Paper Title:
Molding and Optimization of Workflow Based on Stochastic Timed Petri Nets in Customized Supply Chain Management
  Abstract

Customized Supply Chain Management (CSCM) has been made much account of attention in Flexible Manufacturing Systems (FSM). Products are customized by customer’s special needs at stochastic time. Then Manufacturing Oriented to Customization (MOC) would inevitably be faced by those enterprises that had to meet the changeable and complex needs of customers. However, there were large quantities of difficulties would appear in MOC supply chains, such as information overload, technology and resource constraints. All of these problems made it hard to quickly and effectively respond to customers’ needs. Therefore, we need to find a new way to deal effectively with the problems about how to respond quickly to supply chains oriented to customization. Petri Nets have been extensively used for modeling and simulating of the dynamics of flexible manufacturing systems. However, Petri Nets have not been very popular for molding of workflow in CSCM environmental. In this paper Stochastic Timed Petri Nets (STPN) were used to solve the above problems. The enterprises’ workflow was analyzed, and then the STPN model was constructed according to the characteristics of its internal supply chain. In order to minimize total tardiness time of workflow, the structure of STPN was optimized in a proper way. The results suggest that the STPN approach is also valid to minimize due date of each transition in CSCM and responses to customized needs more quickly than before.

  Info
Periodical
Advanced Materials Research (Volumes 201-203)
Edited by
Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang
Pages
1082-1085
DOI
10.4028/www.scientific.net/AMR.201-203.1082
Citation
W. J. Xu, J. Yao, "Molding and Optimization of Workflow Based on Stochastic Timed Petri Nets in Customized Supply Chain Management", Advanced Materials Research, Vols. 201-203, pp. 1082-1085, 2011
Online since
February 2011
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$32.00
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