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The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Reduction. This paper presents the research results and computer simulations.

Petri nets are a useful tool for the modeling and performance evaluation of discrete event systems. Literature reveals that the Petri Net models of real-world discrete event systems are most frequently event graphs (a subclass of Petri nets). Literature also reveals that there are some simple methods for the performance evaluation of event graphs. The general-purpose Petri Net simulator (GPenSIM) is a new simulator that runs on the MATLAB platform. GPenSIM provides a Petri net language, with which Petri net classes and extensions can be developed. GPenSIM also provides functions for performance analysis. Since real-world discrete event systems usually possess a large number of resources, the Petri net models of these systems tend to become huge. Activity-Oriented Petri Nets (AOPN) is an approach that reduces the size of the Petri nets. In addition to the simulator functions, GPenSIM also realizes the AOPN approach on the MATLAB platform. Thus, AOPN is an integral part of GPenSIM. As a running example, a flexible manufacturing system is firstly modeled as an event graph, and then the size of the model is reduced with the AOPN approach. The advantages of GPenSIM and AOPN are discussed in this paper.

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The method for determining dispatching rules for transient phases, based on designation of a local dispatching rule for steady state, which determines the rest of its components has been presented in the paper. The algorithm for determining the transient rules has been presented and dependencies resulting from the presented sufficient conditions have been formulated. For the proposed transitional-rules generation algorithm the simulation model has been prepared. Simulation experiments to verify the correctness and effectiveness of the presented method have been conducted. The experiments have been prepared for input data describing the production system and the production order. In the verification process Enterprise Dynamics computer simulation system has been used. The results were compared with the results obtained for transitional phases realized through the S0 state of the production system.