In this paper, we propose an automatic trading system for portfolio selection that incorporates an investor's trading strategy (aggressive, conservative, or neutral). The system employs technical indicators to forecast assets' future price behaviour. In particular, it clusters assets into three groups: the promising assets are clustered in the “Buy” group, the assets in danger of imminent losses are clustered in the “Sell” group, and the remaining assets are clustered in the “Hold” group. We develop a gradient-based fuzzy rule system that can identify the three groups based on the technical indicator values of the cluster centers. We also develop a labelling algorithm as a corrective measure in case the fuzzy rule-based system identifies more than one group as buy, sell, or hold. Subsequently, we input the clusters to a credibilistic portfolio optimization model that models asset returns using coherent fuzzy numbers. We employ a genetic algorithm to solve the optimization model that exploits the problem's special structure. The proposed methodology is illustrated with a case study of the components of the NASDAQ-100 index

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.

Shared transportation involves vehicles, drivers, and customers, the interactions among which could have potential long-term impacts on the business. Machine learning techniques, and their integration with existing models, have proved to significantly improve results. Availability of extensive unstructured textual data has fostered research in text generation and mining. Cognizance and analysis of such data has become crucial for modern commercial applications. Thus, in this article, sentiment analysis, using natural language processing, is used to quantify raw customer feedback, to obtain drivers' ratings and perform driver selection. Selection of the best drivers for ferrying riders is desired and modeled accordingly. An integrated vehicle routing problem with generalized fuzzy travel durations, and uncertain pick-up and drop demands, is modeled and solved using a hybrid genetic algorithm. Fuzzy simulations in a credibilistic environment are employed to evaluate the cost function. Performance of selected drivers is used to update driver ratings for the subsequent run, and the process is repeated multiple times. The results obtained authenticate the purpose of this article, and comparative analysis is performed to further corroborate the model's capability. An additional case of triangular fuzzy ratings is also illustrated, and its impact on the model discussed. Suggestions for driver classification are also provided for personnel management.

Vehicle routing, being a major concern of any industry with transportation requirements (manufacturing, supply-chain, travel and tourism, etc.), offers immense scope for research. Due consideration of this fact has motivated the development of the approach illustrated hereafter. In this article, a vehicle routing problem (VRP) with generalized fuzzy travel times, multiple depots, split delivery (including inter depot split), and heterogenous, capacitated, alternative fuel driven vehicles, is studied. A hybrid genetic algorithm (GA) is designed to produce efficient solutions. Since traditional methods are incapable of computing the expected values of such fuzzy variables, the technique of fuzzy simulation is incorporated in the GA. Five alternative fuel vehicles-electric, hybrid, diesel, biodiesel, and CNG, are evaluated vis-a-vis multiple criteria using fuzzy hierarchical technique for order preference by similarity to ideal solution (TOPSIS) and their respective scores are input in the hybrid GA for sustainable, apt, and low-cost assignment of vehicles to routes. The algorithm is run for multiple combinations of crossover and mutation probabilities, vehicle capacities, location instances, and number of generations. The experimental results substantiate the robustness of the proposed approach and suffice to project the strength of the computationally challenging model.

In this article, two different multiobjective fuzzy portfolio selection models are presented. The significant criteria considered for portfolio selection are risk (variance or conditional value at risk), return, liquidity, and entropy. Here, the return of the portfolio is considered to be satisfied by a minimum return threshold constraint. Also, to introduce some degree of diversification in the model, a lower and upper bound constraint on investment in an asset is used along with the capital budget and no short selling constraints. Trapezoidal fuzzy returns are considered to incorporate the inherent uncertainty of the stock market, which is handled by using the credibility theory. The weighted sum approach is used to aggregate the objectives and characterize different investor attitudes. Random sample portfolios with progressively increasing sample sizes are generated that obey the constraints of the portfolio models. These random sample portfolios with multiple inputs (risk and entropy) and multiple outputs (return and liquidity) are evaluated in terms of their performance by using data envelopment analysis. Furthermore, a frontier improvement technique existing in the literature is used to rebalance the inefficient random sample portfolios to make them efficient, so that an investor may have more avenues to select efficient portfolios. A detailed numerical illustration with a simulation study using different sample sizes is presented to substantiate the proposed study.

