A methodology for knowledgebased scheduling decision support

The problem of job shop scheduling requires satisfaction of diverse and conflicting constraints. A large body of scheduling algorithms exists. The search for an appropriate algorithm or model can be very frustrating as well as discouraging for the prospective user, due to the complexity and the costs involved. Expert help may not always be affordable or accessible at the right moment. Knowledgebased Model Management Systems (MMS) enable us to make the expertise more accessible and affordable. In this paper we develop Scheduling Assistant, an interactive prototype knowledgebased MMS written in VAX OPS5 that displays a methodology for knowledgebased job shop scheduling decision support. It makes the search for expert scheduling knowledge easier, efficient, and more accessible to the practitioner user.