Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem Case study: The dairy products industry
Introduction
The multi-product institutes always face challenges to modify, remove or add products, indicating that the management should arrange a set of products which would it yield the optimal efficiency of profitability (Amadini, Gabbrielli, & Mauro, 2016). With respect to different features of the products, like the profitability and raw material supply rate, product portfolio design should be planned in a manner where the optimal profitability is guaranteed. The portfolio management science provides tools and solutions that managers and decision-makers can choose from in order to realize the best product portfolio (Aksaraylı and Pala, 2018, Goli et al., 2019, Ha et al., 2017, Hassanlou, 2017).
All of the investment methods and theories are examined in a real-life situation, it is observed that most of these methods have two basic drawbacks in operating phase, in spite of the advantages of selecting each technique and optimizing the product portfolio (Wu & Chuang, 2012). The first drawback is the assumptions underlying these theories. If they do not reveal real conditions, they will yield different results (Sadjadi & Karimi, 2018). The second drawback is the investment trade-off between the investment risk and return, which makes achieving a single optimal solution impossible (Aksaraylı & Pala, 2018).
According to Cardozo & Smith (1983) financial portfolio theory can be adopted for optimal management of the product portfolio. Accordingly, in this study, attend is made to apply the basics of financial portfolio theory in designing an optimal product portfolio. In this theory, risk and return are always considered as the two main objectives in the portfolio optimization models, consequently, the simultaneous risk and return optimization in the design of the product portfolio is discussed here.
Designing a product portfolio is a strategic decision, where the return of each product may change in the future. Determining the risk of each product in a given portfolio is ambiguous. For this purpose, a joint approach of artificial intelligence and robust optimization is proposed hereto find the best portfolio under return uncertainty. This joint approach is implemented in the dairy industry of Iran and the results are analyzed.
This study is organized as follows: the literature review is presented in Section 2 presents; the methodology is introduced in Section 3; the case study is introduced in Section 4; the numerical results are exposed in Section 5 and the article is concluded in Section 6.
Section snippets
Literature review
In the past researches, most of researched developing financial portfolio models. Some of the most contributed ones are presented as follow. Geum, Shin, & Park (2011) developed a systematic framework for improving the productivity of hospital services through a portfolio approach, where, the failure mode and effect analysis (FMEA) is used to find the optimal portfolio. Fernandes, Gouveia, & Pinho (2012) presented a new strategy for investing in the product portfolio, considering the production
Methodology
To optimize the product portfolio with a financial portfolio approach, the two key parameters of risk and return are of concern. The risk reflects the changes and returns of the present portfolio profit (Lejeune & Shen, 2016). Due to fluctuations in different economic indicators, these parameters are not predetermined in the exact sense. For this reason, it is necessary to consider an appropriate method for measuring the value of these parameters. Moreover, as observed in Fig. 1, the necessity
Case study
The Pegah Golpayegan Company, Isfahan province, Iran is considered for implementing this newly proposed product portfolio optimization. The lack of making proper decisions on the product portfolio is one of the main problems in this company, which lead to high set-up costs. This drawback is followed by high risk in production and lack of return which forces the managers to update the product portfolio for the future. This fact is justified with the following brief statistics: Products of 335
Numerical results
In this prediction network, 5 neurons are applied in the first hidden layer and 5 neurons in the second one. Here, 70% of data is considered for training and 30% for testing the network. In MLP + RRA, RRA is applied to the training data to find the best value for weights between different network layers, and to achieve the minimum MSE value. After implementing RRA, we will have the best possible MSE for training data. Then it will apply for the test data to monitor the performance of RRA. For
Conclusion and future researches
This paper has addressed the product portfolio optimization using hybrid artificial intelligence and robust optimization using risk and return concepts. First, the future demand for each product type has been predicted by using a hybrid MLP and RRA meta-heuristic algorithm. Next, a new technique for calculating product risk has been developed. Then, two optimization models based on the Ben-Tal and Nemirovski model and Bertsimas and Sim model were presented. As the portfolio selection is a
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