Advertisement

An Analysis of Strategies for Coupled Design Tasks Reviewing with Random Rework

  • Meng-na Wang
  • Xiao-ming WangEmail author
  • Qing-xin Chen
  • Ning Mao
Conference paper

Abstract

The coupled design tasks are usually need to be confirmed by customer repeatedly before they can be completed. To evaluate the merits of the coupled design task reviewing strategy with the exponentially distributed durations, the project network is firstly transformed by regarding each rework task as a potential new task. Then, the state transition process during the project execution is described by a continuous-time Markov chain (CTMC). Finally, the probability distribution of project completion time is obtained based on the phase-type (PH) distribution, and its expectation is severed as the standard for evaluating the a given reviewing strategy. To validate the constructed model and method, a calculation example is illustrated with a simple project that consists of six activities. The experimental results show that the quality of a given reviewing strategy is related to the project environment. Meanwhile, the experimental results also bring some inspiration to the design project management.

Keywords

Coupled design Reviewing strategy Random rework CTMC PH distribution 

Notes

Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (No. 51505090, No. 51775120 and No. 61573109).

References

  1. 1.
    K.-Y.C. Yu, D.L. Bricker, Analysis of a Markov chain model of a multistage manufacturing system with inspection, rejection, and rework. IIE Trans. 25(1), 109–112 (1993)CrossRefGoogle Scholar
  2. 2.
    V.M. Pillai, M.P. Chandrasekharan, An absorbing Markov chain model for production systems with rework and scrapping. Comput. Ind. Eng. 55(3), 695–706 (2008)CrossRefGoogle Scholar
  3. 3.
    X. Wang, Q. Chen, N. Mao et al., Estimation of mold remaining duration considering reworks. J. Mech. Eng. 50(7), 200–208 (2014)Google Scholar
  4. 4.
    V.G. Kulkarni, V.G. Adlakha, Markov and Markov-regenerative pert networks. Oper. Res. 34(5), 769–781 (1986)CrossRefGoogle Scholar
  5. 5.
    B. Dodin, Approximating the distribution functions in stochastic networks. Comput. Oper. Res. 12(3), 251–264 (1985)CrossRefGoogle Scholar
  6. 6.
    A. Azaro, S.M.T. Fatemi Ghomi, Lower bound for the mean project completion time in dynamic PERT networks. Eur. J. Oper. Res. 186(1), 120–127 (2008)Google Scholar
  7. 7.
    X. Wang, Q. Chen, N. Mao et al., Mould projects due-date forecast methods under random environment. Comput. Integr. Manuf. Syst. 18(2), 405–414 (2012)Google Scholar
  8. 8.
    S. Creemers, R. Leus, M. Lambrecht, Scheduling Markovian PERT networks to maximize the net present value. Oper. Res. Lett. 38(1), 51–56 (2010)CrossRefGoogle Scholar
  9. 9.
    S. Creemers, Minimizing the expected makespan of a project with stochastic activity durations under resource constraints. J. Sched. 18(3), 263–273 (2015)CrossRefGoogle Scholar
  10. 10.
    X.-M. Wang, R. Leus, S. Creemers et al., A CTMDP-based exact method for RCPSP with uncertain activity durations and rework, in OR2017 Conference, Berlin, Germany, 2017Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Meng-na Wang
    • 1
  • Xiao-ming Wang
    • 1
    Email author
  • Qing-xin Chen
    • 1
  • Ning Mao
    • 1
  1. 1.Guangdong Provincial Key Laboratory of Computer Integrated ManufacturingGuangdong University of TechnologyGuangzhouChina

Personalised recommendations