The Scheduling Job-Set Optimization Problem: A Model-Based Diagnosis Approach
This work addresses scheduling optimization for companies facing capacity constraints, but it is incremental as it adapts existing model-based diagnosis methods to a new problem domain.
The paper tackles the problem of selecting which product orders to discard or postpone when demand exceeds production capacity, using model-based diagnosis, and demonstrates that one of the two formalized problems can be effectively addressed with existing tools on industrial-scale benchmarks.
A common issue for companies is that the volume of product orders may at times exceed the production capacity. We formally introduce two novel problems dealing with the question which orders to discard or postpone in order to meet certain (timeliness) goals, and try to approach them by means of model-based diagnosis. In thorough analyses, we identify many similarities of the introduced problems to diagnosis problems, but also reveal crucial idiosyncracies and outline ways to handle or leverage them. Finally, a proof-of-concept evaluation on industrial-scale problem instances from a well-known scheduling benchmark suite demonstrates that one of the two formalized problems can be well attacked by out-of-the-box model-based diagnosis tools.