MATEC Web of Conferences
Volume 51, 20162016 International Conference on Mechanical, Manufacturing, Modeling and Mechatronics (IC4M 2016)
|Number of page(s)||6|
|Section||Chapter 1: Engineering Simulation, Modelling and Analytical Studies|
|Published online||06 April 2016|
A Scenario Tree based Stochastic Programming Approach for Multi-Stage Weapon Equipment Mix Production Planning in Defense Manufacturing
1 Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, 410073, P. R. China
2 School of Materiel Management and Safety Engineering, Air Force Engineering University, Xi’an 710051, P. R. China
3 State Key Laboratory of Complex System Simulation, Beijing Institute of System Engineering, Beijing, 100101, P. R. China
a Corresponding author: firstname.lastname@example.org
The evolving military capability requirements (CRs) must be meted continuously by the multi-stage weapon equipment mix production planning (MWEMPP). Meanwhile, the CRs possess complex uncertainties with the variant military tasks in the whole planning horizon. The mean-value deterministic programming technique is difficult to deal with the multi-period and multi-level uncertain decision-making problem in MWEMPP. Therefore, a multi-stage stochastic programming approach is proposed to solve this problem. This approach first uses the scenario tree to quantitatively describe the bi-level uncertainty of the time and quantity of the CRs, and then build the whole off-line planning alternatives assembles for each possible scenario, at last the optimal planning alternative is selected on-line to flexibly encounter the real scenario in each period. A case is studied to validate the proposed approach. The results confirm that the proposed approach can better hedge against each scenario of the CRs than the traditional mean-value deterministic technique.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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