas heterogêneo livre de penalidade

This work presents a new model for the Heterogeneous p-median Problem (HPM), proposed to recover the hidden category structures present in the data provided by a sorting task procedure, a popular approach to understand heterogeneous individual’s perception of products and brands. This new model is named as the Penalty-free Heterogeneous p-median Problem (PFHPM), a single-objective version of the original problem, the HPM. The main parameter in the HPM is also eliminated, the penalty factor. It is responsible for the weighting of the objective function terms. The adjusting of this parameter controls the way that the model recovers the hidden category structures present in data, and depends on a broad knowledge of the problem. Additionally, two complementary formulations for the PFHPM are shown, both mixed integer linear programming problems. From these additional formulations lower-bounds were obtained for the PFHPM. These values were used to validate a specialized Variable Neighborhood Search (VNS) algorithm, proposed to solve the PFHPM. This algorithm provided good quality solutions for the PFHPM, solving artificial generated instances from a Monte Carlo Simulation and real data instances, even with limited computational resources. Statistical analyses presented in this work suggest that the new algorithm and model, the PFHPM, can recover more accurately the original category structures related to heterogeneous individual’s perceptions than the original model and algorithm, the HPM. Finally, an illustrative application of the PFHPM is presented, as well as some insights about some new possibilities for it, extending the new model to fuzzy environments

Problema del agente viajero selectivo con restricciones adicionales

En esta tesis se introduce una variante del Problema del Agente Viajero Selectivo, también conocido en la literatura como Orienteering Problem (OP). En el OP se tiene un conjunto de clientes potenciales, a cada uno de los cuales se le asocia una puntuación o beneficio que recibe el agente al visitarlo, el objetivo es el de diseñar una ruta que comience y termine en el depósito y que maximice el puntaje colectado, tomando en cuenta que existe un límite máximo en la duración de la ruta. En este trabajo se consideran restricciones de conflictos entre clientes, es decir, si dos de ellos tienen conflicto, no pueden ser incluidos ambos en la ruta; por otra parte, existe un subconjunto de clientes que deben ser visitados de manera obligatoria. Se proponen dos modelos matemáticos del problema, cuya diferencia principal es la manera en que aborda la eliminación de ciclos. El primer modelo usa restricciones de tipo secuencial inspiradas en las propuestas por Miller et al. (1960) y el segundo utiliza restricciones basadas en flujo de múltiples productos y se basan en las restricciones propuestas por Wong (1980) y Claus (1984). Asimismo, se proponen dos algoritmos para la solución del problema planteado, el primero es de tipo heurístico y está basado en un esquema GRASP (Greedy Randomized Adaptive Search Procedure) reactivo, cuya fase de mejora es un método tipo VNS (Variable Neighborhood Search) general, el segundo es una estrategia de descomposición basada en generación de columnas. El desempeño de los algoritmos propuestos es evaluado a través de experimentos computacionales sobre un gran conjunto de instancias y los resultados obtenidos son comparados contra las soluciones ´optimas obtenidas al resolver los modelos matemáticos haciendo uso del solver Cplex 12.6.