MODERN RESEARCHES IN DECISION MAKING
Year:2018 | Volume:3 | Issue:2|Papers Count:12

In this paper a conceptual model is presented for categorizing agent based modeling research in terms of credibility in the field of human sciences. In addition an operating anatomy, based on the proposed categorization, has been created to implement agent based models. In this way, in order to actually implement the operational anatomy presented in this study. We focus on agent based modeling in the field of marketing related to the diffusion of innovation (television diffusion in Iran) and, implementing this anatomical step by step, for the diffusion of innovation for development of the model, communication networks (Preferential) have been considered. In the last step, considering the different scenarios for Preferential, we have examined the effect of increasing the number of communications and adding communication networks in the potential market to accept innovation (product and service).

Warranty is an important element of new products’ marketing. It is obvious that longer warranty length has direct effect on product’ s demand, but it can increase manufacturer's costs. The problem of this paper is maximizing the profit of the manufacturer, which consists of a set of revenue and cost elements. Although, most of researches in warranty area just examine the decisions for under-warranty products, in this research we proposed a model considering the role of both under-and out-of-warranty products on manufacturer’ s profit. For this, we propose a model for calculating the number of under-warranty and out-of-warranty failures in each spare part production period. The demand itself is dependent upon the time, sales price, and length of warranty. Sales price and warranty length are, respectively, inversely and directly proportional to the market demand. Therefore, the simultaneous decision about sales price and length of warranty is of considerable significance in order to maximize the profit. We solve the problem with the Particle Swarm Optimization and Optics Inspired Optimization algorithms. Through a systematic analysis and comparison, some interesting and valuable managerial insights are derived which are applicable for companies such as SANAM Electronic Company for which we test our proposed model.

Transportation is an important factor in the economic, production and service systems and it has allocated a significant portion of the gross domestic product (GDP) of any country. Production routing problem is development of the traveling salesman and vehicle routing problems in which scheduling and production planning are considered too. Generally, in the above problems it is assumed that there is a monopoly and the impact of competitors has not been considered. In this paper, for creating competitive condition, it is assumed that the earliest and latest time of competitor arrival is known. After modeling problem as mixed intger programming, valid inequalities are proposed, and their effects on the model have been investigated by the CPLEX software. These inequalities are provided for production routing problem with homogeneous and heterogeneous vehicles, separately. To evaluate the performance of the proposed valid inequalities, the results of them have been compared.

Correct selection of projects is one of the most important issues of project-oriented organizations for strategic management and resource constraints make organizations unable to invest on all projects. Therefore, it is very important to select the portfolio of projects to be implemented by the organization or the portfolio of projects that can meet the organization's goals and strategies in terms of organizational constraints. To accomplish this, and in order to make a more precise decision, the present study has used three stages of screening to form the project portfolio. In the first stage, projects has been prioritized based on criteria for project selection and using fuzzy multi-criteria decision-making techniques. Since the highest costs of each organization are due to project risk and late completion and may also be a priority project, but it is for the organization to have a high degree of risk and costs, so in the second stage, the screening Projects was conducted based on project risk based on the PMBOK standard using fuzzy clustering. Based on clustering results, the projects were identified in three clusters. The first cluster is projects that have a reasonable risk; second cluster have a high degree of risk, which requires special attention to senior management when needed, and the third cluster is critical risk projects that, if necessary, requires specific planning. In the next step, the experts assigned projects to companies according to the scope of each company's activities. In the final stage of the screening, using zero and one goal programming and taking into account 3ideal constraints of "profit", "budget" and "risk" projects were selected in each of the subsidiary companies.

