76 research outputs found
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
Algorithms for multi-trip vehicle routing and device to device communications
Motivated by the transportation needs of modern-day retailers, we consider a variant of the vehicle routing problem with time windows in which each truck has a variable capacity.
In our model, each vehicle can bring one or more wagons. The clients are visited within
specified time windows, and the vehicles can also make multiple trips. We give a math-
ematical programming formulation for the problem and a branch and price algorithm is
used to solve the model. In each iteration of branch and price, column generation is ap-
plied. Based on the different capacities, different subproblems are created to find the best
column. We extend Solomon’s instances to evaluate our approach. We report on the com-
putational results using concert technology in CPLEX. Ours is the first such study to the
best of our knowledge. For the second part of the thesis, we study the sharing of spec-
trum in the device-to-device (D2D) communications in an underlay cellular network. Our
model maximizes the total sum-rate such that i) each D2D link is assigned at most one
sub-channel, ii) the total interference is at most the required maximum. Our model can also minimize the interference subject to i) the total sum-rate being bounded by some required amount. We give a branch-n-cut algorithm for solving both models. We give a Lagrangian relaxation which is solved optimally and combinatorially for the minimization objective.
We give an iterative rounding algorithm that achieves at least a quarter of the optimal sum
rate and no more than the required maximum of the total interference when the objective
is to maximize sum-rate. Detailed experiments are performed on synthetic as well as net-
work simulator data. Our experiments establish the effectiveness of the branch-n-cut and
the iterative rounding approach for channel assignment.
This thesis is a study on the use of branch and cut, branch and price, and iterative
rounding for solving two real world optimization problems.NSER
Planning and Scheduling Optimization
Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development
Applications of biased-randomized algorithms and simheuristics in integrated logistics
Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe
Commercial Helicopter Services: Toward Quantitative Solutions for Understanding Industry Phenomena and Achieving Stakeholder Optimization
An understanding of industry phenomena and optimization techniques within the upstream energy industry’s transportation sector is markedly absent in the extant literature and suitable for rigorous investigation. This manuscript presents analyses related to the optimization of offshore worker transportation and econometric analyses of factors influencing commercial helicopter operators’ stock returns, which are represented throughout the manuscript as Part I and Part II, respectively.
The global energy industry transports supplies and personnel via helicopter to offshore locations and has been increasingly focusing on optimizing upstream logistics. Using a unique sample of deepwater and ultra-deepwater permanent offshore locations in the Gulf of Mexico, transportation networks consisting of 58 locations operated by 19 firms are optimized via a randomized greedy algorithm. The model developed in Part I has been found to effectively solve the complex transportation problem and simulation results show the potential advantages of alternative clustered and integrated network structures, as compared to an independent firm-level structure. The evaluation of clustered and integrated network structures, which allow ride sharing via energy firm cooperation, provides evidence that such network structures may yield cost reductions for participating firms.
The extent to which commercial helicopter operators’ stock returns are related to commodity prices and other relevant industry variables is absent in the extant literature. Often, firms attribute favorable results to internal factors whereas unfavorable results are attributed to external factors. Using a unique data set from 2013-2018, the current research identifies structural relationships between crude oil prices, natural gas prices, the rotary rig count, a subset of the overall market, firms’ degree of diversification and stock returns of commercial helicopter operators. Empirical analyses developed in Part II show that the prevalent price of crude oil and the overall market environment possess explanatory power of commercial helicopter firms’ stock returns, ceteris paribus. Specifically, 10% increases in the crude oil price and the S&P 500 index yield a 2.7% and 8.0% increase in stock returns, respectively.
Collectively, the abovementioned parts of this manuscript provide rigorous, quantitative analyses of topics unrepresented within the extant literature, which are foundational for future practice and research. Specifically, new knowledge regarding a practical approach to model development and solution deliverance for the transportation of offshore workers to their respective locations and factors influencing commercial helicopter operators’ stock returns has been appropriately designed and empirically evaluated
Survey on Ten Years of Multi-Depot Vehicle Routing Problems: Mathematical Models, Solution Methods and Real-Life Applications
A crucial practical issue encountered in logistics management is the circulation of final products from depots to end-user customers. When routing and scheduling systems are improved, they will not only improve customer satisfaction but also increase the capacity to serve a large number of customers minimizing time. On the assumption that there is only one depot, the key issue of distribution is generally identified and formulated as VRP standing for Vehicle Routing Problem. In case, a company having more than one depot, the suggested VRP is most unlikely to work out. In view of resolving this limitation and proposing alternatives, VRP with multiple depots and multi-depot MDVRP have been a focus of this paper. Carrying out a comprehensive analytical literature survey of past ten years on cost-effective Multi-Depot Vehicle Routing is the main aim of this research. Therefore, the current status of the MDVRP along with its future developments is reviewed at length in the paper
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