نشریه پژوهش های نوین در تصمیم گیری
سال هفتم شماره 1 (بهار 1401)

In today's world where dynamics of data are so important, using of Spatial Data Infrastructure (SDI) can provide a platform for spatial data sharing. Replacing SDI with GIS in Geomarketing Creates new chapter on Spatial Information-Based e-marketing. SDI-based Geomarketing fixes the flaws and shortcomings of spatial information layers in GIS-based Geomarketing. In this research, a new model of location-based marketing is presented, which uses spatial data infrastructure for the first time. In this regard, based on the available spatial data, Semnan city is divided into 139 statistical areas or urban areas. By sharing the databases of the related executive agencies in the context of spatial data infrastructure, the main demographic and economic indicators related to bank marketing were selected. Through statistical methods of correlation coefficient and multivariate regression, the effective indicators on the number of bank branches have been calculated. The number of businesses index with a coefficient of 0.598 had the greatest impact on the number of bank branches in each region. Finally, using the mentioned demographic, economic and geographical indicators and using the TOPSIS method, the regions of Semnan city were prioritized in terms of banks' marketing indicators, and region 70 has the first priority. The results of the Multiple Attribute Decision Model are considered as a dependent spatial variable in the SDI-based Geomarketing model.

In recent years, due to resource constraints, environmental concerns on the one hand and the importance of corporate social performance, laws and intensifying government pressures and the attention of social activists and human rights on the other, logistics networks or the closed and stable supply chain has been increasingly considered by researchers. The logistics network or closed-loop supply chain includes both direct and reverse logistics networks, and purpose of designing it is to combine environmental considerations and constraints through the collection and recycling of used or defective products and necessary actions for reuse with a traditional supply chain. In designing these networks, social goals such as increasing employment as a goal and very important social issue are considered. In the present study, a multi-objective mathematical model with intuitive fuzzy approach to optimize the closed-loop logistics network with emphasis on employment development and development of recycling units in the rubber and plastics industries of Bushehr province is presented. In this regard, first, the various objectives in the research background are evaluated with fuzzy Delphi technique and three priority objectives in Bushehr province are approved and introduced. Then the three objectives are weighted with intuitive fuzzy approach and three-objective model with emphasis on the goal of employment development are solved by LP-METRIC method and GAMS software.

Due to the key role and importance of using agent-based modeling and simulation tools in various applications, such as urban management and planning, social networks, financial markets, traffic flows, and crisis management, it is worthwhile to evaluate and quantitatively compare these tools based on a well-defined standard framework. So far, several studies have reviewed and compared agent-based modeling and simulation tools from different aspects. However, none of the studies are quantitatively performed based on a well-known standard framework. To fill this research gap, in this paper, five of the most widely used agent-based modeling and simulation tools including AnyLogic, NetLogo, Repast, GAMA, and MASON were compared and evaluated based on the well-known ISO 25010 standard, regarding the quality in use, by Goal-Question-Metric (GQM) method. The evaluation results show that AnyLogic tools are of better quality than others, and this is due to the attractiveness of the tool from the user's point of view and less effort to develop an agent-based application compared to other tools.

One of the efficiency assumptions of stock market is that investors act rationally, but the results of researchers' scholars show that in many cases, investors in the stock market have irrational behavior. The present study show irrational behavior. In the present article, two types of personalities are considered for shareholders in the stock market. The first group has an impulsivity personality and the second group has an oscillating personality. Traders who have an impulsivity personality buy stocks when prices increased and sold when prices decreased. On the other hand, there are oscillators who buy when prices dropped and sold when prices increased. The innovation of the present study is to provide a dynamic model for analyzing the behavior of investors by agent-based modeling and considering their various personalities of trader’s behavior in NetLogo software environment. Finally, after designed model, the validity of the model is assessed which results indicate high validity of the designed model in the simulation environment. The results show that the percentage of shareholders with oscillating and impulsivity personality in the price balance is distributed to 52% and 48% between impulsivity and oscillating shareholders.

Statistical process control and maintenance management are two key tools for controlling production processes. However, These two tools are traditionally separated (both in science and in business practice), their goals overlap a great deal. Their common goal is to achieve optimal product quality, little downtime and cost reduction by controlling variances in the process, that achieving these goals will increase the level of reliability of product quality. Using these two tools together can have better performance in terms of cost and quality for the organization; Therefore, in this research, an integrated model of statistical process control and maintenance management has been designed by considering the costs of two tools in Kish Wood Industries Company. The optimization criterion is to minimize the average total cost per unit time of these two systems. For this purpose, from MATLAB software and grid search approach to find the optimal values of sampling size (n), width of control limits (L), sampling interval (h) and number of sampling times during the planned maintenance (k) is used.The values of n, h, L and k for the laminating process were calculated as 5, 1, 2.9 and 30, respectively. The results of this optimization in the studied process show that the duration of the planned maintenance should be increased compared to the existing program, and this can be due to compensatory maintenance when the false alarm is out of control of the process.

In this research, a mixed integer nonlinear programming (MINLP) model is presented to optimize a closed loop supply chain in dairy industry. The mentioned supply chain includes production, distribution and sales. The proposed model contains two objective functions. The first one is economic and seeks to maximize total profit and the second one is aiming to minimize CO2 emissions and COD as environmental objective function. Also, a green vehicle routing problem is presented to addresses the relationship between distributor and customers. This problem includes several products, several warehouses, several types of vehicles and several time periods. Due to complexity and NP-hardness of this problem, the NSGAII algorithm has been applied to resolve the problem. In order to evaluate the validity of this algorithm, some small problems have been designed and solved in Lingo software. The results also were compared with the outcomes of the meta-heuristic method. Finally, the effect of fat loss, as one of the most affecting factors on dairy wastewater pollution, was also investigated. The results showed the efficiency of NSGAII algorithm and the direct effect of fat loss rate on wastewater pollution.

In today's competitive world where companies are faced with a huge amount of customer information due to the growth and development of information technology and the creation of various databases, the use of customer relationship management tools that can accurately and timely identify and monitor customer needs and expectations Becomes more necessary;one of the techniques that can play a key and fundamental role in this period along with this important category is data mining of customer databases. The purpose of this study is to analyze customers clustering based on the WRFM model using non-supervisory data mining methods;the researchers seek to discover the existing rules and patterns to provide more effective strategies for each group of customers, especially key customers, in order to have a better profitability and performance for the organization.Using available purposive sampling method, 64858 samples have been selected from the database of customers who have used hygienic and cosmetic products in the period of 2018-2019.The weight of WRFM attributes has been determined by surveying 3 sales experts (senior managers) of the company. Clementine and SPSS soft wares were used for data analysis. According to the research model, 4 customer categories: Specific and key, Potential golden, lost Uncertainty, New Uncertain were identified and named, and different strategies have been presented for each of these customer categories Also the result show that K-mean clustering with six clusters and purity of 0.744% had better performance than other clustering methods.