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Today's highly competitive business environment makes it an absolute requirement on behalf of the managers to continuously make the best decisions in the shortest possible time. ‘Learning from mistakes’ has left its place to ‘one strike and you're out' reality. That is, there is no room for mistake in making decisions in this global environment. Success depends on quickly allocating the organizational resources towards meeting the actual needs and requirements of the customer. Decision problems involve various numeric and non-numeric constraints, some of which are conflicting with each other. Occasionally, decision-makers do not have complete information on the situation. Thus they perform ‘what-if’ and goalseeking analyses involving constraints. In order to succeed in such an unforgiving environment, managers and decision makers need integrated 'intelligent' decision support systems (DSS) that are capable of using a wide variety of models along with data and information resources available to them at various internal and external repositories. In this chapter we present the use of constraint logic programming as a tool for such decision support systems in constrained search problems, focusing on the model representation and analyses. The original contribution of our approach consists of a declarative framework for constrained search problems, developed within the constraint logic programming (CLP) paradigm together with relational SQL database, and the development of a constraint logic solver for scheduling problems with external resources and resource dependent processing times in different production organization environments.

A Declarative Framework for Constrained Search Problems in Manufacturing