A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning
Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria map be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous easter units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant. (C) 1998 Elsevier Science Inc. All rights reserved.
combinatorial optimization shift scheduling iterative improvement multiple criteria decision making scheduling under uncertainty knowledge acquisition knowledge base consistency steel making