Vehicle Routing With Stochastic Time Dependent Travel Times
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Vehicle Routing With Stochastic Time Dependent Travel Times
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Author : C. Lecluyse
language : en
Publisher:
Release Date : 2007
Vehicle Routing With Stochastic Time Dependent Travel Times written by C. Lecluyse and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
The Vehicle Routing Problem
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Author : Paolo Toth
language : en
Publisher:
Release Date : 2002
The Vehicle Routing Problem written by Paolo Toth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Transportation problems (Programming) categories.
The Vehicle Routing Problem With Time Windows And Time Dependent Travel Times
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Author : Ana Osvald (t.d.-)
language : en
Publisher:
Release Date : 2005
The Vehicle Routing Problem With Time Windows And Time Dependent Travel Times written by Ana Osvald (t.d.-) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Publication 659 The Vehicle Routing Problem With Stochastic Travel Times
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Author : Université de Montréal. Centre de recherche sur les transports
language : en
Publisher:
Release Date : 1990
Publication 659 The Vehicle Routing Problem With Stochastic Travel Times written by Université de Montréal. Centre de recherche sur les transports and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with categories.
Time Dependent Routing
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Author : Rabie Jaballah
language : en
Publisher:
Release Date : 2022
Time Dependent Routing written by Rabie Jaballah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Business logistics categories.
The vehicle routing problem (VRP), introduced more than 60 years ago, is at the core of transportation systems. With decades of development, the VRP is one of the most studied problems in the literature, with a very rich set of variants. Yet, primarily due to the lack of data, two critical assumptions make the VRP fail to adapt effectively to traffic and congestion. The first assumption considers that the travel speed is constant over time ; the second, that each pair of customers is connected by an arc, ignoring the underlying street network. Traffic congestion is one of the biggest challenges in transportation systems. As traffic directly affects transportation activities, the whole supply chain needs to adjust to this factor. The continuous growth of freight in recent years worsens the situation, and a renewed focus on mobility, environment, and city logistics has shed light on these issues. Recently, advances in communications and real-time data acquisition technologies have made it possible to collect vehicle data such as their location, acceleration, driving speed, deceleration, etc. With the availability of this data, one can question the way we define, model, and solve transportation problems. This allows us to overcome the two issues indicated before and integrate congestion information and the whole underlying street network. We start by considering the whole underlying street network, which means we have customer nodes and intermediate nodes that constitute the street network. Then, we model the travel time of each street during the day. By dividing the day into small intervals, up to a precision of a second, we consider precise traffic information. This results in a new problem called the time-dependent shortest path vehicle routing problem (TD-SPVRP), in which we combine the time-dependent shortest path problem (TD-SPP) and the time-dependent VRP (TD-VRP), creating a more general and very challenging problem. The TD-SPVRP is closer to what can be found in real-world conditions, and it constitutes the topic of Chapter 2, where we formulate it as a mixed-integer linear programming model and design a fast and efficient heuristic algorithm to solve this problem. We test it on instances generated from actual traffic data from the road network in Québec City, Canada. Results show that the heuristic provides high-quality solutions with an average gap of only 5.66%, while the mathematical model fails to find a solution for any real instance. To solve the challenging problem, we emphasize the importance of a high-performance implementation to improve the speed and the execution time of the algorithms. Still, the problem is huge especially when we work on a large area of the underlying street network alongside very precise traffic data. To this end, we use different techniques to optimize the computational effort to solve the problem while assessing the impact on the precision to avoid the loss of valuable information. Two types of data aggregation are developed, covering two different levels of information. First, we manipulated the structure of the network by reducing its size, and second by controlling the time aggregation level to generate the traffic data, thus the data used to determine the speed of a vehicle at any time. For the network structure, we used different reduction techniques of the road graph to reduce its size. We studied the value and the trade-off of spatial information. Solutions generated using the reduced graph are analyzed in Chapter 3 to evaluate the quality and the loss of information from the reduction. We show that the transformation of the TD-SPVRP into an equivalent TD-VRP results in a large graph that requires significant preprocessing time, which impacts the solution quality. Our development shows that solving the TD-SPVRP is about 40 times faster than solving the related TD-VRP. Keeping a high level of precision and successfully reducing the size of the graph is possible. In particular, we develop two reduction procedures, node