Removing propagation redundant constraints in redundant modeling
ACM Transactions on Computational Logic (TOCL)
Finite domain bounds consistency revisited
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Salinity is the relative concentration of salts in water. In a city of southern China, the local water supply company pumps water from a nearby river for potable use. During the winter dry season, the intrusion of sea water raises the salinity of the river to a high level and affects approximately the daily life of 450,000 residents of the city. This paper reports the application of constraint programming (CP) to optimize the logistical operations of the raw water system so as to satisfy the daily water consumption requirement of the city and to keep the potable salinity below a desirable level for as many days as possible. CP is the key to the success of the project for its separation of concerns and powerful constraint language that allows for rapid construction of a functional prototype and production system. Flexibility and adaptiveness allow us to deal with our clients' many changes in the requirements. Deriving good variable and value ordering heuristics, and generating useful implied constraints, we demonstrate that branch-and-bound search with constraint propagation can cope with an optimization problem of large size and great difficulty.