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.

/pdf/an-adaptive-large-neighborhood-search-heuristic-for-the-54a14dq2lk.pdf

An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.

The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.

The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.

The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.

- Covers the whole field of constraint programming
- Survey-style chapters
- Five chapters on applications

Table of Contents

Foreword (Ugo Montanari) 
Part I : Foundations
Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) 
Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) 
Chapter 3. Constraint Propagation (Christian Bessiere) 
Chapter 4. Backtracking Search Algorithms (Peter van Beek) 
Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) 
Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel)
Chapter 7. Tractable Structures for CSPs (Rina Dechter)
Chapter 8. The Complexity of Constraint Languages
(David Cohen, Peter Jeavons) 
Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) 
Chapter 10. Symmetry in Constraint Programming
(Ian P. Gent, Karen E. Petrie, Jean-Francois Puget)
Chapter 11. Modelling (Barbara M. Smith) 
Part II : Extensions, Languages, and Applications

Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) 
Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte)
Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) 
Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) 
Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) 
Chapter 17. Constraints over Structured Domains
(Carmen Gervet) 
Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh)
Chapter 19. Temporal CSPs (Manolis Koubarakis)
Chapter 20. Distributed Constraint Programming
(Boi Faltings) 
Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel)
Chapter 22. Constraint-Based Scheduling and Planning
(Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten)
Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) 
Chapter 24. Configuration (Ulrich Junker)
Chapter 25. Constraint Applications in Networks
(Helmut Simonis)
Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

/pdf/handbook-of-constraint-programming-53srutguo2.pdf

Handbook of Constraint Programming

Introduction to stochastic programming

https://www.mansci.ovgu.de/mansci_media/publikationen/2007/typology-EGOTEC-5t0pvr6fjifln4r4oav60tt612.pdf

An improved typology of cutting and packing problems

https://www.iro.umontreal.ca/~ferland/Seminars/Local_search/Pisinger,Ropke_ALNS_2007.pdf

A general heuristic for vehicle routing problems

Single liner shipping service design

In the three-dimensional bin packing problem the task is to orthogonally pack a given set of rectangular items into a minimum number of three-dimensional rectangular bins We give a characterization of the algorithm proposed by Martello et al (2000) for the exact solution of the problem, showing that not all orthogonal packings can be generated by the proposed algorithm The packings, however, have the property of being robot packings, which is relevant in practical settings References to the modified algorithm, which solves the orthogonal as well as robot packable three-dimensional problem, are given

https://pubsonline.informs.org/doi/pdf/10.1287/opre.1050.0210

Erratum to The Three-Dimensional Bin Packing Problem: Robot-Packable and Orthogonal Variants of Packing Problems

Dynamic programming is one of the fundamental techniques for solving optimization problems. In this paper we propose a general framework which can be used to decrease the time and space complexity of these algorithms with a logarithmic factor. The framework is based on word encoding, i.e. by representing subsolutions as bits in an integer. In this way word parallelism can be used in the evaluation of the dynamic programming recursion. Using this encoding the subset-sum problem can be solved in O( n b/ log b) time and O(b/ log b) space, where n is the number of integers given and b is the target sum. The knapsack problem can be solved in O( n m/ log m) time and O(m/ log m) space, where n is the number of items and m = max{b,z} is the maximum of the capacity b and the optimal solution value z . The problem of finding a path of a given length b in a directed acyclic graph G=(V,E) can be solved in O(|E|b/ log b) time and O(|V|b/ log b) space. Several other examples are given showing the generality of the achieved technique. Extensive computational experiments are provided to demonstrate that the achieved results are not only of theoretical interest but actually lead to algorithms which are up to two orders of magnitude faster than their predecessors. This is a surprising observation as the increase in speed is larger than the word size of the processor.

Dynamic Programming on the Word RAM

Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.

/pdf/optimization-in-liner-shipping-37ovo8ok1a.pdf

Optimization in liner shipping

The Bounded Knapsack Problem (BKP) is a generalization of the 0–1 Knapsack Problem where a bounded amount of each item type is available. The currently most efficient algorithm for BKP transforms the data instance to an equivalent 0–1 Knapsack Problem, which is solved efficiently through a specialized algorithm. However this paper demonstrates, that the transformation introduces many similar weighted items, resulting in very hard instances of the 0–1 Knapsack Problem. To avoid these problems, a specialized algorithm is proposed which solves an expanding core problem through dynamic programming, such that the number of enumerated item types is minimal. Sorting and reduction is done by need, resulting in very little effort for the preprocessing. Compared to other algorithms for BKP, the presented algorithm uses tighter reductions and enumerates considerably less item types. Computational experiments are presented, showing that the presented algorithm out-performs all previously published algorithms for BKP.

David Pisinger

Papers

Single liner shipping service design

Erratum to The Three-Dimensional Bin Packing Problem: Robot-Packable and Orthogonal Variants of Packing Problems

Dynamic Programming on the Word RAM

Optimization in liner shipping

A Minimal Algorithm for the Bounded Knapsack Problem