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

Milk Run Design: Definitions, Concepts and Solution Approaches

Efficient inbound networks in the European automotive industry rely on a set of different transport concepts including milk runs - understood as regularly scheduled pickup tours. The complexity of designing such a mixed network makes decision support necessary: In this book we provide definitions, mathematical models and a solution method for the Milk Run Design problem and introduce indicators assessing the performance of established milk runs in relation to alternative transport concepts

Public Transportation Systems:Basic Principles of System Design,Operations Planning and Real-TimeControl

This document is based on a set of lecture notes prepared in 2007-2010 for a University of California, Berkeley graduate course, Public Transportation Systems, a course targeted to first year graduate students with diverse academic backgrounds. Systems are examined in order of increased complexity so that generic insights evident in simple systems can be put to use as knowledge building blocks for the study of more complex systems. The document is organized in eight modules: five on planning (general, shuttle systems, corridors, two dimensional systems, and unconventional transit); two on management (vehicles and employees); and one on operations (how to stay on schedule)

A collaborative framework in outbound logistics for the us automakers

The competitive landscape of the U.S. automotive market has transformed from the traditional Big Three players to too many viable players. In 2008-2009, the harsh market conditions, excess production capacity, capital asset redundancies, and many inefficient strategies submerged as the roadblocks for the US automakers to stay competitive and profitable in the North American market. In this new competitive era, cross-company collaboration in product development, standardizing and communizing supply base, sharing flexible manufacturing platforms, using common inbound and out bound logistics service providers and warehousing etc. can play vital roles for the US automakers to reduce overall cost and return to profitability. Through the horizontal collaboration in the outbound logistics operations, these companies can create close-knit business partnership and act faster than the foreign rivals in delivering finished vehicles at the optimum cost. The optimization of outbound logistics operations through consolidation and collaboration among OEMs has tremendous potential to contribute to the profitability by lowering the cost of transportation, in-house inventory, transportation time, and facility costs. The collaboration in the intra- and inter-OEM outbound logistics operations is a critical area that the US automakers need to pay attention and prioritize in their cost reduction initiatives. This research presents an integrated collaboration framework for the outbound logistics operations of the US automakers. In our framework, we propose three potential levels for the US automakers to form outbound logistics collaboration: operational, tactical, and strategic. Our research proposition is to improve the performance of outbound logistics systems of automotive OEMs by means of horizontal collaboration between plants and competing OEMs. The proposed research thus relates to the literature on logistics system design and management and horizontal collaboration in supply chain management. The collaboration framework is demonstrated through a real world case study in US automotive industry. The contribution of this research is the introduction of a framework for intra- and inter-OEM collaboration and the development of novel logistics network design and flow models integrated with inventory models, lost sales, and expedited shipment. Besides the contribution to the academic literature, the proposed collaborative distribution system is a new concept in the automotive industry. Hence, this novel research work will also benefit to the practitioners. Keywords: Operational Collaboration, Tactical Collaboration, Strategic Collaboration, Frequency based Inventory, Customer Patience and Lost Sales, Expedited Shipments