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

Repositioning of empty containers pose a significant cost in the shipping industry due to the large difference in export and import between some parts of the world, e.g., North America and Asia. Dejax and Crainic [9] estimate, that movement of empty containers comprise up to 40% of all container movements. This paper presents a revenue management model for a liner shipping company where the repositioning of empty containers is taken into account. The aim is to maximize the profit of transported cargo in a network, subject to the cost and availability of empty containers. The model is an augmented multi-commodity flow problem with additional inter-balancing constraints to control repositioning of empty containers. An arc flow formulation is Dantzig-Wolfe decomposed to a path flow formulation, where the LP relaxation is solved with a delayed column generation algorithm. A feasible IP solution is hereafter found by rounding down the LP solution and adjusting flow balance constraints with leased containers. Computational results are reported for eight instances based on real-life shipping networks. Solving the LP relaxed path flow model with a column generation algorithm outperforms solving the LP relaxed arc flow model with the CPLEX barrier solver even for very small instances. The proposed algorithm is able to solve instances with 234 ports, and 293 vessels for 9 time periods in 34 minutes. The integer solutions found by rounding are computed in less than 5 seconds and the gap is within 0.01% of the LP upper bound, which is assumed to be below the level of uncertainty of the input data. The solved instances are quite large compared to computational results in the reviewed literature on models for empty container repositioning.

Liner Shipping Revenue Management with Respositioning of Empty Containers

Baggage operations at an airport are split into several different sub problems. The problem of assigning baggage carousels to each arriving flight is part of the important passenger arrival process. This article presents a mixed integer formulation for the underlying decision problem which aims at balancing customer satisfaction with operational needs. We introduce a static model that fulfills the different real-world requirements and balances the different objective criteria. It is shown how the model can be used in the dynamic environment of an airport. Because of the high degree of uncertainty at an airport, the issue of stability is discussed in detail. In a case study several scenarios with different time horizons and times of decision were studied. The model was verified with data from Frankfurt Airport and is successfully running as part of the decision support system at Frankfurt Airport.

https://link.springer.com/content/pdf/10.1007/s43069-020-00040-1.pdf

Baggage Carousel Assignment at Airports: Model and Case Study

We consider the second-order conic equivalent of the classic knapsack polytope where the variables are subject to generalized upper bound constraints. We describe and compare a number of separation and extension algorithms which make use of the extra structure implied by the generalized upper bound constraints in order to strengthen the second-order conic equivalent of the classic cover cuts. We show that determining whether a cover can be extended with a variable is NP-hard. Computational experiments are performed comparing the proposed separation and extension algorithms. These experiments show that applying these extended cover cuts can greatly improve solution time of second-order cone programs.

/pdf/separation-and-extension-of-cover-inequalities-for-second-4gmqsgs1wc.pdf

Separation and extension of cover inequalities for second-order conic knapsack constraints with GUBs

A major cost in semiconductor manufacturing is the generation of photo masks which are used to produce the dies. When producing smaller series of chips it can be advantageous to build a shuttle mask (or multi-project wafer) to share the startup costs by placing different dies on the same mask. The shuttle layout problem is frequently solved in two phases: first, a floorplan of the shuttle is generated. Then, a cutting plan is found which minimizes the overall number of wafers needed to satisfy the demand of each die type. Since some die types require special production technologies, only compatible dies can be cut from a given wafer, and each cutting plan must respect various constraints on where the cuts may be placed. We present an exact algorithm for solving the minimum cutting plan problem, given a floorplan of the dies. The algorithm is based on delayed column generation, where the pricing problem becomes a maximum vertex-weighted clique problem in which each clique consists of cutting compatible dies. The resulting branch-and-price algorithm is able to solve realistic cutting problems to optimality in a couple of seconds.

David Pisinger

Papers

Liner Shipping Revenue Management with Respositioning of Empty Containers

Baggage Carousel Assignment at Airports: Model and Case Study

Separation and extension of cover inequalities for second-order conic knapsack constraints with GUBs

Optimal wafer cutting in shuttle layout problems

Avoiding anomalies in the mt2 algorithm by Martello and Toth