Combining Local Search and Constraint Propagation to find a minimal change solution for a Dynamic CSP

Many hard practical problems such as Time Tabling and Scheduling can be formulated as Constraint Satisfaction Problems. For these CSPs, powerful problem-solving methods are available. However, in practice, the problem definition may change over time. Each separate change may invoke a new CSP formulation. The resulting sequence of CSPs is denoted as a Dynamic CSP. A valid solution of one CSP in the sequence need not be a solution of the next CSP. Hence it might be necessary to solve every CSP in the sequence forming a DCSP. Successive solutions of the sequence of CSPs can differ quite considerably. In practical environments large differences between successive solutions are often undesirable. To cope with this hin- drance, the paper proposes a repair-based algorithm, i.e., a Local Search algorithm that systematically searches the neighborhood of an infringed solution to find a new nearby solution. The algorithm combines local search with constraint propagation to reduce its time complexity.