نشریه پژوهش های نوین در تصمیم گیری
سال ششم شماره 2 (تابستان 1400)

Portfolio optimization problem is one of the most interesting and practical issues in the financial markets. Portfolio optimization is applied as an applicable and efficiant tool for helping investors in their decision making by allocating wealth to the different asset with controlling the return and risk. The purpose of this paper is to develop a novel portfolio selection and optimization method in portfolio selection problem by considering the return and risk under fuzziness. In the paper, two possibilistic programming model is developed by applying measures of the probabilistic and possibilistic mean and downside risk of fuzzy return. The performance of the proposed models was evaluated by using historical data introduced by Markowitz and data of the Tehran Stock Exchange. The results of the paper show that the proposed models are able to propose an appropriate portfolio for investors with optimizing the return and risk, simultaneously, in terms of different investment strategies.

With the expansion of online sales sites, the desire to online shopping is increasing day by day due to its many benefits. Most customers, before deciding and choosing a product, check the previous buyers of the product opinions and choose their product based on it. There are different brands in the market and on the other hand reviewing a large volume of comments to make a decision to buy a product is a big challenge, the existence of automated text mining and sentiment analysis tools to review users' comments and opinions can be very useful and can be used as a way to rank products based on customer feedback. In this research, by examining more than 4500 customers and experts reviews about 70 products, the features considered by customers and sales site specialists have been extracted based on text mining methods, and by using aspect-based sentiment analysis, a product ranking system has been created.

Artificial neural networks and fuzzy sets theory is one of appropriate techniques to solve engineering problems in order to predict variables of supply chains and, also, of systems with high complexity and implicitly which provide no sufficient data. The main advantage of this technique over others, which lies in the short time of data examination and algorithm discovery, is in the line with that prediction and/or its influence on the future. Adaptive neuro-fuzzy inference system (ANFIS) combines neural networks with fuzzy logic concepts and is able to use capabilities of both in one framework, the inference system of which is in conformity to fuzzy "if-then" rules having potential for learning how to approximate non-linear functions. Among applications of this technique are to define variables based on the past data and their impacts on the past temporal sequences in order to predict future conditions. This research, therefore, uses neuro-fuzzy technique in order to blood donation based on data from the past years. Since each technique has its own error rates, Metaheuristic Whale Algorithm is used to reduce errors of ANFIS by improving the parameter values of neuro-fuzzy systems. The obtained results show a reduction of the RMSE of prediction from 0.00261 to 0.00153 in the ANFIS-WOA and a 41% improvement over the ANFIS method.

Deciding whether or not to invest in a venture is a strategic decision that leads the financial resources of the venture capital companies. This research intends to provide a framework for evaluating and ranking ventures that apply for capital, considering various success factors. The novelty of this research is the application of Prospect Theory as a framework which is based on behavioral strategy in the decision-making of venture capitalists. To conduct this research, first, by reviewing the literature, the most important factors influencing venture failure has been identified, then experts’ opinion who are active in venture capital companies has been gathered. Next, a framework based on Prospect Theory has been developed. Finally, this framework has been used in one of the venture capital firms to evaluate the twelve ventures seeking capital. According to the results,, instead of making decisions based on final results, human beings make decisions based on the perceived value of gains and losses. At the same time, the value of losses has a greater effect on decision-making.

Due to the increasing requirement in the manufacturing industry and the rapid development of technology, the traditional supply chains face many challenges such as changes in demand and transportation problems so that they need the flexibility in capacity, lead time, and distribution channels. In this research, in order to overcome the problems of the traditional supply chain, a supply chain based on ICPT in the MTS production environment called smart supply chain is considered where including four levels of manufacturer, warehouse, distributor, and customer that the goal is to maximize profits and minimize lead time. At first, a Mixed Integer Non-Linear Programming was proposed for the problem, then a sample problem was solved by the augmented ε-constraint technique in GAMS and the results were analyzed. Two scenarios of increment of demand and adding a new product were examined which all of them showed the accuracy of the model and the efficiency of the proposed method.

The growing population density in cities and the need for infrastructure services to meet the demands of citizens on the one hand and the increasing development of digital technologies on the other hand, have led governments to form smart cities. Smart cities employ the capabilities of emerging technologies such as the Internet of Things and Big Data to collect and analyze smart devices’ data for improving infrastructure and enhancing the quality of public services. These issues lead planners to a huge amount of financial investment and innovation in business models. The smart city encompasses a variety of stakeholder groups and IoT creates novel business opportunities through collecting and analyzing the data of sensors and smart devices. In this regard, identifying effective indicators for designing a business model is needed to ensure value creation. The purpose of this research is to identify and classifying the main indicators for designing business models of IoT Big Data projects in the smart city. For fulfilling this purpose, the meta-synthesis methodology is employed. Among the 212 articles identified, 48 met all inclusion criteria and were reviewed in-depth. After content analysis, a conceptual model was designed and 21 Indicators were extracted based on the structure of the conceptual model. These indicators are classified into five categories: value creation drives, value creation nodes, value exchange, value capture, and environmental drivers.

Organizations in dynamic commercial environments, use agile supply chain as the key strategy for confronting market instability. The purpose of the present research is to have a systematic review of Agile Supply Chain literature using meta-synthesis method in order to provide an overall model in this field. Meta-synthesis technique is implemented in this study utilizing the seven-step method of Sandelowski and Barroso (2006). After taking the respective steps of this method and upon analyzing various articles, 41 articles are finally inspected and 167 codes are derived. Eventually after categorizing the codes, three groups including agility consequences, agility enablers and agility drivers, which affect and are affected by agile supply chain, have been introduced to presenting a pattern. Finally, a general concept of competitive advantage for agility consequences, six concepts of market sensitivity, information/technology integration, flexibility, integration, strategic partnership and leadership for agility enablers and five general concepts of economic, market & competition, environment, social and technology for agility drivers have been identified.

This paper proposes a bi-objective mixed-integer linear programming model for formulating a lot- sizing and scheduling problem for the perishable yogurt industry under demand uncertainty. The objectives of the proposed model are to simultaneously minimize the overall cost and the total production completion time.  The proposed MILP formulation integrates many distinctive features of yogurt processing, including shelf-life constraints, setups, packaging rates, minimum and maximum lot size limits, future time for holding products, and fuzzy demand. Additionally, the proposed model, including inventory control, is a multi-product and multi-period model hence, it is categorized as an operational-strategic model. We introduce a hybrid approach focused on fuzzy possibility programming and a fuzzy goal programming approach for solving the suggested bi-objective model, where possibility, necessity and credibility measures are adopted according to the decision makers’ preference.  Compared to the traditional model of lot sizing and scheduling, better decision-making and sensitivity analysis for DMs can be made based on the three obtained efficiency values. Data from the yogurt plant were used to assess the feasibility of the proposed model and solution approach. The results obtained from applying the method and sensitivity analysis showed the effectiveness of the mathematical formulation as well as the proposed solution method.