Data-Based Identification Method for Jobshop Scheduling Problems Using Timed Petri Nets

Abstract

We address a data-based identification method of machine scheduling problems using timed Petri nets. A general machine scheduling model is represented by timed Petri nets with resource places. Given a set of machines and jobs, and their starting times and completion times of several machines, the objective is to find resource constraints of a given machine scheduling problem from input and output data. The problem is to find the connectivity of each resource place in the operational places. A mixed integer linear programming model is formulated to find an optimal connectivity of resource places to minimize the mean square error of the input and output data. An approximation algorithm is developed to apply larger instances. Numerical examples are provided to show the effectiveness of the proposed approximation algorithm.