Extended event-condition-action rules and fuzzy Petri nets based exception handling for workflow management

  • Authors:

  • Yan Ye;Zhibin Jiang;Xiaodi Diao;Gang Du

  • Affiliations:

  • Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, Shanghai 200240, China and Department of Industrial Engineering, School of Mechatronics Engineering, G ...;Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Putuo District Center Hospital, Shanghai 200062, China;Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, Shanghai 200240, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Exception handling plays a key role in dynamic workflow management that enables streamlined business processes. Handling application-specific exceptions is a knowledge-intensive process involving different decision-making strategies and a variety of knowledge, especially much fuzzy knowledge. Current efforts in workflow exception management are not adequate to support the knowledge-based exception handling. This paper proposes a hybrid exception handling approach based on two extended knowledge models, i.e., generalized fuzzy event-condition-action (GFECA) rule and typed fuzzy Petri net extended by process knowledge (TFPN-PK). The approach realizes integrated representation and reasoning of fuzzy and non-fuzzy knowledge as well as specific application domain knowledge and workflow process knowledge. In addition, it supports two handling strategies, i.e., direct decision and analysis-based decision, during exception management. The approach fills in the gaps in existing related researches, i.e., only providing the capability of direct exception handling and neglecting fuzzy knowledge. Based on TFPN-PK, a weighted fuzzy reasoning algorithm is designed to address the reasoning problem of uncertain goal propositions and known goal concepts by combining forward reasoning with backward reasoning and therefore to facilitate cause analysis and handling of workflow exceptions. A prototype system is developed to implement the proposed approach.