In recent decade, humans' ability have rapidly increased in the way of producing and storage of data. By increasing the volume of data stored, the needs for a method by which information and knowledge resources within the data could be analyzed is sensed. Multi-criteria decision making methods and data mining techniques in recent decades helped managers in their decision making process. In this study, a method has been proposed for workshops clustering by combining data mining and MCDM approaches. In the proposed method, first data mining is conducted based on the AHP, K-means and Kohonen neural network approaches, then the performance of designed model is measured by validation indices like SSE and variance between cluster methods in order to determine optimal number of clusters. The food industry has been analyzed as a case study and based on the obtained results, four clusters as the optimal number of clusters have been introduced. The obtained clusters are named based on the variables of population distribution, income, value-added industry and industrial activities in the clusters. Finally, the suggestion is proposed in two sections of practical and research for decision and policy makers in industry and the other researchers in this field.

Today, in the worlds intensive and competitive environment with the existence of different industries which result increases of environmental pollution and its destructive consequence many different organization are concerned with environmental issues in supply chain and selecting the appropriate strategic indicators in green supply chain management which is one of the most important approaches for improvement in the competitive world. Since Esfahan Mobarakeh Steel Company is one of the major polluting industries in the country, in this study the aim is to identify and prioritize strategic green supply chain indicators for the Esfahan Mobarakeh Steel Company. For this reason, after extracting the strategic green supply chain indicators and by putting these indicators in the form of balanced scorecards, using Delphi technique, the final model of development strategic supply chain management was obtained and finally, using the logarithmic fuzzy preference programming was prioritized. According to result, among balanced scorecard perspective, internal processes alocated the highest priority. Among the indicators of internal processes Competitor Pressures, Growth & learning perspective Regulatory Pressures, Customer Market Pressures, Financial perspective Economic Benefii are considered to allocate the highest priorities, respectively

The main purposes of this research are to design and develop a tag-oriented deep artificial neural network as recommender system for online shops in order to increase online sale. The chosen research method is design science and experimental in which in the evaluation measurement step design of experiment has been used. In order to improve the performance of the recommender system user tags have been used and deep neural networks algorithm has been used to recommend movies to users. An experiment has been designed and implemented in which the designed and developed recommender system has been compared to collaborative filtering method, which is one of the established methods of recommendation, in the evaluation metrics of precision, recall and f1 for the recommendation of movies. The result proves that the developed recommender system with utilizing the tag-oriented deep artificial neural network outperforms the collaborative filtering in all of these metrics.

Crowdfunding Platforms are transformed to Websites by which people will be able either to back financially new Ideas or to try to seek investment (Fundraising) for their products and services. Whereas in recent years this kind of investment is highly advertised in press circles and many success stories are reported in this kind of investments, many people have referred to these newly developed platforms (websites) and many projects have been launched on these various sites. In spite of dramatic growth of the turnout of the people in the role of investors or in the role of investment attractors, the percentage of successful projects in absorbing complete investment has experienced decreasing growth. The works, which have been conducted with regards to prediction of successful projects, are merely concentrated on optimization of prediction models to improve the prediction procedures, but in these studies the prediction scenarios are not dealt with. But we should consider this fact that without identification and definition of prediction scenarios, we couldn’ t reach to the main goal of this undertaking which is to present effective suggestions for success improvement of the launching of crowd funding projects. In this paper, the main objective is to suggest some proposals based on the available information regarding the status of the projects through success prediction of launched projects by means of Business Intelligence: (BI). To realize this, in this paper by using business intelligence features, initially, we have presented a comprehensive model for prediction issues in target business based on Key Performance Indicator: (KPI). Then, according to extracted requests from the model, we gathered a large amount of data from the Kick Start website which consist of the project records, user records, temporal data and projects users profile in famous social media. In the next step, by consideration of extracted model, based on business intelligence, we implemented the corresponding model by means of data analysis for prediction and evaluation of the financial pledges of the implemented projects. Our practical results show that prediction models can predict effectively the success of the projects and also they can suggest, by means of identification of projects success factors, proposals in order to improve the success probability of the projects.

