Tactical Problems in Vehicle Routing Applications

The class of Vehicle Routing Problems (VRPs) is one the most studied topics in the Operations Research community. The vast majority of the published papers focus on single-period problems, with a few branches of the literature considering multiperiod generalisations. All of these problems though, consider a short horizon and aim at optimising the decisions at an operational level, i.e. that will have to be taken in the near future. One step above are tactical problems, i.e. problems concerning a longer time horizon. Tactical problems are of a fundamental importance as they directly influence the daily operations, and therefore a part of the incurred costs, for a long time. The main focus of this thesis is to study tactical problems arising in routing applications. The first problem considered concerns the design of a fleet of vehicles. Transportation providers often have to design a fleet that will be used for daily operations across a long-time span. Trucks used for transportation are very expensive to purchase, maintain or hire. On the other side, the composition of the fleet strongly influences the daily plans, and therefore costs such as fuel or drivers’ wages. Balancing these two components is challenging, and optimisation models can lead to substantial savings or provide a useful basis for informed decisions. The second problem presented focuses on the use of a split deliveries policy in multi-period routing problems. It is known that the combined optimisation of delivery scheduling and routing can be very beneficial, and lead to significant reductions in costs. However, it also adds complexity to the model. The same is true when split deliveries are introduced. The problem studied considers the possibility of splitting the deliveries over different days. An analysis, both theoretical and numerical, of the impact of this approach on the overall cost is provided. Finally, a districting problem for routing applications is considered. These types of problems typically arise when transportation providers wish to increase their service consistency. There are several reasons a company may wish to do so: to strengthen the customer-driver relationship, to increase drivers’ familiarity with their service area, or, to simplify the management of the service area. A typical approach, considered here, is to divide the area under consideration in sectors that will be subsequently assigned to specific drivers. This type of problem is inherently of a multi-period and tactical nature. A new formulation is proposed, integrating standard routing models into the design of territories. This makes it possible to investigate how operational constraints and other requirements, such as having a fair workload division amongst drivers, influence the effectiveness of the approach. An analysis of the cost of districting, in terms of increased routing cost and decreased routing flexibility, and of several operational constraints, is presented

Agent based simulation of the dial-a-flight problem

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018Agent based simulation and modelling (ABSM) has been noted as a novel method in solving complex problems. This dissertation makes use of the ABSM method in conjunction with a Genetic Algorithm to find good solutions to the dial-a-flight problem. The task is to generate a schedule for a heterogeneous fleet of aircraft, with the objective to reduce operational cost but maintain customer satisfaction. By making use of booking list data from an air taxi business, operating in the Okavango Delta, two agent based models were designed, the first makes use of multi-criteria decision analysis (MCDA) and the other a method proposed by Campbell [7], to test their effectiveness against either upper bound or manual solutions. The solution quality varied between tests, with booking list sizes between 10 and 200 requests producing improvements to the upper bound and manual results with a mean improvement from the benchmarks of 1.61\%. The method could also be refined further by adopting improvement mechanisms to final schedules or by making use of retrospective decision making aided by self learning techniques.MT 201

Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro

Arc routing problems: A review of the past, present, and future

[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emerging applicationsThis research was supported by the Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number: PGC2018-099428-B-I00. The Research Council of Norway, Grant/Award Numbers: 246825/O70 (DynamITe), 263031/O70 (AXIOM).Corberán, Á.; Eglese, R.; Hasle, G.; Plana, I.; Sanchís Llopis, JM. (2021). Arc routing problems: A review of the past, present, and future. Networks. 77(1):88-115. https://doi.org/10.1002/net.21965S8811577

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects