14 research outputs found

    Vehicle routing for hazardous material transportation

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    The main objective of this thesis is to study the hazardous materials (HazMat) transportation problem considered as a heterogeneous fleet vehicle routing problem. HazMat transportation decisions comprise different and sometimes conflicting objectives. Two are considered in this work, the total routing cost and the total routing risk. The first task undertaken was the formulation of a mathematical model for the routing risk minimization, which depends on the type of vehicle, the material being transported, and the load change when the vehicle goes from one customer to another. A piecewise linear approximation is employed to keep a mixed integer linear programing formulation. Hybrid solution methods based on neighborhood search are explored for solving the routing risk minimization. This includes the study of neighborhood structures and the development of a Variable Neighborhood Descent (VND) algorithm for local search, and a perturbation mechanism (shaking neighborhoods). A postoptimization procedure is applied to improve the solution quality. Finally, two different solution approaches, a multi-objective dominance-based algorithm and a meta-heuristic ϵ-constraint method are employed for addressing the multi-objective version of the problem. Two performance metrics are used: the hypervolume and the ∆-metric. The front approximations show that a small increment in the total routing cost can produce a high reduction in percentage of the expected consequences given the probability of a HazMat transportation incident.Résumé: L’objectif de cette thèse est d’étudier le problème du transport de matiêres dan- ` gereuses (HazMat) vu comme un probleme de tournées de véhicules à flotte hétèrogène. Les dècisions pour ce type de transport comportent des objectifs différents, parfois antagonistes. Deux sont pris en compte dans ce travail, le coût et le risque. La première tâche entreprise a été la formulation d’un modèle mathématique pour la minimisation du risque, qui depend du type de véhicule, du matériel transporté et du changement de charge lorsque le véhicule passe d’un client à un autre. Une approximation linéaire par morceaux est utilisée pour conserver une formulation de programmation linéaire en nombres entiers mixtes. Des méthodes hybrides basées sur des explorations de voisinages sont proposées pour traiter la minimisation du risque. Cela comprend l’étude des structures de voisinages et le développement d’un algorithme de descente à voisinages variables (VND) pour la recherche locale, ainsi qu’un mécanisme de perturbation des solutions. Une post-optimisation est appliquée pour améliorer la qualité des solutions obtenues. Enfin, deux approches, un algorithme base sur la dominance multi-objectif et une méta-heuristique de type ϵ- contrainte, sont développes pour traiter la version multi-objectif. Deux mesures de performance sont utilisées : l’hypervolume et la ∆-metrique. Les approximations de fronts montrent qu’une légère augmentation du coût total des tournées peut entraîner une forte réduction en pourcentage des risques.Resumen: El objetivo principal de esta tesis es estudiar el problema del transporte de materiales peligrosos (HazMat hazardous materials) modelado como un problema de ruteo de vehículos con flota heterogénea (HVRP ´ heterogeneous fleet vehicle routing problem). Las decisiones en el transporte de HazMat comprenden considerar objetivos diferentes y a veces contradictorios. Dos son los objetivos considerados en este trabajo, el costo y el riesgo total de ruteo. La primera tarea realizada fue la formulación de un modelo matemático para la minimización del riesgo de ruteo, que depende del tipo de vehículo, el material que se transporta y el cambio en el tamaño de la carga cuando el vehículo pasa de un cliente a otro. Se emplea una aproximación lineal por partes de la función objetivo para mantener una formulación de programación lineal entera mixta. Se exploran métodos híbridos de solución basados en búsqueda por vecindarios para resolver el problema de minimización del riesgo total de ruteo. Esto incluye el estudio de las estructuras del vecindario y el desarrollo de un algoritmo de descenso de vecindario variable (VND variable neighborhood descent) para realizar la búsqueda local, y de mecanismos de perturbación (estructuras de vecindario para perturbar las soluciones). Se aplica un procedimiento post-optimización (SP set partitioning) para mejorar la calidad de las soluciones. Finalmente, se emplean dos enfoques de solución diferentes para abordar la versión multi-objetivo del problema, un algoritmo basado en la dominancia Pareto y un método ϵ-constraint heurísticos. Se utilizan dos indicadores de rendimiento para algoritmos multiobjetivo: el hypervolumen y la métrica ∆. Las aproximaciones del frente Pareto obtenidas muestran que un pequeño incremento en el costo total de ruteo puede producir una gran reducción en el porcentaje de las consecuencias esperadas dada la probabilidad de un incidente de transporte de materiales peligrosos.Doctorad

    A Polyhedral Study of Mixed 0-1 Set

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    We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

    Hybrid metaheuristics for solving multi-depot pickup and delivery problems

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    In today's logistics businesses, increasing petrol prices, fierce competition, dynamic business environments and volume volatility put pressure on logistics service providers (LSPs) or third party logistics providers (3PLs) to be efficient, differentiated, adaptive, and horizontally collaborative in order to survive and remain competitive. In this climate, efficient computerised-decision support tools play an essential role. Especially, for freight transportation, e efficiently solving a Pickup and Delivery Problem (PDP) and its variants by an optimisation engine is the core capability required in making operational planning and decisions. For PDPs, it is required to determine minimum-cost routes to serve a number of requests, each associated with paired pickup and delivery points. A robust solution method for solving PDPs is crucial to the success of implementing decision support tools, which are integrated with Geographic Information System (GIS) and Fleet Telematics so that the flexibility, agility, visibility and transparency are fulfilled. If these tools are effectively implemented, competitive advantage can be gained in the area of cost leadership and service differentiation. In this research, variants of PDPs, which multiple depots or providers are considered, are investigated. These are so called Multi-depot Pickup and Delivery Problems (MDPDPs). To increase geographical coverage, continue growth and encourage horizontal collaboration, efficiently solving the MDPDPs is vital to operational planning and its total costs. This research deals with designing optimisation algorithms for solving a variety of real-world applications. Mixed Integer Linear Programming (MILP) formulations of the MDPDPs are presented. Due to being NP-hard, the computational time for solving by exact methods becomes prohibitive. Several metaheuristics and hybrid metaheuristics are investigated in this thesis. The extensive computational experiments are carried out to demonstrate their speed, preciseness and robustness.Open Acces