This paper presents a new multiple criteria optimization model of an assignment problem with imprecise coefficients. Besides, minimizing the total cost, total time of finishing jobs, and maximization of the overall achieved quality, we introduce a new criterion that minimizes the number of workers employed to finish all jobs. It contributes significantly in multi-job assignment to adjust the number of workers assigned to at least one job for balancing work allocation among the workers. Furthermore, we employ new diversification constraints to obtain a reasonable tradeoff between the number of workers employed and number of jobs assigned. A new interactive possibilistic programming approach is developed for trapezoidal possibility distributions, which uses α-level sets to incorporate confidence levels of the decision maker in his fuzzy judgments leading to α-efficient solutions. Numerical experiments are conducted using data coming from a manpower planning problem to demonstrate working of the proposed multiple criteria assignment model and effectiveness of the fuzzy interactive approach.

In this paper, we study the multiattribute decision-making (MADM) problem with intuitionistic fuzzy values that represent information regarding alternatives on the attributes. Assuming that the weight information of the attributes is not known completely, we use an approach that utilizes the relative comparisons based on the advantage and disadvantage scores of the alternatives obtained on each attribute. The relative comparison of the intuitionistic fuzzy values in this research use all the three parameters, namely membership degree (“the more the better”), nonmembership degree (“the less the better”), and hesitancy degree (“the less the better”), thereby leading to the tradeoff values of all the three parameters. The score functions (advantage and disadvantage scores) used for this purpose are based on the positive contributions of these parameters, wherever applicable. Furthermore, these scores are used to obtain the strength and weakness scores leading to the satisfaction degrees of the alternatives. The optimal weights of the attributes are determined using a multiobjective optimization model that simultaneously maximizes the satisfaction degree of each alternative. The optimal solution is used for ranking and selecting the best alternative on the basis of the overall attribute values. To validate the proposed methodology, we present a numerical illustration of a real-world case. The methodology is further extended to treat MADM problem with interval-valued intuitionistic fuzzy information. Finally, a thorough comparison is done to demonstrate the advantages of the solution methodology over the existing methods used for the intuitionistic fuzzy MADM problems.

In this paper, we propose a new possibilistic programming approach to solve a fuzzy multiobjective assignment problem in which the objective function coefficients are characterized by triangular possibility distributions. The proposed solution approach simultaneously minimizes the best scenario, the likeliest scenario, and the worst scenario for the imprecise objective functions using α-level sets. The α-level sets are used to define the confidence level of the fuzzy judgments of the decision maker. Additionally, we provide a systematic framework in which the decision maker controls the search direction by updating both the membership values and aspiration levels until a set of satisfactory solutions is obtained. Numerical examples, with dataset from realistic situations, are provided to demonstrate the effectiveness of the proposed approach.

This paper addresses the problem of portfolio selection with fuzzy parameters from a perspective of chance-constrained multiobjective programming. The key financial criteria used here are conventional, namely, return, risk, and liquidity; however, we use short- and long-term variants of return rather than a single measure of an investor's expectations in respect thereof. The proposed model aims to achieve the maximal return (short term as well as long term) and liquidity of the portfolio. It does so at a credibility, which is no less than the confidence levels defined by the investor. Further, to capture uncertain behavior of the financial markets more realistically, fuzzy parameters used here are such as those characterized by general functional forms. To solve the problem, we rely on a specially developed algorithm that hybridizes fuzzy simulation and real-coded genetic algorithm. Numerical experiments are included to showcase the applicability and efficiency of the model in a real investment environment.