Traditional research on dynamic job shop scheduling (DJSS) is largely based on combinatorial analysis. Unfortunately, the NP-complete nature of the problem forces many assumptions that limit the use of analytical methods in practical problem solving to be included in existing solution procedures. Customer and job priorities, based on historical data, are seldom included in scheduling models. Many complexities in solving scheduling problems can be reduced if restrictions on limiting resources can be modified in consultation with the decision maker. Such changes can alter the feasible solution space, and permit sound, profitable decisions to enter into the model. This technique is not practicable in a traditional mathematical approach to obtain a solution for the scheduling problem. In this paper, we present a decision support system for real time scheduling that not only allows changes in limiting resources but also permits planned and unplanned downtime of machines, customer order changes and utilization of equipment with multiple capabilities. Expert systems and solicited user input are used to modify resource constraints. This system combines the customer value, job value and the potential value of job and customer along with a traditional component of scheduling analysis to create a schedule. The impact of this system on classical job shop scheduling is also discussed.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modeling and Simulation
- Management Science and Operations Research