    Dynamic pricing services to minimise CO2 emissions of delivery vehicles

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    In recent years, companies delivering goods or services to customers have been under increasing legal and administrative pressure to reduce the amount of CO2 emissions from their delivery vehicles, while the need to maximise profit remains a prime objective. In this research, we aim to apply revenue management techniques, in particular incentive/dynamic pricing to the traditional vehicle routing and scheduling problem while the objective is to reduce CO2 emissions. With the importance of accurately estimating emissions recognised, emissions models are first reviewed in detail and a new emissions calculator is developed in Java which takes into account time-dependent travel speeds, road distance and vehicle specifications. Our main study is a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request for the service at their preferred time windows and the company needs to allocate the service tasks to time windows and decide on how to schedule these tasks to their vehicles. Incentives are provided to encourage customers choosing low emissions time windows. To help the company in determining the schedules/routes and incentives, our approach solves the problem in two phases. The first phase solves time-dependent vehicle routing/scheduling models with the objective of minimising CO2 emissions and the second phase solves a dynamic pricing model to maximise profit. For the first phase problem, new solution algorithms together with existing ones are applied and compared. For the second phase problem, we consider three different demand modelling scenarios: linear demand model, discrete choice demand model and demand model free pricing strategy. For each of the scenarios, dynamic pricing techniques are implemented and compared with fixed pricing strategies through numerical experiments. Results show that dynamic pricing leads to a reduction in CO2 emissions and an improvement in profits

    Preventing premature convergence and proving the optimality in evolutionary algorithms

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    http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

    Optimización metaheurística para la planificación de redes WDM

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    Las implementaciones actuales de las redes de telecomunicaciones no permiten soportar el incremento en la demanda de ancho de banda producido por el crecimiento del tráfico de datos en las últimas décadas. La aparición de la fibra óptica y el desarrollo de la tecnología de multiplexación por división de longitudes de onda (WDM) permite incrementar la capacidad de redes de telecomunicaciones existentes mientras se minimizan costes. En este trabajo se planifican redes ópticas WDM mediante la resolución de los problemas de Provisión y Conducción en redes WDM (Provisioning and Routing Problem) y de Supervivencia (Survivability Problem). El Problema de Conducción y Provisión consiste en incrementar a mínimo coste la capacidad de una red existente de tal forma que se satisfaga un conjunto de requerimientos de demanda. El problema de supervivencia consiste en garantizar el flujo del tráfico a través de una red en caso de fallo de alguno de los elementos de la misma. Además se resuelve el Problema de Provisión y Conducción en redes WDM con incertidumbre en las demandas. Para estos problemas se proponen modelos de programación lineal entera. Las metaheurísticas proporcionan un medio para resolver problemas de optimización complejos, como los que surgen al planificar redes de telecomunicaciones, obteniendo soluciones de alta calidad en un tiempo computacional razonable. Las metaheurísticas son estrategias que guían y modifican otras heurísticas para obtener soluciones más allá de las generadas usualmente en la búsqueda de optimalidad local. No garantizan que la mejor solución encontrada, cuando se satisfacen los criterios de parada, sea una solución óptima global del problema. Sin embargo, la experimentación de implementaciones metaheurísticas muestra que las estrategias de búsqueda embebidas en tales procedimientos son capaces de encontrar soluciones de alta calidad a problemas difíciles en industria, negocios y ciencia. Para la solución del problema de Provisión y Conducción en Redes WDM, se desarrolla un algoritmo metaheurístico híbrido que combina principalmente ideas de las metaheurísticas Búsqueda Dispersa (Scatter Search) y Búsqueda Mutiarranque (Multistart). Además añade una componente tabú en uno de los procedimiento del algoritmo. Se utiliza el modelo de programación lineal entera propuesto por otros autores y se propone un modelo de programación lineal entera alternativo que proporciona cotas superiores al problema, pero incluye un menor número de variables y restricciones, pudiendo ser resuelto de forma óptima para tamaños de red mayores. Los resultados obtenidos por el algoritmo metaheurístico diseñado se comparan con los obtenidos por un procedimiento basado en permutaciones de las demandas propuesto anteriormente por otros autores, y con los dos modelos de programación lineal entera usados. Se propone modelos de programación lineal entera para sobrevivir la red en caso de fallos en un único enlace. Se proponen modelos para los esquemas de protección de enlace compartido, de camino compartido con enlaces disjuntos, y de camino compartido sin enlaces disjuntos. Se propone un método de resolución metaheurístico que obtiene mejores costes globales que al resolver el problema en dos fases, es decir, al resolver el problema de servicio y a continuación el de supervivencia. Se proponen además modelos de programación entera para resolver el problema de provisión en redes WDM con incertidumbres en las demandas

    Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

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    The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios
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