Abstract
Product portfolio optimization (PPO) is a strategic decision for many organizations. There are several technical methods for facilitating this decision. According to the reviewed studies, the implementation of the robust optimization approach and the invasive weed optimization (IWO) algorithm is the research gap in this field. The contribution of this paper is the development of the PPO problem with the help of the robust optimization approach and the multi-objective IWO algorithm. Considering the profit margin uncertainty in real-world investment decisions, the robust optimization approach is used to address this issue. To illustrate the real-world applicability of the model, it is implemented for dairy products of Pegah Golpayegan Company in Iran. The numerical results obtained from the IWO algorithm demonstrate the effectiveness of the proposed algorithm in tracing out the efficiency frontier of the product portfolio. The average risk of efficient frontier solutions in the deterministic model is about 0.4 and for the robust counterpart formulation is at least 0.5 per product. The efficient frontier solutions obtained from robust counterpart formulation demonstrate a more realistic risk level than the deterministic model. The comparisons between CPLEX, IWO and genetic algorithm (GA) shows that the performance of the IWO algorithm is much better than the older algorithms and can be considered as an alternative to algorithms, such as GA in product portfolio optimization problems.
Highlights
One of the most important decisions in production and servicebased enterprises and organizations is the choice of suitable product portfolio for planning and production over long-term horizons
The goal of this study is to provide a general approach for robust optimization of product portfolio problem
This study found that Non-dominated Sorting Genetic Algorithm (NSGA), Strength Pareto Evolutionary Algorithm (SPEA), and Indicator-Based Evolutionary Algorithm (IBEA) are the algorithms most widely used in the literature for portfolio optimization [27]
Summary
One of the most important decisions in production and servicebased enterprises and organizations is the choice of suitable product portfolio for planning and production over long-term horizons. A two-stage GA was used to solve the multi-objective stock portfolio optimization problem with the objectives of maximizing the returns and minimizing the investment risk with the Markowitz model serving as the fundamental mathematical formulation [17]. Higher Γ0 values raise the level of conservatism in the objective function On this basis, the robust counterpart formulation of the aforementioned nominal linear optimization problem is derived as follows Bertsimas and Sim [3]. The addition of integer constraint to the portfolio optimization model results in a combinatorial quadratic integer programming problem, which is NP-hard and cannot be solved by exact solution methods in large-scale instances.
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