A hybrid GRASP-VNS for Ship Routing and Scheduling Problem with Discretized Time Windows

This paper addresses the Ship Routing and Scheduling Problem with Discretized Time Windows. Being one of the most relevant and challenging problems faced by decision makers from shipping companies, this tramp shipping problem lies in determining the set of contracts that should be served by each ship and the time windows that ships should use to serve each contract, with the aim of minimizing total costs. The use of discretized time windows allows for the consideration of a broad variety of features and practical constraints in a simple way. In order to solve this problem we propose a hybridazation of a Greedy Randomized Adaptive Search Procedure and a Variable Neighborhood Search, which improves previous heuristics results found in literature and requires very short computational time. Moreover, this algorithm is able to achieve the optimal results for many instances, demonstrating its good performance

Optimización de Rutas basadas en Soft Computing para Movilidad Inteligente

La movilidad y transporte de pasajeros y mercancías es uno de los principales desafíos para el desarrollo de islas, ciudades y territorios. La prosperidad, competitividad y sostenibilidad de múltiples áreas económicas se ven afectadas por la movilidad. El crecimiento de la población, la capacidad limitada de los sistemas e infraestructuras de transporte y el impacto medioambiental del transporte fuerza a los territorios en el desarrollo de una movilidad sostenible y efectiva. En este complejo escenario, un territorio con una gestión del transporte y movilidad sostenible y eficiente ofrece a los ciudadanos una mejor calidad de vida. La transformación digital y las TIC impulsan la mejora de los servicios de movilidad para los ciudadanos, ayudan a gestionar correctamente la demanda en las redes de transporte y generan valor económico y ambiental. El surgimiento de la movilidad inteligente integra el sistema de transporte, las infraestructuras y las tecnologías para hacer que el transporte de pasajeros y mercancías sea eficiente, accesible, más seguro y limpio. Por lo tanto, las estrategias de movilidad inteligente deben ser capaces de proporcionar beneficios económicos y ambientales tangibles y mejorar la calidad del transporte de mercancías y pasajeros. Significa tomar acciones en múltiples frentes; gestión eficiente de la carga y la movilidad de pasajeros, reducción del impacto medioambiental, mejora de la planificación y la eficiencia del transporte público, reducción de la congestión, optimización del uso de la infraestructura física, entre otros. Una de las operaciones clave para los servicios de movilidad es la planificación de rutas. Esta actividad operativa incluye principalmente dos modos de transporte, mercancías y pasajeros. La mayoría de los transportes de mercancías y pasajeros se realizan a través de transporte por carretera. Las decisiones tomadas con respecto a las operaciones de planificación de rutas afectan económica y ambientalmente, y en general a la calidad de vida de los ciudadanos en los territorios en los que se desarrollan. Las operaciones de planificación de rutas se pueden optimizar para mejorar diferentes aspectos como la calidad del servicio, costes y flexibilidad del mismo, consumo de energía, impacto medioambiental, sostenibilidad, entre otros. La tarea de abordar las operaciones de planificación de rutas da lugar a la aparición de complejos problemas de optimización combinatoria que requieren considerar múltiples requisitos, restricciones, fuentes de información, entre otros. En la mayoría de los casos, estos problemas de optimización se clasifican como NP-duros con respecto a su complejidad computacional. Esta clase de problemas requiere enfoques de optimización eficientes y estrategias inteligentes para obtener soluciones de alta calidad y evitar grandes tiempos de cálculo. En este sentido, los enfoques de optimización aproximados, como las heurísticas y metaheurísticas, y las técnicas inteligentes inherentes a la Inteligencia Artificial y la Soft Computing han demostrado ser métodos efectivos y eficientes para resolver complejos problemas de planificación de rutas. Esta tesis presentada en la modalidad de compendio de publicaciones tiene como objetivo diseñar, implementar y validar procedimientos de optimización simples, eficientes y flexibles basados ​​en Inteligencia Artificial y Soft Computing dedicados a mejorar las soluciones de planificación de rutas en los contextos de transporte de mercancías, planificación personalizada de rutas turísticas y transporte eco-eficiente de residuos reciclables. Se han propuesto varios enfoques de solución para resolver problemas como Vehicle Routing Problem with Time Windows, Periodic Vehicle Routing Problem with Time Windows, Team Orienteering Problem with Time Windows, Tourist Trip Design Problem y variantes del mundo real y nuevas extensiones de los problemas mencionados. La calidad del servicio, la orientación al cliente, la imprecisión e incertidumbre en la información y la ecoeficiencia son criterios considerados en los problemas de planificación de rutas identificados. Los experimentos computacionales han demostrado que los métodos y técnicas propuestos son adecuados para obtener soluciones de alta calidad en tiempos computacionales cortos y pueden incorporarse como módulos en sistemas de transporte inteligentes

