Time Complexity Analysis of the Legal Firing Sequence Problem of Petri Nets with Inhibitor Arcs

• TAOKA Satoshi

  Graduate School of Engineering, Hiroshima University

• WATANABE Toshimasa

  Graduate School of Engineering, Hiroshima University

Abstract

Petri nets with inhibitor arcs are referred to as inhibitor-arc Petri nets. It is shown that modeling capability of inhibitor-arc Petri nets is equivalent to that of Turing machines. The subject of this paper is the legal firing sequence problem (INLFS) for inhibitor-arc Petri nets: given an inhibitor-arc Petri net IN, an initial marking M_0 and a firing count vector X, find a firing sequence S such that its firing starts from M_0 and each transition t appears in S exactly X(t) times as prescribed by X. The paper is the first step of research for time complexity analysis and designing algorithms of INLFS, one of the most fundamental problems for inhibitor-arc Petri nets having more modeling capability than ordinary Peri nets. The recognition version of INLFS, denoted as RINLFS, means a decision problem, asking a "yes" or "no" answer on the existence of a solution δ to INLFS. The main results are the following (1) and (2). (1) Proving (1-1) and (1-2) when the underlying Petri net of IN is an unweighted state machine: (1-1) INLFS can be solved in pseudo-polynomial (O(|X|)) time for IN of nonadjacent type having only one special place called a rivet; (1-2) RINLFS is NP-hard for IN with at least three rivets; (2) Proving that RINLFS for IN whose underlying Petri net is unweighted and forward conflict-free is NP-hard. Heuristic algorithms for solving INLFS are going to be proposed in separate papers.

Journal

• IEICE Trans. Fundamentals, A

  IEICE Trans. Fundamentals, A 89 (11), 3216-3226, 2006-11-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• Petri nets
• inhibitor-arcs
• legal firing sequences
• pseudo-polynomial time algorithms
• NP-hardness

Details

• CRID: 1572543027451891328
• NII Article ID: 110007537814
• NII Book ID: AA10826239
• ISSN: 09168508
• Text Lang: en
• Data Source:
  • CiNii Articles