This paper develops an adaptive scheduling policy for dynamic manufacturing systems. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. The inductive learning methodology used for constructing this state-dependent scheduling policy also provides an understanding of the relative importance of the various system parameters in determining the appropriate dispatching rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tight due date jobs and machine breakdowns - two of the most common sources of disruptions in most manufacturing systems. From an operational perspective, the most important characteristics of the pattern-directed scheduling (PDS) approach are its ability to incorporate the idiosyncratic characteristics of the given system into the dispatching rule selection process, and its ability to refine itself incrementally on a continuing basis by taking new system parameters into account.
- Adaptive scheduling
- Dispatching rule selection
- Dynamic manufacturing system
- Inductive learning
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering