###### Abstract
[PERSON] and [PERSON] [18] discussed several disadvantages of the revised algorithm and the stopping condition of the revised algorithm, stating that it was not defined properly as it might cause the algorithm to reach a non-optimum solution. They then developed their own algorithm, called the \"improved algorithm\", which is equipped with a neighborhood search process involving firm stopping conditions. The literature on methods for solving problems with a multi-bottleneck is rapidly growing. [PERSON] et al. [25] modeled the problem using fuzzy linear programming. [PERSON] et al. [26] proposed a TS and simulated annealing hybrid approach. [PERSON] et al. [27] proposed an approach based on [PERSON]'s need hierarchy theory. [PERSON] et al. [28] also used immune-based approaches, such as self-adaptive regulation and vaccination. Recently, [PERSON] et al. [29] presented an integrated heuristic model comprising the AHP and the TOC (AHP-TOC heuristic) in a decision model, in which the priority of a product and a resource center optimize the product throughput in a multiple-constraint resource environment. [PERSON] et al. [30] reviewed the AHP-TOC heuristic and illustrated its shortcomings. In this overview of PMP literature, we can conclude that although the optimum product mix may sometimes include products with the lowest product margin and the lowest ratio of throughput per constraint time, violating the TOC-derived heuristic principle [17], TOC-based heuristic approaches provide efficient solutions (sometimes with some slight modifications) for a wide range of PMP instances within reasonable computation times. PMP, thus far, has been dealt with principally from the perspective of production, but it must be emphasized that the competitive positions of products in the market play a significant role in the determination of strategic product mixes. [PERSON] [9] asserts that companies tend to view products as either \"dogs\" (which have low profit margins), or as \"stars\" (with large margins), in line with traditional product margin approaches. To compete in the marketplace, companies, on the one hand, expand and differentiate their product mix in a way that can lead to increased revenues, and, on the other hand, the strategy that they adopt should allow for the optimal utilization of enterprise resources and for obtaining a product mix that maximizes operating profit [31]. Companies are, thus, obliged to determine how to offer the right product mix to the market. Such decisions on optimal product mix are closely associated with the general wisdom suggested by the Boston Consulting Group (BCG) (an introduction is available at www.bcg.com/publications/19/70/strategy-the-product-portfolio.aspx (accessed on 2 December 2020)) and their notion of product portfolio strategy [32]. In the formulation of product portfolio strategies, it is essential to gather and aggregate the views of senior executives in a systematic manner. In this study, the GE-McKinsey matrix (an introduction is available at www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/enduring-ideas-the-ge-and-mckinsey-nine-box-matrix (accessed on 2 December 2020)) is used to decide on a product portfolio strategy, instead of using the BCG method. According to [PERSON] et al. [33], a McKinsey matrix has these strengths: (i) it helps to prioritize limited resources in order to maximize return, (ii) managers have a better understanding of how their goods or business divisions operate, and (iii) it will determine the strategic initiatives that the organization should take to improve the performance of its business portfolio. GE has many advantages over BCG since it evaluates the products according to the attractiveness of the market involved and the strength of the firm's position in the market, with 5 main criteria, including 73 sub-criteria. Moreover, GE consists of nine decision cells with three types of classification (strong, average, weak) instead of four cells with two types of classification (high, low), as in the BCG matrix.