The Pickup and Multiple Delivery Problem

This thesis presents my work on the pickup and multiple delivery problem, a real-world vehicle routing and scheduling problem with soft time windows, working time and last-in-first-out constraints, developed in collaboration with Transfaction Ltd., who conduct logistics analysis for several large retailers in the UK. A summary of relevant background literature is presented highlighting where my research fits into and contributes to the broader academic landscape. I present a detailed model of the problem and thoroughly analyse a case-study data set, obtaining distributions used for further research. A new variable neighbourhood descent with memory hyper-heuristic is presented and shown to be an effective technique for solving instances of the real-world problem. I analyse strategies for cooperation and competition amongst haulage companies and quantify their effectiveness. The value of time and timely information for planning pickup and delivery requests is investigated. The insights gained are of real industrial relevance, highlighting how a variety of business decisions can produce significant cost savings

Models and Solutions Algorithms for Improving Operations in Marine Transportation

International seaborne trade rose significantly during the past decades. This created the need to improve efficiency of liner shipping services and marine container terminal operations to meet the growing demand. The objective of this dissertation is to develop simulation and mathematical models that may enhance operations of liner shipping services and marine container terminals, taking into account the main goals of liner shipping companies (e.g., reduce fuel consumption and vessel emissions, ensure on-time arrival to each port of call, provide vessel scheduling strategies that capture sailing time variability, consider variable port handling times, increase profit, etc.) and terminal operators (e.g., decrease turnaround time of vessels, improve terminal productivity without significant capital investments, reduce possible vessel delays and associated penalties, ensure fast recovery in case of natural and man-made disasters, make the terminal competitive, maximize revenues, etc.). This dissertation proposes and models two alternatives for improving operations of marine container terminals: 1) a floaterm concept and 2) a new contractual agreement between terminal operators. The main difference between floaterm and conventional marine container terminals is that in the former case some of import and/or transshipment containers are handled by off-shore quay cranes and placed on container barges, which are further towed by push boats to assigned feeder vessels or floating yard. According to the new collaborative agreement, a dedicated marine container terminal operator can divert some of its vessels for the service at a multi-user terminal during specific time windows. Another part of dissertation focuses on enhancing operations of liner shipping services by introducing the following: 1) a new collaborative agreement between a liner shipping company and terminal operators and 2) a new framework for modeling uncertainty in liner shipping. A new collaborative mechanism assumes that each terminal operator is able to offer a set of handling rates to a liner shipping company, which may result in a substantial total route service cost reduction. The suggested framework for modeling uncertainty is expected to assist liner shipping companies in designing robust vessel schedules

Modeling Framework and Solution Methodologies for On-Demand Mobility Services With Ridesharing and Transfer Options

The growing complexity of the urban travel pattern and its related traffic congestion, along with the extensive usage of mobile phones, invigorated On-Demand Mobility Services (ODMS) and opened the door to the emergence of Transportation Network Companies (TNC). By adopting the shared economy paradigm, TNCs enable private car owners to provide transportation services to passengers by providing user-friendly mobile phone applications that efficiently match passengers to service providers. Considering the high level of flexibility, convenience, and reliability of ODMS, compared to those offered by traditional public transportation systems, many metropolitan areas in the United States and abroad have reported rapid growth of such services. This dissertation presents a modeling framework to study the operation of on-demand mobility services (ODMS) in urban areas. The framework can analyze the operation of ODMS while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed-integer program and an efficient decomposition-based methodology is developed for its solution. This solution methodology aims at solving the offline version of the problem, in which the passengers’ demand is assumed to be known ii for the entire planning horizon. The presented approach adopts a modified column generation algorithm, which integrates iterative decomposition and network augmentation techniques to analyze networks with moderate size. Besides, a novel methodology for integrated ride-matching and vehicle routing for dynamic (online) ODMS with ridesharing and transfer options is developed to solve the problem in real-time. The methodology adopts a hybrid heuristic approach, which enables solving large problem instances in near real-time, where the passengers’ demand is not known a priori. The heuristic allows to (1) promptly respond to individual ride requests and (2) periodically re-evaluate the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The outcomes of experiments considering hypothetical and real-world networks are presented. The results show that the modified column generation approach provides a good quality solution in less computation time than the CPLEX solver. Additionally, the heuristic approach can provide an efficient solution for large networks while satisfying the real-time execution requirements. Additionally, investigation of the results of the experiments shows that increasing the number of passengers willing to rideshare and/or transfer increases the general performance of ODMS by increasing the number of served passengers and associated revenue and reducing the number of needed vehicles

A Polyhedral Study of Mixed 0-1 Set

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