Final project model

PRODUCTION
MANAGEMENT SYSTEMS
A resource portfolio planning model using
Sampling-based stochastic programming and genetic algorithm

Reconstruct an executable model

GROUP 9
MEMBER：
M10301206 蔣翔宇
M10308803 Phuong
M10301008 王奕翔
M10321814 Bimo Grahito Wicaksono
M10321111 吳家臻
Catalog

Bab I Abstract 5
Bab II Introduction 5
Bab III Problem formation 5
Bab IV Model 7
Bab V Reconstruct 7
Bab VI Method 8
Bab VII Result 11
Bab VIII Conclusion 17
Bab IX References 18

Picture
Figure 1 9
Figure 2 10
Figure 3 10
Figure 4 11
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Figure 6 12
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Figure 9 13
Figure 10 14
Figure 11 15
Figure 12 15
Figure 13 16
Figure 14 16
Figure 15 17

Table
Table 1 14
Table 2 17
Table 3 17

Bab I Abstract
Resource portfolio planning optimization is crucial to high-tech manufacturing industries. One of the most important characteristics of such a problem is intensive investment and risk in demands. In this study, a nonlinear stochastic optimization model is developed to maximize the expected profit under demand uncertainty. For solution efficiency, a stochastic programming-based genetic algorithm (SPGA) is proposed to determine a profitable capacity planning and task allocation plan. The algorithm improves a conventional two-stage stochastic programming by integrating a genetic algorithm into a stochastic sampling procedure to solve this large-scale nonlinear stochastic optimization on a real-time basis.
Finally, the tradeoff between profits and risks is evaluated under different settings of algorithmic and hedging parameters.
Experimental results have shown that the proposed algorithm can solve the problem efficiently.

Bab II Introduction
Stochastic resource planning and capacity allocation deals with the problem of how to find an optimal resource portfolio under uncertain demands. Such a portfolio planning has been explored in high-tech manufacturing industries due to intensive capital and technology

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