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The Vehicle Routing Problem (VRP) dates back to the end of the fifties of the last century when Dantzig and Ramser set the mathematical programming formulation and algorithmic approach to solve the problem of delivering gasoline to service stations. Since then the interest in VRP evolved from a small group of mathematicians to a broad range of researchers and practitioners from different disciplines who are involved in this field today. Nine chapters of this book present recent improvements, innovative ideas and concepts regarding the vehicle routing problem. It will be of interest to students, researchers and practitioners with knowledge of the main methods for the solution of the combinatorial optimization problems.
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Vehicle Routing Problems are concerned with determining how a set of vehicles can visit a set of customers while minimizing some objective. It is a complex and difficult problem. Exact methods of resolution are limited to problems with relatively small size. This is why many approximate methods were adapted to the resolution of this problem. Among this adaptations, we find algorithms based meta-heuristics. This master thesis presents a computation software of school bus tours. It explains the problem of Traveling Salesman Problem and vehicle Routing Problem and by using Concorde TSP solver, gives heuristics algorithms which we have used in this project to solve it.
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The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance.
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This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering.
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Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems.
Technology: general issues --- History of engineering & technology --- matheuristics --- hyperheuristics --- vehicle routing --- wireless networking --- green energy --- low carbons 
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The world is on the edge of an economical breakthrough with the emergence of autonomous cars. Such vehicles do not, or for a very short time, require the driver’s attention or even not require a driver at all. This new era of vehicles is largely promoted by Tesla’s eccentric CEO Elon Musk and the promotion of the S 3 X Y series of vehicles which include an autopilot mode. In parallel to the emergence of autonomous cars, autonomous trucks are also in development and are likely to revolutionize the transport sector and in particular freight transport. Again, Tesla wants to be a pioneer in this domain with the Tesla Semi, which is supposed to arrive around 2021 on the market. Autonomous vehicles are expected to improve road safety by drastically reducing the number of incidents. Furthermore, they are likely to be an excellent ally in the fight against climate change by improving the road transport efficiency and reducing greenhouse gas emissions. The aim of this work is to emphasize the gains of using autonomous trucks over regular trucks for freight transport. These gains are mainly based on the prime costs of such trucks but also, in a lesser extent, to the greenhouse gas emissions. To do so, two similar trucks, one with a driver and an autonomous one, are compared for different delivery situations, from national to international level. While the autonomous truck can drive continuously, the driver is submitted to the European legislation on driving and working hours. Their respective journey, which visits a set of n clients selected randomly, is computed by minimizing either the driving time or the driving distance. To each route, a monetary cost is associated which serves as a basis for the comparison between the two trucks.
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The Thesis aims to develop a biobjective vehicle routing problem integrating costs and emissions into the optimization of routes with a heterogenouse fleet of vehicles.
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"What is the shortest possible route for a traveling salesman seeking to visit each city on a list exactly once and return to his city of origin? It sounds simple enough, yet the traveling salesman problem is one of the most intensely studied puzzles in applied mathematics--and it has defied solution to this day. In this book, William Cook takes readers on a mathematical excursion, picking up the salesman's trail in the 1800s when Irish mathematician W. R. Hamilton first defined the problem, and venturing to the furthest limits of today's state-of-the-art attempts to solve it. Cook examines the origins and history of the salesman problem and explores its many important applications, from genome sequencing and designing computer processors to arranging music and hunting for planets. He looks at how computers stack up against the traveling salesman problem on a grand scale, and discusses how humans, unaided by computers, go about trying to solve the puzzle. Cook traces the salesman problem to the realms of neuroscience, psychology, and art, and he also challenges readers to tackle the problem themselves. The traveling salesman problem is--literally--a $1 million question. That's the prize the Clay Mathematics Institute is offering to anyone who can solve the problem or prove that it can't be done. In Pursuit of the Traveling Salesman travels to the very threshold of our understanding about the nature of complexity, and challenges you yourself to discover the solution to this captivating mathematical problem"--
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