Contributed Session Fri.1.H 3027

Friday, 10:30 - 12:00 h, Room: H 3027

Cluster 7: Finance & economics [...]

Optimal control


Chair: Yuichi Takano



Friday, 10:30 - 10:55 h, Room: H 3027, Talk 1

Arindum Mukhopadhyay
A socio-economic production quantity (SEPQ) model for imperfect items with pollution control and varying setup costs

Coauthor: Adrijit Goswami


Corporate social responsibility (CSR) initiatives have gained considerable prominence in recent years.
Public demand for environmental protection has created a need for identifying the most ecofriendly
and economic production strategies. Keeping this in mind, this paper investigates a
socioeconomic production quantity (SEPQ) model with imperfect quality items for varying
setup cost using the setup cost as a function of production run length. The setup cost and run length
can be related in terms of process deterioration and learning and forgetting effects. Three different
approaches of minimizing pollution during production process and transportation is provided.
Mathematical models and solution procedures are developed for each of them. Numerical example
and sensitivity analysis are provided to illustrate and analyse the model performance. It is observed
that our model has a significant impacts on the optimal lot size and optimal profit of the model.



Friday, 11:00 - 11:25 h, Room: H 3027, Talk 2

Yuichi Takano
Control policy optimization for dynamic asset allocation by using kernel principal component analysis

Coauthor: Jun-Ya Gotoh


We utilize a nonlinear control policy, which is a function of past asset returns or economic indicators, to construct a portfolio. Although the problem of selecting the best control policy from among nonlinear functions is intractable, our previous study has built a computational framework for solving this problem. Specifically, we have shown that this problem can be formulated as a convex quadratic optimization problem by using a kernel method, which is an engine for dealing with the strong nonlinearity of statistical models in machine learning. Our nonlinear control policy resulted in better investment performance than the basic model and linear control policies could give. However, it was difficult to handle a large-scale portfolio optimization problem. Thus in this presentation, we provide an efficient solution for optimization of a nonlinear control policy by using kernel principal component analysis. Computational experiments show that our solution is effective not only in reducing the CPU time but also in improving the investment performance.


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