A metaheuristic for the capacity-pricing problem in the car rental business

A atenção ao problema de capacidade-preço no aluguer de carros tem vindo a aumentar à medida que as empresas começaram a investir em ferramentas avançadas de apoio à decisão para essas questões críticas. Ao planear um período de vendas, uma empresa deve decidir o número e o tipo de veículos necessários na sua frota de forma a atender à procura. A procura pelos veículos para aluguer é altamente sensível ao preço e, portanto, as decisões de capacidade e preço estão intimamente ligadas. Além disso, como os produtos são alugados, a capacidade "volta". Isso cria uma ligação entre a capacidade, a mobilização da frota e outras ferramentas que permitem à empresa atender à procura, tal como upgrades, transferência de veículos entre locais ou aluguer temporário de veículos adicionais. O impacto da solução desse complexo problema no lucro de uma empresa já foi estimado e avaliado, mas quando são tidos em conta os problemas do mundo real, o tamanho e a complexidade do problema tornam os métodos existentes lentos e inadequados para fornecer soluções num prazo razoável. O principal objetivo deste projeto é então selecionar, projetar e desenvolver uma meta-heurística eficiente que forneça boas soluções em curtos períodos de tempo.The capacity-pricing problem in car rental has increasingly been stepping in the spotlight as companies began investing in advanced decision-support tools for these critical issues. When planning a sales period, a company must decide the number and type of vehicles needed in its fleet in order to meet demand. The demand for rental vehicles is particularly price-sensitive and therefore capacity and pricing decisions are closely linked. In addition, as the products are rented, the capacity "returns". This creates an association between capacity, fleet mobilization and other tools that allow the company to meet demand, such as upgrades, transferring vehicles between locations or the temporary leasing of additional vehicles. The impact of solving this complex problem on a company's profit has already been estimated and evaluated, but when real-world problems are taken into account, the size and complexity of the problem makes existing methods slow and inadequate to provide solutions within a reasonable time. Therefore, the main objective of this dissertation is then to select, design and develop an efficient metaheuristic that provides similar or better results than the ones obtained in the literature

Algoritmos de aproximação para problemas de roteamento e conectividade com múltiplas funções de distância

Orientador: Lehilton Lelis Chaves PedrosaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta dissertação, estudamos algumas generalizações de problemas clássicos de roteamento e conectividade cujas instâncias são compostas por um grafo completo e múltiplas funções de distância. Por exemplo, existe o Problema do Caixeiro Alugador (CaRS), no qual um viajante deseja visitar um conjunto de cidades alugando um ou mais carros disponíveis. Cada carro tem uma função de distância e uma taxa de retorno ao local do aluguel. CaRS é uma generalização do Problema do Caixeiro Viajante (TSP). Nós lidamos com esses problemas usando algoritmos de aproximação, que são algoritmos eficientes que produzem soluções com garantia de qualidade. Neste trabalho, são apresentadas duas abordagens, uma baseada em uma redução linear que preserva o fator de aproximação e outra baseada na construção de instâncias de dois problemas distintos. Os problemas considerados são o Steiner TSP, o Problema do Passeio com Coleta de Prêmios e o Problema da Floresta Restrita. Generalizamos cada um desses problemas considerando múltiplas funções de distância e, para cada um deles, apresentamos um algoritmo de aproximação com fator O(logn), onde n é o número de vértices (cidades). Essas aproximações são assintoticamente ótimas, já que não há algoritmos com fator o(log n), a não ser que P = NPAbstract: In this dissertation, we study some generalizations of classical routing and connectivity problems whose instances are composed of a complete graph and multiple distance functions. As an example, there is the Traveling Car Renter Problem (CaRS) in which a traveler wants to visit a set of cities by renting one or more available cars. Each car is associated to a distance function and a service fee to return to the rental location. CaRS is a generalization of the Traveling Salesman Problem (TSP). We deal with these problems using approximation algorithms which are efficient algorithms that produce solutions with quality guarantee. In this work, two approaches are presented, one based on a linear reduction that preserves the approximation factor and the other based on the construction of instances of two distinct problems. The studied problems are the Steiner TSP, the Profitable Tour Problem, and the Constrained Forest Problem. We generalize these problems by considering multiple distance functions and, for each of them, we present an O(log n)-approximation algorithm, where n is the number of vertices (cities). The factor is asymptotically optimal, since there is no approximation algorithm with factor o(log n) unless P = NPMestradoCiência da ComputaçãoMestra em Ciência da Computação001CAPE

Uma análise sobre o problema do caixeiro viajante alugador com passageiros e seus subproblemas / An analysis of the traveling car renter with passengers and its subproblems