There are two difficulties in implementing an objective evaluation of the performance of decision-making units (DMUs). The first one is how to treat undesirable outputs jointly produced with the desirable outputs, and the second one is how to treat uncontrollable variables, which often capture the impact of the operating environment. Given difficulties in both model construction and data availability, very few published papers simultaneously consider the above two problems. The objective of the present paper is to propose a novel pair of data envelopment analysis (DEA) models for measurement of relative efficiencies of DMUs in the presence of non-discretionary factors, undesirable factors, and imprecise data. Compared to traditional DEA, the proposed DEA approach measures the efficiency of each DMU relative to the worst practice frontier, also called the input frontier, and is called the worst relative efficiency or pessimistic efficiency. The pair of proposed DEA models simultaneously takes into account the crisp data, ordinal preference information, and interval data, as well as undesirable factors and non-discretionary factors, for measurement of relative efficiencies of DMUs. The results of this study are not only useful for the performance evaluation method, but also have policy implications for industrial and academic researchers. A numeric example has been provided to illustrate the applicability of the DEA models.

Today, the portfolio optimization and its management is one of the most important areas in financial decision-making. Therefore, picking a portfolio of stocks that could bring the highest rate of return and the lowest risk investment for its holder simultaneously has become one of the main concerns of the economic actors. But in choosing the optimum portfolio just these factors are not decisive and according to the economic environment, many factors can affect this process which should be identified and considered. Therefore, in order to cover these matter multi-criteria decision-making approaches should be used. On the other hand, when the real-world conditions and restrictions, including restrictions on investment in any of the stocks and cardinality constraint are considered in portfolio optimization, the problem is not easily solvable by means of usual mathematical methods. Specially when there are a large number of assets in the portfolio evaluation process. Regarding this fact, the main purpose of this paper is to solve portfolio optimization problem by using the Data Envelopment Analysis (DEA) and Symbiotic Organisms Search (SOS). Finally, the model used in this study has been solved with real data and the results have been analyzed. The results of this paper demonstrate that the proposed approach has been successful in portfolio optimization and has been able to properly interact with the actual limitations and effective variables of the market.

Natural and anthropogenic disasters annually give rise to huge financial and human losses. Disasters make a large amount of demand for relief items (e. g. medicine and hygiene products) in the affected area. There is a great deal of importance in appropriate response to demand of these items, as they have a vital role in preserving injured people's life. This paper presents a bi-objective location-allocation model for blood supply under uncertainty. The objectives are minimizing the blood shortage in blood centers and also minimizing the operational costs including cost of transporting temporary blood facilities and the cost of blood collection and transportation in a multi-period context. Due to the lack of information about the real values of some input parameters such as demand, amount of donated blood and also unit operational cost in disaster, these are considered as uncertain parameters in order to make more precise decisions. Robust optimization approach based on box uncertainty set is used to tackle the model uncertainty. Moreover, Lexicographic weighted Tchebycheff method is used to obtain the pareto-optimal solutions. Finally, a numerical example is presented to compare the performance of the robust model and its deterministic counterpart.

In 360 degree feedback model several groups of raters with different perspectives provide assessments typically in Likert scale format. Due to the qualitative and subjective nature of the assessments, these data are highly uncertain and divergent. This problem causes that aggregation of the data in 360 degrre model, with traditional average based methods be inaccurate and invalid. Regarding the importance of the aggregation problem in 360 degrre feedback process and lack of a suitable solution to cope with it, in this paper a new model based on Evidence Theory has been proposed to aggregate its assessments and model the uncertainty contained in it. In the proposed model, first the evaluation data obtained from each rating group has been aggregated and its uncertainty has been modeled using a basic belief assignment. In the next step, the evidences obtained from rating groups have been aggregated using evidence combination rules. To design the model, various methods to transform feedbacks to basic belief assignents, various evidence combination rules and different criteria in extracting the final results of the model, have been investigated through a simulation study. In the simulation process the model's performance in 27 different states, defined based on different combination of model's parameters, and using ten thousand simulated records, has been examined. Results reveal that the proposed model, compared to the traditional average based aggregation, significantly reduces error and increase the accuracy of the results in 360 degree feedback model. In addition, other benefits of the proposed model have been explained in the text.