Reactive Real-time Scheduling Using Simulation-Optimization and Evolutionary Algorithms
Engelbert Pasieka, Sebastian Engell · 2024
Industrial scheduling is an important task that involves the allocation of the orders to resources and the sequencing and timing of the operations to meet delivery dates and to improve the performance of the production system. Most of the research in this area addresses static or offline scheduling problems, i.e. solving scheduling problems for a given set of resources, orders, and due dates, and focuses on the efficiency of the solution process. However, in industrial operations each schedule is outdated shortly after it has been computed due to unforeseen events, e.g. new orders or due dates, lack of materials, maintenance, etc. Often the precomputed schedule does not meet the constraints any more or cannot be executed at all because resources are (temporarily) not available. Then in practice manual re-scheduling takes place. In this contribution we discuss how a static scheduling system can be redesigned for use in dynamic situations, i.e. for reactive scheduling.Our approach builds on previous work on production planning using simulation-optimization. It combines a discrete-event simulator with a tailored evolutionary algorithm to implement a dynamic adaptation of the schedules to the available information. The simulation model is continuously updated with the latest information about the state of the plant and of the orders. At the beginning of a cycle the evolutionary algorithm evaluates all schedules of the current population with this model and then improves them. We demonstrate our solution for a pharmaceutical batch plant example with a high combinatorial complexity. Our study demonstrates that the simulation-optimization framework can effectively handle unforeseen events and provides good solutions fast.