We present a reachability graph-based search optimization tool for scheduling.Motivated by the lack of tool support for optimization of TCPNs.Implements an event-driven timed state space with AI heuristic search algorithms.Aimed at supporting flexible decision making process with algorithm portfolio.Comparative study of nine search algorithms on real system demonstrates tool efficiency. The combination of Petri net (PN) modeling with AI-based heuristic search (HS) algorithms (PNHS) has been successfully applied as an integrated approach to deal with scheduling problems that can be transformed into a search problem in the reachability graph. While several efficient HS algorithms have been proposed albeit using timed PN, the practical application of these algorithms requires an appropriate tool to facilitate its development and analysis. However, there is a lack of tool support for the optimization of timed colored PN (TCPN) models based on the PNHS approach for schedule generation. Because of its complex data structure, TCPN-based scheduling has often been limited to simulation-based performance analysis only. Also, it is quite difficult to evaluate the strength and tractability of algorithms for different scheduling scenarios due to the different computing platforms, programming languages and data structures employed. In this light, this paper presents a new tool called TIMSPAT, developed to overcome the shortcomings of existing tools. Some features that distinguish this tool are the collection of several HS algorithms, XML-based model integration, the event-driven exploration of the timed state space including its condensed variant, localized enabling of transitions, the introduction of static place, and the easy-to-use syntax statements. The tool is easily extensible and can be integrated as a component into existing PN simulators and software environments. A comparative study is performed on a real-world eyeglass production system to demonstrate the application of the tool for scheduling purposes.

Peer Reviewed