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Research Article

Academia Journal of Scientific Research 7(10): 542-552, October 2019
DOI: 10.15413/ajsr.2019.0152
ISSN: 2315-7712
2019 Academia Publishing 



Extremely fast heuristic event-driven job shop scheduler with a new class of extended petri nets

Accepted 7th June 2019

Alexander Kostin

Girne American University, Cyprus.

A heuristic job shop scheduler which is implemented with a new class of extended Petri nets is proposed. The used Petri nets outlined in the paper attributed tokens and represent the complete algorithmic system. The scheduler creates a schedule sequentially without any form of enumerative search. Its structure and Petri net scheme were also explained. The scheduler includes models of machines and runs in the environment of WINSIM system which implements the used class of Petri nets. With the scheduler, two groups of experiments were conducted using three different greedy strategies in each group. In the first group, the scheduler was tested on single published benchmark data instances of 10 10 and 15 15 scheduling tasks. In the second group, 10 10, 15 15 and 20 20 scheduling tasks were investigated with a series of 1000 generated random task instances each to obtain statistics on average makespan, its standard deviation, average loads of machines and relative unbalance of machine loads. It was observed that for each of the studied problems, the developed scheduler runs hundreds or thousands of times faster than any known job shop scheduling method due to a unique mechanism of monitoring events in the active Petri net.

Key words: Event-driven job shop scheduler, extended Petri nets, scheduling experiments, Petri-net, tool, WINSIM.

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article as:

Kostin A (2019). Extremely fast heuristic event-driven job shop scheduler with a new class of extended petri nets. Acad. J. Sci. Res. 7(10): 542-552.

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