A two-level hedging point policy for controlling a manufacturing system with time-delay, demand uncertainty and extra capacity

Tung Sun Chan, Zheng Wang, Jie Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


This paper focuses on the production control of a manufacturing system with time-delay, demand uncertainty and extra capacity. Time-delay is a typical feature of networked manufacturing systems (NMS), because an NMS is composed of many manufacturing systems with transportation channels among them and the transportation of materials needs time. Besides this, for a manufacturing system in an NMS, the uncertainty of the demand from its downstream manufacturing system is considered; and it is assumed that there exist two-levels of demand rates, i.e., the normal one and the higher one, and that the time between the switching of demand rates are exponentially distributed. To avoid the backlog of demands, it is also assumed that extra production capacity can be used when the work-in-process (WIP) cannot buffer the high-level demands rate. For such a manufacturing system with time-delay, demand uncertainty and extra capacity, the mathematical model for its production control problem is established, with the objective of minimizing the mean costs for WIP inventory and occupation of extra production capacity. To solve the problem, a two-level hedging point policy is proposed. By analyzing the probability distribution of system states, optimal values of the two hedging levels are obtained. Finally, numerical experiments are done to verify the effectiveness of the control policy and the optimality of the hedging levels.
Original languageEnglish
Pages (from-to)1528-1558
Number of pages31
JournalEuropean Journal of Operational Research
Issue number3
Publication statusPublished - 1 Feb 2007
Externally publishedYes


  • Control
  • Hedging point policy
  • Networked manufacturing systems
  • Production
  • Time-delay

ASJC Scopus subject areas

  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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