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)

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Handbook of Constraint Programming

http://www3.eng.cam.ac.uk/~hj231/Papers/rostering.pdf

Staff scheduling and rostering: A review of applications, methods and models

Dantzig-Wolfe decomposition and column generation, devised for linear programs, is a success story in large-scale integer programming. We outline and relate the approaches, and survey mainly recent contributions, not yet found in textbooks. We emphasize the growing understanding of the dual point of view, which has brought considerable progress to the column generation theory and practice. It stimulated careful initializations, sophisticated solution techniques for the restricted master problem and subproblem, as well as better overall performance. Thus, the dual perspective is an ever recurring concept in our "selected topics."

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Selected Topics in Column Generation

In most vehicle routing and crew scheduling applications solved by column generation, the subproblem corresponds to a shortest path problem with resource constraints (SPPRC) or one of its variants.

Shortest Path Problems with Resource Constraints

After discussing the nature of improvisation and the unique challenges and dangers implicit in the learning task that jazz improvisers create for themselves, the author broadly outlines seven characteristics that allow jazz bands to improvise coherently and maximize social innovation in a coordinated fashion. He also draws on his own experience as a jazz pianist. Finally, implications for organizational design and managing for learning are suggested.

https://www.jstor.org/stable/info/2640303

Coda : Creativity and Improvisation in Jazzand Organizations: Implications Fororganizational Learning

This paper presents the development of new elimination tests which greatly enhance the performance of a relatively well established dynamic programming approach and its application to the minimization of the total traveling cost for the traveling salesman problem with time windows. The tests take advantage of the time window constraints to significantly reduce the state space and the number of state transitions. These reductions are performed both a priori and during the execution of the algorithm. The approach does not experience problems stemming from increasing problem size, wider or overlapping time windows, or an increasing number of states nearly as rapidly as other methods. Our computational results indicate that the algorithm was successful in solving problems with up to 200 nodes and fairly wide time windows. When the density of the nodes in the geographical region was kept constant as the problem size was increased, the algorithm was capable of solving problems with up to 800 nodes. For these problems, the CPU time increased linearly with problem size. These problem sizes are much larger than those of problems previously reported in the literature.

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An Optimal Algorithm for the Traveling Salesman Problem with Time Windows

This paper describes the Preferential Bidding Problem solved in the airline industry to construct personalized monthly schedules for pilots and officers. This problem consists in assigning to crew members pairings, days off, annual leaves, training periods, etc., while considering a set of weighted bids that reflect individual preferences. This assignment must be done under strict seniority restrictions: the construction of a maximum-score schedule for a particular crew member must never be done at the expense of a more senior employee. This research and development project has resulted in the Preferential Bidding System that has been used at Air Canada since May 1995. The solution process is summarized as follows. For each employee, from the most senior to the most junior, a so-called residual problem is solved: given an employee and a set of unassigned pairings, the solution to an integer linear program determines the employee's maximum-score schedule while taking into account all the remaining employees. The residual problem is solved by column generation embedded in a branch-and-bound tree. Integer solutions are obtained by using very efficient cutting planes, without which it would have been impossible to solve some of these residual problems.

The Preferential Bidding System at Air Canada

This paper presents an optimal dynamic programming algorithm, the first such algorithm in the literature to solve the shortest path problem with time windows and additional linear costs on the node service start times. To optimally solve this problem, we propose a new dynamic programming algorithm which takes into account the linear node costs. This problem has numerous applications: Two examples are job-shop scheduling and aircraft routing and scheduling. To underline the efficiency of the proposed method, we compare it with an approach based on partial discretization of the time windows. It clearly outperformed the discretization approach on test problems with wide time windows and many nodes with negative costs. q 1998 John Wiley & Sons, Inc. Networks 31: 193–204, 1998

A Dynamic Programming Algorithm for the Shortest Path Problem with Time Windows and Linear Node Costs

Jazz musician Ken Peplowski gives his view why managers can learn from jazz music, because a jazz band is “kind of like a corporation.”

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Eric Gelinas

Papers

An Optimal Algorithm for the Traveling Salesman Problem with Time Windows

The Preferential Bidding System at Air Canada

A Dynamic Programming Algorithm for the Shortest Path Problem with Time Windows and Linear Node Costs

The Process of Improvisation

The Preferential Bidding System at Air Canada