Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop

Abstract

A vital component of modern manufacturing systems is the scheduling and control system, which determines companies' overall performance in their respective supply chains. This paper studies iterative simulation-based scheduling mechanisms for manufacturing systems that operate in dynamic and stochastic environments. Also assessed are the issues involved when these mechanisms are used to make higher-level scheduling decisions, such as dispatching rule selection, instead of generation of a full schedule. A typical simulation-based system is outlined and tested under various experimental conditions. Examined are the effects of stochastic events such as machine breakdowns and processing time variations on the system performance, and the effectiveness of the simulation-based approach from the control point of view is evaluated. Finally, different levels of two important factors (look-ahead window and scheduling period) are compared for the iterative approach. Computational results show that, although simulation-based scheduling proves effective when these parameters are properly set, the overall performance diminishes due to the dynamic and stochastic nature of the system, which degrades the multi-pass improvement capability of the simulation runs. Experimental results also support the initial expectation in that frequent updates to the higher-level schedule may not be necessary when these decisions are naturally "adaptive" to the unexpected system changes.