An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows
TL;DR: This paper presents a heuristic for the pickup and delivery problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows that is very robust and is able to adapt to various instance characteristics.
Abstract: The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.
This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.
The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.
The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.
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TL;DR: A unified heuristic which is able to solve five different variants of the vehicle routing problem and shown promising results for a large class of vehicle routing problems with backhauls as demonstrated in Ropke and Pisinger.
1,282 citations
Cites background or methods from "An Adaptive Large Neighborhood Sear..."
...Ropke and Pisinger [3] used a modified version of this algorithm in the VRP for splitting requests on a route into two strongly connected subsets....
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...For additional detail on the two-stage algorithm see [3]....
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...The conversions which will be described in the following paragraphs are extensions of the transformations presented by Ropke and Pisinger [50] for solving VRP problems with Backhauls....
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...Finally, one may use history-based removal where the q variables are chosen according to some historical information as presented in [3]....
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...We refer to [3] for additional details....
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01 Jun 2008
TL;DR: Single as well as multi vehicle mathematical problem formulations for all three VRPPD types are given, and the respective exact, heuristic, and metaheuristic solution methods are discussed.
Abstract: This paper is the second part of a comprehensive survey on pickup and delivery models. Basically, two problem classes can be distinguished. The first part dealt with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. In this class four subtypes were considered, namely the Vehicle Routing Problem with Clustered Backhauls (VRPCB all linehauls before backhauls), the Vehicle Routing Problem with Mixed linehauls and Backhauls (VRPMB any sequence of linehauls and backhauls permitted), the Vehicle Routing Problem with Divisible Delivery and Pickup (VRPDDP customers demanding delivery and pickup service can be visited twice), and the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP customers demanding both services have to be visited exactly once). The second part now considers all those problems where goods are transported between pickup and delivery locations, denoted as Vehicle Routing Problems with Pickups and Deliveries (VRPPD). These are the Pickup and Delivery VRP (PDVRP unpaired pickup and delivery points), the classical Pickup and Delivery Problem (PDP paired pickup and delivery points), and the Dial-A-Ride Problem (DARP paired pickup and delivery points and user inconvenience taken into consideration). A single as well as a multi vehicle mathematical problem formulation for all three VRPPD types is given, and the respective exact, heuristic, and metaheuristic solution methods are discussed.
703 citations
Cites background from "An Adaptive Large Neighborhood Sear..."
...* Ropke and Pisinger (2006a) MD, S min....
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TL;DR: This work introduces the electric vehicle-routing problem with time windows and recharging stations E-VRPTW and presents a hybrid heuristic that combines a variable neighborhood search algorithm with a tabu search heuristic, which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme.
Abstract: Driven by new laws and regulations concerning the emission of greenhouse gases, carriers are starting to use electric vehicles for last-mile deliveries. The limited battery capacities of these vehicles necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning to avoid inefficient vehicle routes with long detours. We introduce the electric vehicle-routing problem with time windows and recharging stations E-VRPTW, which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme. Furthermore, we consider limited vehicle freight capacities as well as customer time windows, which are the most important constraints in real-world logistics applications. As a solution method, we present a hybrid heuristic that combines a variable neighborhood search algorithm with a tabu search heuristic. Tests performed on newly designed instances for the E-VRPTW as well as on benchmark instances of related problems demonstrate the high performance of the heuristic proposed as well as the positive effect of the hybridization.
695 citations
Cites methods from "An Adaptive Large Neighborhood Sear..."
...The ratio of VNS iterations to TS iterations and all other parameters were tuned in a systematic fashion following the approach described in Ropke and Pisinger (2006). We start from a base parameter setting that we found during the development of our VNS/TS and then tune the parameters consecutively, always keeping the best setting found for a parameter and proceeding with the next one....
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TL;DR: A general framework to model a large collection of pickup and delivery problems, as well as a three-field classification scheme for these problems, is introduced.
Abstract: Pickup and delivery problems constitute an important class of vehicle routing problems in which objects or people have to be collected and distributed. This paper introduces a general framework to model a large collection of pickup and delivery problems, as well as a three-field classification scheme for these problems. It surveys the methods used for solving them.
685 citations
Cites methods from "An Adaptive Large Neighborhood Sear..."
...Another LNS heuristic for the VRP-PDTW was later proposed by Ropke and Pisinger (2006a) ....
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01 Sep 2011
TL;DR: A survey of some of the most important lines of hybridization of metaheuristics with other techniques for optimization, which includes, for example, the combination of exact algorithms and meta heuristics.
Abstract: Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples.
684 citations
Cites background from "An Adaptive Large Neighborhood Sear..."
...Further special MIP-based neighborhoods have, for example, been described by Oncan et al. [108] for partitioning problems, Ropke and Pisinger [109] for pickup and delivery problems with time windows, and Pirkwieser and Raidl [110] for a Periodic LocationRouting Problem....
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...[108] for partitioning problems, Ropke and Pisinger [109] for pickup and delivery problems with time windows, and Pirkwieser and Raidl [110] for a Periodic LocationRouting Problem....
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References
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Book•
01 Jan 1990TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Abstract: From the Publisher:
The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Like the first edition,this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects.
In its new edition,Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity,and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.
As in the classic first edition,this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further,the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.
Each chapter presents an algorithm,a design technique,an application area,or a related topic. The chapters are not dependent on one another,so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally,the new edition offers a 25% increase over the first edition in the number of problems,giving the book 155 problems and over 900 exercises thatreinforcethe concepts the students are learning.
21,651 citations
"An Adaptive Large Neighborhood Sear..." refers methods in this paper
...Notice that the sorting in line 7 can be avoided in an actual implementation of the algorithm as it is sufficient to use a linear time selection algorithm [ 6 ] in line 9....
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TL;DR: This chapter presents the basic schemes of VNS and some of its extensions, and presents five families of applications in which VNS has proven to be very successful.
3,572 citations
"An Adaptive Large Neighborhood Sear..." refers methods in this paper
...The related Variable Neighborhood Search (VNS) was proposed by Mladenovi´ c and Hansen [ 14 ]....
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01 Jan 2001
TL;DR: In this paper, the authors present a comprehensive overview of the most important techniques proposed for the solution of hard combinatorial problems in the area of vehicle routing problems, focusing on a specific family of problems.
Abstract: The Vehicle Routing Problem covers both exact and heuristic methods developed for the VRP and some of its main variants, emphasizing the practical issues common to VRP. The book is composed of three parts containing contributions from well-known experts. The first part covers basic VRP, known more commonly as capacitated VRP. The second part covers three main variants of VRP with time windows, backhauls, and pickup and delivery. The third part covers issues arising in real-world VRP applications and includes both case studies and references to software packages. The book will be of interest to both researchers and graduate-level students in the communities of operations research and matematical sciences. It focuses on a specific family of problems while offering a complete overview of the effective use of the most important techniques proposed for the solution of hard combinatorial problems. Practitioners will find this book particularly usef
3,395 citations