 Neste artigo apresentamos uma análise sobre o Problema do Caixeiro Viajante Alugador com Passageiros (CaRSP) e seus subproblemas. O CaRSP é uma variante do clássico Problema do Caixeiro Viajante (PCV) que leva em consideração duas tendências atuais no sistema de transportes: o aluguel e o compartilhamento de veículos. A variante do PCV que trabalha com aluguel de veículos se chama Problema do Caixeiro Alugador (CaRS). Este trabalho apresenta uma correção de um modelo matemático já relatado na literatura para o CaRS. Também é proposta uma versão do PCV que trabalha apenas com o compartilhamento de veículos chamada de Problema do Caixeiro Viajante com Passageiros (PCV-P). Estas variantes tem o potencial de aumentar a taxa de ocupação dos automóveis em estradas ou cidades e minimizar custos a partir da divisão de despesas. Além disso, também podem reduzir a quantidade de veículos transitando e, consequentemente, os impactos ambientais causados pela poluição, os engarrafamentos e áreas ocupadas por carros estacionados. O trabalho apresenta a descrição, o estado da arte e a formulação matemática de três problemas: CaRS, PCV-P e CaRSP. Cada problema envolve decisões no que se refere à sequência de cidades visitadas, à ordem de veículos utilizados e/ou ao esquema de embarque de passageiros ao longo do percurso. Estes modelos são implementados em um solver e submetidos para solucionar um grupo de 30 instâncias. Os resultados obtidos são analisados e conclusões são tecidas à cerca do desempenho de cada modelo.     

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Um Algoritmo Híbrido para o Problema da Clique Máxima / A Hybrid Algorithm for the Maximum Clique Problem

Este artigo apresenta um algoritmo que realiza a hibridização entre a meta-heurística Simulated Anneling e uma Lista Tabu para resolver o problema da clique máxima. O algoritmo foi avaliado mediante os resultados obtidos para o banco de instâncias do Centro de matemática discreta e ciência da computação teórica (DIMACS). O algoritmo consegue processar um total de 83% das instâncias em menos de 2 minutos e obtém o valor ótimo para 74,6%. Os resultados mostraram-se promissores em contraste com as observações realizadas na literatura. Em comparação com o resultado recente publicado, a média da solução do algoritmo aqui apresentado foi estritamente melhor em 52,11% e empataram em 21,1% das instâncias

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

National freight transport planning: towards a Strategic Planning Extranet Decision Support System (SPEDSS)

This thesis provides a `proof-of-concept' prototype and a design architecture for a Object Oriented (00) database towards the development of a Decision Support System (DSS) for the national freight transport planning problem. Both governments and industry require a Strategic Planning Extranet Decision Support System (SPEDSS) for their effective management of the national Freight Transport Networks (FTN). This thesis addresses the three key problems for the development of a SPEDSS to facilitate national strategic freight planning: 1) scope and scale of data available and required; 2) scope and scale of existing models; and 3) construction of the software. The research approach taken embodies systems thinking and includes the use of: Object Oriented Analysis and Design (OOA/D) for problem encapsulation and database design; artificial neural network (and proposed rule extraction) for knowledge acquisition of the United States FTN data set; and an iterative Object Oriented (00) software design for the development of a `proof-of-concept' prototype. The research findings demonstrate that an 00 approach along with the use of 00 methodologies and technologies coupled with artificial neural networks (ANNs) offers a robust and flexible methodology for the analysis of the FTN problem domain and the design architecture of an Extranet based SPEDSS. The objectives of this research were to: 1) identify and analyse current problems and proposed solutions facing industry and governments in strategic transportation planning; 2) determine the functional requirements of an FTN SPEDSS; 3) perform a feasibility analysis for building a FTN SPEDSS `proof-of-concept' prototype and (00) database design; 4) develop a methodology for a national `internet-enabled' SPEDSS model and database; 5) construct a `proof-of-concept' prototype for a SPEDSS encapsulating identified user requirements; 6) develop a methodology to resolve the issue of the scale of data and data knowledge acquisition which would act as the `intelligence' within a SPDSS; 7) implement the data methodology using Artificial Neural Networks (ANNs) towards the validation of it; and 8) make recommendations for national freight transportation strategic planning and further research required to fulfil the needs of governments and industry. This thesis includes: an 00 database design for encapsulation of the FTN; an `internet-enabled' Dynamic Modelling Methodology (DMM) for the virtual modelling of the FTNs; a Unified Modelling Language (UML) `proof-of-concept' prototype; and conclusions and recommendations for further collaborative research are identified