Benders decomposition in Constraint Programming

Recent work have exhibited specific structure among combinatorial problem instances that could be used to speed up search or to help users understand the dynamic and static intimate structure of the problem being solved. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. This paper presents how Benders decomposition could be adapted to constraint programming when specific relationships between variables are exhibited. It discusses the way a decomposition framework could be embedded in constraint solvers, taking advantage of structures for a non expert user in a generic way. To achieve the interaction between structures, it explores the possibility of deriving logic Benders cuts using an explanation based framework for Constraint Programming.

[1]  Vipul Jain,et al.  Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems , 2001, INFORMS J. Comput..

[2]  Jacques F. Benders,et al.  Partitioning procedures for solving mixed-variables programming problems , 2005, Comput. Manag. Sci..

[3]  Erlendur S. Thorsteinsson Branch-and-Check: A Hybrid Framework Integrating Mixed Integer Programming and Constraint Logic Programming , 2001, CP.

[4]  Yannis C. Stamatiou,et al.  Random Constraint Satisfaction a More Accurate Picture , 2022 .

[5]  J. Hooker,et al.  Logic-based Benders decomposition , 2003 .

[6]  Thierry Benoist,et al.  Constraint Programming Contribution to Benders Decomposition: A Case Study , 2002, CP.

[7]  Bart Selman,et al.  Backdoors To Typical Case Complexity , 2003, IJCAI.

[8]  Narendra Jussien,et al.  Local search with constraint propagation and conflict-based heuristics , 2000, Artif. Intell..

[9]  Narendra Jussien,et al.  The versatility of using explanations within constraint programming , 2003 .

[10]  Hadrien Cambazard,et al.  Decomposition and Learning for a Hard Real Time Task Allocation Problem , 2004, CP.

[11]  Toby Walsh,et al.  Scenario-based Stochastic Constraint Programming , 2009, IJCAI.

[12]  Henri Beringer,et al.  Intelligent Backtracking for CLP Languages: An Application to CLP(R) , 1991, ISLP.

[13]  Hadrien Cambazard,et al.  Identifying and Exploiting Problem Structures Using Explanation-Based Constraint Programming , 2005, CPAIOR.

[14]  Ulrich Junker Conflict Detection for Arbitrary Constraint Propagation Algorithms , 2001 .

[15]  Narendra Jussien,et al.  The PaLM system: explanation-based constraint programming , 2000 .

[16]  John N. Hooker,et al.  Optimization and , 2000 .