A Combined Metaheuristic Algorithm for the Vehicle Routing Problem and its Open Version

Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated vehicles is located. We are also given the ‘‘traveling costs’’ between the depot and all the customers, and between each pair of customers. In the OVRP against to VRP, vehicles are not required to return to the depot after completing service. Because VRP and OVRP belong to NP-hard Problems, an efficient hybrid elite ant system called EACO is proposed for solving them in the paper. In this algorithm, a modified tabu search (TS), a new state transition rule and a modified pheromone updating rule are used for more improving solutions. These modifications lead that the proposed algorithm does not trapped at the local optimum and discovers different parts of the solution space. Computational results on fourteen standard benchmark instances for VRP and OVRP show that EACO finds the best known solutions for most of the instances and is comparable in terms of solutions quality to the best performing published metaheuristics in the literature

Proposta de um modelo de roteamento aberto de veículos em uma instituição prestadora de serviços de saúde

This research article presents an optimization model based on the application of two heuristics for a real situation of routing a fleet of vehicles of an Institution Provider of Health Services (IPS) to transport their patients. A quantitative study was carried out by applying the heuristics of the nearest neighbor and that of the modified nearest neighbor as this type of routing is of the COVRP type, for its initials in English: capacitated open vehicle routing problem. The cost table, the distance matrix construction algorithm and the heuristic algorithms are presented. The results indicate that the nearest neighbor heuristic offers a solution with a lower cost than that of the modified nearest neighbor since the savings would be 7.34% and 6.05% with respect to the current cost.En este artículo de investigación se  presenta un modelo de optimización basado en la aplicación de dos heurísticas para una situación real de enrutamiento de una flota de vehículos de una Institución Prestadora de Servicios de Salud (IPS) para transportar sus pacientes. Se realizó un estudio cuantitativo mediante la aplicación de las heurísticas del vecino más cercano y la del vecino más cercano modificada ya que este tipo de enrutamiento es del tipo COVRP, por sus siglas en inglés: capacited opened vehicle routing problem. Se presenta la tabla de desglose de los costos, el algoritmo de construcción de la matriz de distancias y los algoritmos para las heurísticas. Los resultados indican que la heurística del vecino más cercano ofrece una solución con un costo menor que la del vecino más cercano modificada ya que los ahorros serían del 7,34% y 6,05% respecto al costo actual.Este artigo de pesquisa apresenta um modelo de otimização baseado na aplicação de duas heurísticas para uma real situação de roteamento de uma frota de veículos de uma Instituição de Prestadores de Serviços de Saúde (IPS) para o transporte de seus pacientes. Um estudo quantitativo foi realizado aplicando as heurísticas do vizinho mais próximo e do vizinho vizinho modificado, uma vez que esse tipo de roteamento é do tipo COVRP, por sua sigla em inglês: capacited open vehicle routing problem. A tabela de decomposição de custos, o algoritmo para a construção da matriz de distância e os algoritmos para as heurísticas são apresentados. Os resultados indicam que a heurística do vizinho mais próximo oferece uma solução com custo menor que o vizinho mais próximo modificado, uma vez que a economia seria de 7,34% e 6,05% em relação ao custo atual

Developing sustainable supply chains in regional Australia considering demand uncertainty, government subsidies and carbon tax regulation

There is a tremendous opportunity to implement sustainable supply chain management practices in terms of logistics, operations, and transport network in regional Australia. Unfortunately, this opportunity has not been investigated and there is a lack of academic studies in this body of knowledge. This thesis is made up by three related, but independent models designed to efficiently distribute products from a regional hub to other part of the country. This research aims to develop efficient and sustainable supply chain practices to deliver regional Australian products across the country and overseas. As the airports of most Australian capital cities are over-crowded while many regional airports are under-utilised, the first model examines the ways to promote the use of regional airports. Australia is a significant food producer and the agricultural products are primarily produced in regional areas. In the other two models, we focus on the distribution of perishable products from regional Australia. The first model presented in Chapter 2 outlines how different government subsidy schemes can be used to influence airfreight distributions that favour the use of regional airports and promote regional economic development. The model simultaneously considers time-window and release-time constraints as well as the heterogeneous fleet for ground distribution where fuel consumption is subject to load, travel distance, speed and vehicle characteristics. A real-world case study in the state of Queensland, Australia is used to demonstrate the application of the model. The results suggest that the regional airport's advantages can be promoted with suitable subsidy programs and the logistics costs can be reduced by using the regional airport from the industry’s perspective. The second model presented in Chapter 3 examines the impacts of carbon emissions arising from the storage and transportation of perishable products on logistical decisions in the cold supply chain considering carbon tax regulation and uncertain demand. The problem is formulated as a two-stage stochastic programming model where Monte Carlo approach is used to generate scenarios. The aim of the model is to determine optimal replenishment policies and transportation schedules to minimise both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The proposed model was implemented in a real-world case study in the state of Queensland, Australia to demonstrate the application of the model. The results highlight that a higher emissions price does not always contribute to the efficiency of the cold supply chain system. The third model presented in Chapter 4 investigates the impacts of two different transport modes - road and rail - on the efficiency and sustainability of transport network to deliver meat and livestock from regional Queensland to large cities and seaports. The model is formulated as a mixed-integer linear programming model that considers road traffic congestions, animal welfare, quality of meat products and environmental impacts from fuel consumption of different transport modes. The aim of the model is to determine an optimal network configuration where each leg of journey is conducted by the most reliable, sustainable and efficient transport mode. The results indicate that it would be possible to significantly decrease total cost if a road-rail intermodal network is used. Considering animal welfare, product quality and traffic congestion can have a significant effect on the decisions related to transport mode selection