Large neighborhood search for the double traveling salesman problem with multiple stacks

This paper considers a complex real-life short-haul/long haul pickup and delivery application. The problem can be modeled as double traveling salesman problem (TSP) in which the pickups and the deliveries happen in the first and second TSPs respectively. Moreover, the application features multiple stacks in which the items must be stored and the pickups and deliveries must take place in reserve (LIFO) order for each stack. The goal is to minimize the total travel time satisfying these constraints. This paper presents a large neighborhood search (LNS) algorithm which improves the best-known results on 65% of the available instances and is always within 2% of the best-known solutions

Ant Colony Approach for Multiple Pickup and Multiple Dropoff

The Multiple Travelling Salesman Problem, popularly known as MTSP is an NP-hard problem. MTSP is a well-known combinatorial optimization problem in which more than one salesmen visit all cities only once and return to the depot. In our problem, we apply the MTSP algorithm to multiple drivers picking and dropping packets at multiple locations and the drivers not returning to the starting location. There are no exact solutions for solving this combinatorial problem that can guarantee to find the optimal route within a reasonable time. A meta-heuristic algorithm, Ant Colony Optimization (ACO) is used as a base for our solution construction for different variations of the problem such as handling multiple pickups and multiple drop-offs using a single driver, multiple drivers, drivers starting at different times, and drivers available for different times. The goal is to maximize the number of goods delivered while minimizing distance (or time) within some threshold limits. The results are compared to existing algorithms like Brute-force approach and Nearest Neighbor algorithms. Our results show that the proposed ant colony algorithm achieves better results or at worst identical results to the Brute-force approach.Computer Scienc

Ferramenta de suporte ao projeto de sistemas flexíveis de transporte público de passageiros

Tese de Doutoramento em Engenharia Industrial e de Sistemas.As áreas rurais, com densidades populacionais baixas, apresentam desafios à mobilidade das suas populações. Os serviços de transporte público regular têm-se mostrado ineficazes e ineficientes levando os operadores de transporte coletivo a reduzir a sua oferta e a diminuir a qualidade do serviço oferecido. Em alternativa aos serviços regulares de transporte, alguns estudos têm vindo a mostrar as vantagens da implementação de sistemas de transportes flexíveis, em particular, transportes a pedido (DRT - Demand Responsive Transport). No entanto, os principais resultados observados nos estudos realizados apontam para a existência de várias dificuldades para o sucesso dos DRTs (aspetos legais, organizacionais, financeiros, etc.), assim como para a inexistência de ferramentas de apoio capazes de auxiliar os decisores nas etapas do planeamento estratégico e tático, antes mesmo de proceder à sua implementação. No sentido de minorar ou colmatar as lacunas referidas, esta tese pretende contribuir para uma discussão abrangente destes sistemas de transporte e propor uma nova ferramenta de suporte ao projeto de sistemas DRT. A ferramenta proposta integra um sistema de apoio à decisão (SAD) concebido para estimar o desempenho operacional de diferentes configurações a implementar, permitindo optar pela melhor solução encontrada. O SAD é suportado por um modelo de simulação microscópica do funcionamento do sistema, e inclui métodos de solução para diferentes variantes do problema de otimização de rotas e escalas encontradas neste tipo de serviços de transporte, para além de uma framework para a avaliação da sustentabilidade das soluções. Na validação do SAD desenvolvido, utilizou-se um estudo de caso português. Os resultados dos testes efetuados permitiram evidenciar as potencialidades da ferramenta proposta. Adicionalmente, a avaliação da sustentabilidade da solução permitiu identificar a difícil sustentabilidade financeira deste tipo de sistemas, mas também as suas vantagens em termos sociais e ambientais que poderão justificar a sua adoção.Rural areas with low population densities, present challenges to mobility of their populations. The regular public transport services have proved quite ineffective and inefficient leading transport operators to reduce their supply and their services quality. As an alternative to regular services, some studies have come to show the advantages of the implementation of flexible transport systems, as demand responsive transport (DRT). However, the main results obtained in the studies scope point both to the existence of different types of difficulties to the DRTs success (legal, organizational, financial aspects, etc.), and the lack of supporting tools that can assist decision makers in both strategic and tactical planning, even before proceeding to implement. In order to overcome these shortcomings, this thesis intends to discuss broadly these transport systems and to present a new tool to support the design of DRT systems. The proposed tool integrates a decision support system (DSS) specifically designed to assess the operating performance of different alternative system configurations to be implemented, allowing to choose the best solution found. The DSS is supported by a microscopic simulation model of the system operation, and even includes solution methods for different variants of the vehicle routing problem found in this type of transportation services, furthermore a framework to evaluate the solutions sustainability. To validate the developed DSS, we used a Portuguese case study. The test results allowed highlighting the DSS potentialities. Additionally, the solution sustainability assessment identified the hard financial self-sustaining of this type of systems, but also their advantages in both social and environmental impact that will probably be sufficient to justify its implementation.Fundação para a Ciência e Tecnologia SFRH/BD/60776/2009