Seyed Jafar Sadjadi

Sentiment analysis (SA) of natural language text has become as a powerful instrument for enhancing financial market predictions. Quarterly reports from companies, in particular, offer a rich source of data for sentiment analysis, providing key insights into a company’s performance, strategic actions, and future prospects. These reports can significantly influence investor decisions regarding asset investments. Notwithstanding the potential, prior research has not investigated sentiment analysis concerning these resources in portfolio optimization. To fill this void, we propose an innovative three-stage approach to constructing stock portfolios. In the first stage, we perform sentiment analysis on companies’ quarterly reports using the FinBERT model to assess the sentiment surrounding each company. In the second stage, we utilize a Long-Short-Term Memory (LSTM) model for forecasting future prices, which enables the calculation of expected returns and the covariance matrix. In final stage, we present a three-objective portfolio optimization model that incorporates risk, return, and sentiment-derived trend features. We solve this model using the Weighted Goal Programming (WGP) method. Our results indicate that the proposed model effectively supports portfolio optimization. Moreover, the model is implemented using data from companies that are part of the Dow Jones Industrial Average (DJIA), and findings demonstrate high accuracy, confirming the practical potential of the proposed approach.

In the world of omnichannel retail, where customers seamlessly switch between online and offline channels, pricing and inventory management decisions have become more complex than ever. Customer purchasing behavior is influenced by uncertainty, market fluctuations, and competitive interactions, which traditional models fail to accurately predict. In such conditions, the need for intelligent and adaptive decision-making frameworks is more critical than ever. For the first time, this study presents a novel approach combining Quantum Decision Theory, Quantum Markov Chains (QMC), Quantum Dynamic Games, and Reinforcement Learning to optimize dynamic pricing and inventory management. By leveraging concepts such as superposition, observer effect, and quantum interference, the proposed model overcomes the limitations of classical models and provides a deeper understanding of customer behavior in uncertain environments. Additionally, a Quantum Multi-Level Markov Process (QMLMP) is employed to model market variations and enhance predictions. The results of this study demonstrate that the innovative model improves the accuracy of purchase behavior predictions, optimizes pricing and inventory management strategies, and helps retailers make more competitive and profitable decisions. This research introduces a transformative approach to tackling retail challenges in the digital age and paves the way for future studies in this domain.

In the competitive retail omnichannel market, customer loyalty is essential for maintaining market share and reducing the cost of acquiring new customers. Previous research has primarily focused on factors influencing customer loyalty, often in isolation, but this study goes beyond traditional approaches. The aim of this research is to fill significant gaps in current studies by integrating a more comprehensive set of variables that reflect the complex and dynamic nature of customer loyalty in a flexible omnichannel environment. The main innovation of this study lies in the use of new and comprehensive omnichannel data, which includes sales data across various platforms, socio-economic conditions, shopping cart behaviors, and customer sentiments. The proposed model utilizes a hybrid approach, incorporating BERT for sentiment analysis, reinforcement learning for behavior analysis, and fine-tuning for improved predictions. Additionally, graph-based models (GCN) and adaptive learning are employed to analyze trends and predict loyalty at both individual and neighborhood levels. This research provides an intelligent analytical framework for predicting customer loyalty in omnichannel retail environments, enhancing Customer Relationship Management (CRM) subsystems within Enterprise Information Systems (EIS). By optimizing decisions in areas such as pricing, inventory management, and personalized advertising, this study ultimately leads to improved customer retention and increased market competitiveness.

Portfolio optimization continues to be a complex and challenging task within the fields of finance and management. Two critical factors that can improve the performance of traditional models are incorporating the effects of both financial and operational performance of companies and addressing the inherent uncertainty surrounding expected returns. This article addresses these two challenges. To evaluate the financial and operational efficiency of firms, we analyze their quarterly reports using the FinBERT model, incorporating their influence into the optimization framework through adjusted returns. To address the unpredictability linked to anticipated returns, we utilize fuzzy trapezoidal numbers in our methodology. Furthermore, conventional risk measurement systems, which rely based on probability-based assumptions and past data, often find it challenging to address the unique dynamics and inherent uncertainties of the market. In contrast, our suggested approach utilizes a credibilistic conditional value at risk (CCVaR) framework to evaluate portfolio risk. The approach additionally factors in transaction costs and incorporates practical constraints like cardinality and upper and lower bounds, maintaining the portfolio’s diversification, well-balanced, and reflective of practical scenarios. We apply the proposed approach to real-world data from DJIA stocks. Experimental findings highlight the approach’s efficacy in creating mixed portfolios that effectively create an equilibrium between risk and return. This research enhances the domain of investiture management by developing advanced portfolio optimization methods for stock market assets and offering a reliable approach for handling risk in today’s increasingly complex financial landscape.

