@inproceedings{4f424399cf8b447ba7987034e092e574,

title = "Combining Local Search and Constraint Propagation to find a minimal change solution for a Dynamic CSP",

abstract = "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.",

author = "N. Roos and Y.P. Ran and Herik, {H.J. van den}",

year = "2000",

doi = "10.1007/3-540-45331-8_26",

language = "English",

isbn = "978-3-540-41044-7",

series = "Lecture Notes in Artificial Intelligence",

publisher = "Springer, Berlin, Heidelberg",

pages = "272--282",

booktitle = "Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2000",

address = "Germany",

}