CP-based column generation
       Application        Reference
       Urban transit      T.H. Yunes., A.V. Moura, C.C. de Souza. Solving very large crew scheduling problems to optimality. 
       crew               Proceedings of ACM symposium on Applied Computing, pages 446-451, 2000.
       management
                          T.H. Yunes., A.V. Moura, C.C. de Souza. Hybrid column generation approaches for urban transit 
                          crew management problems. Transportation Science 39(2):273-288, 2005.
       Travelling         K. Easton, G.L. Nemhauser, and M.A. Trick. Solving the travelling tournament problem: A combined 
       tournament         integer programming and constraint programming approach. Proceedings of Practice and Theory of 
                          Automated Timetabling, volume 2740 of Lecture Notes in Computer Science, pages 100-112. 
                          Springer, 2002.
       Two-dimensional    D. Pisinger, M. Sigurd. Using decomposition techniques and constraint programming for solving the 
       bin packing        two-dimensional bin-packing problem. Journal on Computing 19(1):36-51, 2007.
       Graph coloring     S. Gualandi. Enhancing constraint programming-based column generation for integer programs. 
                          PhD thesis, Politechnico di Milano, 2008.
       Constrained        T. Fahle, M. Sellmann. Cost based filtering for the constrained knapsack problem. Annals of 
       cutting stock      Operations Research 115(1):73-93, 2002.
       Employee           S. Demassey, G. Pesant, L.M. Rousseau. A cost-regular based hybrid column generation approach. 
       timetabling        Constraints 11(4):315-333, 2006.
       Wireless mesh      A. Capone, G. Carello, I. Filippini, S. Gualandi, F. Malucelli. Solving a resource allocation problem in 
       networks           wireless mess networks: A comparison between a CP-based and a classical column generation. 
                          Networks 55(3):221-233, 2010.
       Multi-machine      R. Sadykov, L.A. Wolsey. Integer programming and constraint programming in solving a 
       scheduling         multimachine assignment scheduling problem with deadlines and release dates. Journal on 
                          Computing 18(2):209-217, 2006.
       CP-based column generation
       Application        Reference
       Airline crew       U. Junker, S.E. Karisch, N. Kohl, B. Vaaben, T. Fahle, M. Sellmann. A framework for constraint 
       assignment         programming based column generation. Proceedings of Principles and Practice of Constraint 
                          Programming, volume 1713 of Lecture Notes in Computer Science, pages 261-274, 1999.
                          T. Fahle, U. Junker, S.E. Karisch, N. Kohl, M. Sellmann, B. Vaaben. Constraint programming based 
                          column generation for crew assignment. Journal of Hueristics 8(1):59-81, 2002.
                          M. Sellmann, K. Zervoudakis, P. Stamatopoulos, T. Fahle. Crew assignment via constraint 
                          programming: integrating column generation and heuristic tree search. Annals of Operations 
                          Research 115(1):207-225, 2002.
       Vehicle routing    L.M. Rousseau. Stabilization issues for constraint programming based column generation. 
       with time          Proceedings of Integration of AI and OR techniques in CP for Combinatorial Optimization, volume 
       windows            3011 of Lecture notes in Computer Science, pages 402-408. Springer, 2004.
                          L.M. Rousseau, M. Gendreau, G. Pesant, F. Focacci. Solving VRPTWs with constraint 
                          programming based column generation. Annals of Operations Research 130(1):199-216, 2004.
       CP-based Benders decomposition
       Application        Reference
       Parallel machine   V. Jain, I.E. Grossmann. Algorithms for hybrid MILP/CP models for a class of optimization problems. 
       scheduling         INFORMS Journal on Computing 13(4):258-276, 2001.
       Polypropylene      C. Timpe. Solving planning and scheduling problems with combined integer and constraint 
       batch scheduling   programming. OR Spectrum 24(4):431-448, 2002.
       Call center        T. Benoist, E. Gaudin, B. Rottembourg. Constraint programming contribution to Benders 
       scheduling         decomposition: A case study. Principles and Practice of Constraint Programming, volume 2470 of 
                          Lecture Notes in Computer Science, pages 603-617. Springer, 2002.
       Multi-machine      J.N. Hooker. A hybrid method for planning and scheduling. Principles and Practice of Constraint 
       scheduling         Programming, volume 3258 of Lecture Notes in Computer Science, pages 305-316. Springer, 2004.
                          J.N. Hooker. Planning and scheduling to minimize tardiness. Principles and Practice of Constraint 
                          Programming, volume 3709 of Lecture Notes in Computer Science, pages 314-327. Springer, 2005.
     CP versus IP
                                CP                IP
              Variables         Finite domain     Continuous, 
                                                  Binary, Integer
              Constraints       Symbolic:         Linear,
                                alldifferent      algebraic:
                                cumulative        (+, –, *, =, ≤, ≥)
              Inference         Constraint        LP relaxation
                                propagation
                                     Local           Global
                                   Feasible          Optimal
     CP versus IP
     •  “MILP is very efficient when the relaxation is tight and 
        models have a structure that can be effectively exploited”
     •  “CP works better for highly constrained discrete 
        optimization problems where expressiveness of MILP is a 
        major limitation”
     •  “From the work that has been performed, it is not clear 
        whether a general integration strategy will always perform 
        better than either CP or an MILP approach by itself. This 
        is especially true for the cases where one of these 
        methods is a very good tool to solve the problem at hand. 
        However, it is usually possible to enhance the 
        performance of one approach by borrowing some ideas 
        from the other”
         – Source: Jain and Grossmann, 2001