An accurate prediction of asset prices is perhaps the biggest challenge of any study in portfolio optimization. Asset prices are affected by several random and nonrandom factors, which makes them difficult to forecast. This paper proposes a two-phase dynamic portfolio optimization approach. In the first phase, assets are clustered into buy, sell, and hold groups using technical indicators. We provide a methodology to integrate the investor attitude (optimistic, pessimistic, or neutral) during the clustering phase. In the second phase, we input the clustered groups into a portfolio optimization model to obtain the optimum asset allocations. We use coherent fuzzy numbers to model the asset returns to integrate the investor attitude in this phase. The optimization model is solved using a genetic algorithm. The portfolios are rebalanced at regular intervals as new data becomes available. We illustrate the proposed methodology on a 100-asset problem of the US stock market. We analyze the real-world performance of the obtained portfolios. We compare the performance of the proposed approach with the mean–variance model, and other portfolios, such as the naïve portfolio and the NASDAQ-100 index.

This paper extends q -rung orthopair fuzzy numbers into q -rung orthopair trapezoidal fuzzy numbers to solve a multiattribute group decision-making problem. The decision-makers (DMs) provide some of their assessments in hesitant form, along with hesitancy weights. The basic operations laws, Hamming distance, weighted similarity measure, value and ambiguity indexes, weighted average aggregation operator, and weighted geometric aggregation operator, with their properties, are discussed for these extended fuzzy numbers. The value and ambiguity indexes are used in the Shannon entropy to evaluate the weights of the DMs and attributes'. These weights are then used to aggregate the DMs' assessments in the TOPSIS approach to obtain a weighted similarity measure from both the alternatives' positive and negative ideal solutions. The proposed approach's effectiveness is demonstrated by solving a waste disposal site selection problem. The approach is further validated through the basic properties of multiattribute decision making, comparative analyses, and comparing simulation results with an existing approach.

In this paper, we propose a new credibility function for a fuzzy variable that can accommodate the attitude of the investor (pessimistic, optimistic, or neutral) along with capturing the return expectations. We use an adaptive index, which the investors can use to specify their general perception of the financial market. We extend the classic mean-variance model so that it provides greater flexibility to the investors in specifying their requirements viz., level of diversification, minimum and maximum level of investment in a particular asset, and the skewness requirement. We also replace variance with mean-absolute semideviation as a measure of quantifying risk, which is more realistic, and solve the resultant multiobjective credibility model with a real-coded genetic algorithm. Numerical examples have been provided at the end to illustrate the methodology and advantages of the model.

The world faced a major disruption in the form of the coronavirus disease (COVID-19) pandemic, which caused many countries to impose severe restrictions on movement, popularly known as “lockdown.” These lockdowns impacted transportation adversely, leading to massive disruptions in global and local supply chains. As the local markets were shut down, more people started turning to e-commerce logistics platforms offering doorstep deliveries of essential items (food and medicines). This resulted in an explosion in demand for such services, and businesses struggled to complete their deliveries. Additionally, the volume of real-time text data suddenly increased, as these customers started sharing their feedback on social media platforms. The availability of real-time raw text data and its popularity for solving complex business problems motivated the development of the approach proposed herein to address last-mile delivery issues. Thus, this paper suggests the use of Twitter data to identify the various grievances of customers about e-commerce logistics platforms. Natural language processing, a popular tool for text analytics, is employed to extract consumer tweets from the Twitter profiles of such businesses and subsequently to clean, process, and analyse them. Issues are categorized and used as objectives in a multiobjective fuzzy vehicle routing problem (VRP). An integrated hybrid fuzzy VRP is developed and coded to solve last-mile delivery issues. Experimental results and comparative analyses highlight the benefits of the novel approach. Managerial insights and scope for future research assist in the further development of the idea.