A New Approach to Modeling and Solving Minimal Perturbation Problems

Formulation of many real-life problems evolves when the problem is being solved. For example, a change in the environment might appear after the initial problem specification and this change must be reflected in the solution. Such changes complicate usage of a traditionally static constraint satisfaction technology that requires the problem to be fully specified before the solving process starts. In this paper, we propose a new formal description of changes in the problem formulation called a minimal perturbation problem. This description focuses on the modification of the solution after a change in the problem specification. We also describe a new branch-and-bound like algorithm for solving such type of problems.

[1]  Mark Wallace,et al.  Minimal Perturbance in Dynamic Scheduling , 1998, ECAI.

[2]  Mats Carlsson,et al.  An Open-Ended Finite Domain Constraint Solver , 1997, PLILP.

[3]  Hana Rudová,et al.  Limited Assignment Number Search Algorithm , 2002 .

[4]  Stefan Voß,et al.  Meta-heuristics: The State of the Art , 2000, Local Search for Planning and Scheduling.

[5]  Hana Rudová,et al.  Soft CLP (FD) , 2003, FLAIRS.

[6]  Nico Roos,et al.  Fourth International Workshop on Integration of AI and OR techniques in Constraint Programming for Combinatorial Optimisation Problems , 2002 .

[7]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling IV , 2002, Lecture Notes in Computer Science.

[8]  Mark Wallace,et al.  Minimal Perturbation in Dynamic Scheduling , 1998 .

[9]  Rina Dechter,et al.  Belief Maintenance in Dynamic Constraint Networks , 1988, AAAI.

[10]  Yongping Ran Approaches to Find a Near-minimal Change Solution for Dynamic CSPs , 2002 .

[11]  Waldemar Kocjan Dynamic scheduling. State of the art report. , 2002 .

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

[13]  Eugene C. Freuder,et al.  Partial Constraint Satisfaction , 1989, IJCAI.

[14]  Matthew L. Ginsberg,et al.  Limited Discrepancy Search , 1995, IJCAI.

[15]  Hana Rudová,et al.  University Course Timetabling with Soft Constraints , 2002, PATAT.

[16]  Nico Roos,et al.  Approaches to find a near-minimal change solution for Dynamic CSPs , 2002 .

[17]  Roman Barták,et al.  Minimal Perturbation Problem - A Formal View , 2003 .

[18]  W. D. Harvey,et al.  Nonsystematic backtracking search , 1995 .

[19]  Alexander Nareyek,et al.  Local Search for Planning and Scheduling , 2001, Lecture Notes in Computer Science.

[20]  Mark Wallace,et al.  Probe Backtrack Search for Minimal Perturbation in Dynamic Scheduling , 2000, Constraints.