Scheduling of semiconductor test facility via Petri nets and hybrid heuristic search

Abstract

This paper proposes and evaluates two Petri netbased hybrid heuristic search strategies and their applications to semiconductor test facility scheduling. To reduce the setup time, such as the time spent to bring the test facilities to the required temperatures, scheduling multiple lots for each job type together is desirable. Petri nets can concisely model multiple lot sizes for each job, the strict precedence constraints, multiple kinds of resources, concurrent activities and flexible routes. To cope with the complexities for multiple lots scheduling, this paper presents two Petri net-based hybrid heuristic search strategies. They combine the heuristic best-first strategy with the controlled backtracking strategy based on the execution of the Petri nets. The obtained scheduling results are compared and analyzed through a small-size test facility. The better algorithm is also applied to a more sizable facility containing types of resources with a total of 79 pieces and 30 jobs. The future work includes the real-time implementation of the proposed method and scheduling results in real industrial settings. Index Terms-Heuristic search, hybrid strategy, Petri net, scheduling, semiconductor test. Huanxin Henry Xiong (S'94-M'96) received the B.S. and the M.S.

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

“…Additionally, there are no infinite occurrence sequences, thus the system always terminates, a result that is further supported by the identical number of nodes and arcs for the state space and SCC graphs. It should be noted that the number of nodes in the presented method, i.e., 559 nodes, is less than the expanded markings (i.e., 598) found in Xiong and Zhou [26]. Figure 7.…”

“…Validation of the method has been supported by the state space results of modeled system properties. The method was tested by applying it to known job shop benchmark examples, i.e., ft6 (six resources and six jobs) [65], as well as the example introduced by Xiong and Zhou [26] with lot sizes (1,1,1,1), (5,5,2,2), and (10,10,6,6). The scheduling goal was to obtain the minimal makespan.…”

“…State space was extracted for the CTPN scheduling system model of the job shop example introduced in [26] with lot size (1,1,1,1). One scheduling cycle was taken in the system model.…”

“…In Castiglione et al [21] an analytic modeling technique based on the use of Markovian Agents and Mean Field Analysis has been proposed that allows the effective modeling of concurrent Big Data applications. In heuristic search algorithm methods, PNs model the system and a search algorithm based on a heuristic function is used to expand a portion of the reachability graph, containing the most promising nodes that leads to an optimal scheduling solution [3,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The heuristic function must be admissible in order to obtain the optimal solution.…”

“…In Lee and DiCesare [3] a heuristic search algorithm based on the A* algorithm, a branch and bound type algorithm, was introduced to limit the search in the PN reachability graph in order to find the optimal solution for FMS scheduling problems. Modified and improved versions of the A* based heuristic search method have been presented in the literature [25][26][27][28][29][30][31][32][33][34][35][36]. In order to reduce the search time, non-admissible heuristic functions were proposed in [31,32].…”