reduction and parallel arc reduction. Both techniques reduce the size of the graph, with different results. While the node reduction leads to improved reduction in the gap of 1.11%, the parallel arc reduction gives a gap of 2.57% indicating a distortion in the reduced graph. We analyzed the compromises regarding the traffic information, between a massive amount of very precise data or a smaller volume of aggregated data with some potential information loss. This is done while analyzing the precision of the aggregated data under different travel time models, and these developments appear in Chapter 4. Our analysis indicates that a full coverage of the street network at any time of the day is required to achieve a high level of coverage. Using high aggregation will result in a smaller problem with better data coverage but at the cost of a loss of information. We analyzed two travel time estimation models, the link travel model (LTM) and the flow speed model (FSM). They both shared the same performance when working with large intervals of time (120, 300, and 600 seconds), thus a higher level of aggregation, with an absolute average gap of 5.5% to the observed route travel time. With short periods (1, 10, 30, and 60 seconds), FSM performs better than LTM. For 1 second interval, FSM gives an average absolute gap of 6.70%, while LTM provides a gap of 11.17%. This thesis is structured as follows. After a general introduction in which we present the conceptual framework of the thesis and its organization, Chapter 1 presents the literature review for the two main problems of our development, the shortest path problem (SPP) and the VRP, and their time-dependent variants developed over the years. Chapter 2 introduces a new VRP variant, the TD-SPVRP. Chapter 3 presents the different techniques developed to reduce the size of the network by manipulating spatial information of the road network. The impact of these reductions is evaluated and analyzed on real data instances using multiple heuristics. Chapter 4 covers the impact of time aggregation data and travel time models when computing travel times on the precision of their estimations against observed travel times. The conclusion follows in the last chapter and presents some research perspectives for our works.
Vehicle Routing
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Author : Bruce L. Golden
language : en
Publisher: North Holland
Release Date : 1988
Vehicle Routing written by Bruce L. Golden and has been published by North Holland this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Business & Economics categories.
Hardbound. This collection of original research papers is dedicated to the analysis and solution of vehicle routing problems. Vehicle routing is a branch of operations research that has attracted a great deal of research attention over the years. The accumulated body of knowledge has resulted in algorithms and insights that have enabled companies and organizations in both the public and private sectors to save from 5% to 10% on distribution related costs. The volume brings together, in a comprehensive way, the theory and practice of vehicle routing in book form for the first time.
Vehicle Routing
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Author : Paolo Toth
language : en
Publisher: SIAM
Release Date : 2014-12-05
Vehicle Routing written by Paolo Toth and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-05 with Mathematics categories.
Vehicle routing problems, among the most studied in combinatorial optimization, arise in many practical contexts (freight distribution and collection, transportation, garbage collection, newspaper delivery, etc.). Operations researchers have made significant developments in the algorithms for their solution, and Vehicle Routing: Problems, Methods, and Applications, Second Edition reflects these advances. The text of the new edition is either completely new or significantly revised and provides extensive and complete state-of-the-art coverage of vehicle routing by those who have done most of the innovative research in the area; it emphasizes methodology related to specific classes of vehicle routing problems and, since vehicle routing is used as a benchmark for all new solution techniques, contains a complete overview of current solutions to combinatorial optimization problems. It also includes several chapters on important and emerging applications, such as disaster relief and green vehicle routing.
A Genetic Algorithm For The Vehicle Routing Problem With Time Dependent Travel Times
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Author : Soojung Jung
language : en
Publisher:
Release Date : 2000
A Genetic Algorithm For The Vehicle Routing Problem With Time Dependent Travel Times written by Soojung Jung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Genetic algorithms categories.
The Vehicle Routing Problem With Stochastic Travel Times
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Author : Gilbert Laporte
language : en
Publisher: Montréal : Centre for Research on Transportation = Centre de recherche sur les transports
Release Date : 1989
The Vehicle Routing Problem With Stochastic Travel Times written by Gilbert Laporte and has been published by Montréal : Centre for Research on Transportation = Centre de recherche sur les transports this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Delivery of goods categories.
Scatter Search
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Author : Manuel Laguna
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Scatter Search written by Manuel Laguna and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The book Scatter Search by Manuel Laguna and Rafael Martí represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but from the use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.