A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

TL;DR

This paper introduces StarFLIP++, a C++ software library combining approximate reasoning and iterative heuristics to effectively model and solve complex, real-world combinatorial optimization problems with uncertain data and vague constraints.

Contribution

The paper presents a reusable, modular software framework that integrates approximate reasoning and iterative heuristics for solving complex combinatorial problems, demonstrated through industrial scheduling applications.

Findings

Successfully applied to steel plant scheduling

Reused components in workforce shift scheduling

Demonstrated flexibility and effectiveness of the library

Abstract

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 may 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 caster 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.