The Resource-Constrained Project Scheduling Problem (RCPSP) remains a critical area of study in project management, focusing on optimizing project schedules under constraints such as limited resources and task interdependencies. This review synthesizes advancements from 2016 to 2024, encompassing problem variants, optimization techniques, objectives, and real-world applications. Key developments include the evolution of hybrid metaheuristics, multi-objective optimization approaches, and the integration of stochastic models to enhance robustness against uncertainties. Furthermore, the application of machine learning and sustainability-driven models has expanded the practical scope of RCPSP in dynamic and complex environments. Challenges such as scalability, uncertainty management, and the need for practical implementations are addressed, with future directions emphasizing AI integration, decentralized scheduling, and real-time adaptive solutions. This study provides a comprehensive perspective on RCPSP, bridging theoretical research with practical implications for diverse industries.

The measurement of productivity change in decision-making units (DMUs) is crucial for assessing their performance and supporting efficient decision-making processes. In this paper, we propose a new approach for measuring productivity change using the Malmquist productivity index (MPI) within the context of two-stage network data envelopment analysis (TSNDEA) under data uncertainty. The two-stage network structure represents a realistic model for DMUs in various fields, such as insurance companies, bank branches, and mutual funds. However, traditional DEA models do not adequately address the issue of data uncertainty, which can significantly impact the accuracy of productivity measurements. To address this limitation, we integrate the MPI methodology with an uncertain programming framework to tackle data uncertainty in the productivity change measurement process. Our proposed approach enables the evaluation of productivity change by capturing both technical efficiency and technological progress over time. By incorporating fuzzy mathematical programming into the DEA framework, we model the inherent uncertainty in input and output data more effectively, enhancing the robustness and reliability of productivity measurements. The utilization of the proposed approach provides decision-makers with a comprehensive analysis of productivity change in DMUs, allowing for better identification of efficiency improvements or potential areas for enhancement. The findings from our study can enhance the decision-making process and facilitate more informed resource allocation strategies in real-world applications.

This study presents a two-phase approach of Data Envelopment Analysis (DEA) and Goal Programming (GP) for portfolio selection, representing a pioneering attempt at combining these techniques within the context of portfolio selection. The approach expands on the conventional risk and return framework by incorporating additional financial factors and addressing data uncertainty, which allows for a thorough examination of portfolio outcomes while accommodating investor preferences and conservatism levels. The initial phase employs a super-efficiency DEA model to streamline asset selection by identifying suitable investment candidates based on efficiency scores, setting the stage for subsequent portfolio optimization. The second phase leverages the Extended GP (EGP) framework, which facilitates the comprehensive incorporation of investor preferences to determine the optimal weights of the efficient assets previously identified within the portfolio. Each goal is tailored to reflect specific financial factors spanning both technical and fundamental aspects. To tackle data uncertainty, robust optimization is applied. The research contributes to the robust GP (RGP) literature by analyzing new RGP variants, overcoming limitations of traditional and other uncertain GP models by incorporating uncertainty sets. Robust counterparts of the EGP models are accordingly developed using polyhedral and combined interval and polyhedral uncertainty sets, providing a flexible representation of uncertainty in financial markets. Empirical results, based on real data from the Tehran Stock Exchange comprising 779 assets, demonstrate the superiority of the proposed approach over traditional portfolio selection methods across various uncertainty settings. Additionally, a comprehensive sensitivity analysis investigates the impact of uncertainty levels on the robust EGP models. The proposed framework offers guidance to investors and fund managers through a pragmatic approach, enabling informed and robust portfolio decisions by considering efficiency, uncertainty, and extended financial factors.

In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.