Modern Research in Decision Making

Abstract Multi-methodology is a term used by Critical Operations Researchers for combining several OR methodologies in an intervention in different problematic situations. They believe that, even though OR methodologies are rooted in the different paradigms, they can be used as alternatives or complementary in a same intervention considering their weakness and advantages. In this paper, we have tried to improve the multi-methodology framework developed by other researchers using the Social Network Analysis approach, as well as the capabilities of the UCINET and NetDraw software packages. So, we have determined the complementary and alternative OR methodologies founded in the different paradigms and schools for the domestic dimensions of problematic situation in Iran. Calculation of concepts such as degree centrality, isolated points and etc. have shown the most powerful Operations Research methodologies and the gaps exist in OR methodologies, as well as the fields necessary to be improved. Application of the results will be useful for Operations Researchers.

Abstract In this paper, the considered set covering issue is a stance of location-allocation issue in which the residents of each the eighteen district of region 6 of Tehran, have definite requirements to receive services (medical first aid) from some of emergency centers. We want to arrange so that at least one center, in each district, provides services to people who have been exposed to incidents. In this regard and to emplacement of these relay centers, a developed multi-objective model is used that has objectives such as minimizing the establishment cost of relay stations, maximizing the total covered population and minimizing the total distances between demand locations and allocated service providers. Since, each of the centers has only an ambulance and these ambulances being out of reach during providing services, possible limitations have been used in this model. So, in this research a combination of multi-objective decision making (MODM) methods and set covering problem (SCP) is used to specify the location of relay centers in 18 districts of region 6 in Tehran. The chosen solution is simulated annealing method that is performed using Matlab software. At the end, the model is implemented through the Ultra Innovative algorithm of particle mass optimization to compare to the answers of the Simulated Annealing algorithm. Results show that both algorithms give the same answers for the considered locations.

Abstract This paper concerns studying a three-level supply chain and coordinating price and inventory decisions using game theory. In the retailing level, competing exist between retailers and they compete each other to gain higher demand. Demand is deterministic. The main challenge is modeling a multi-level supply chain and formulating the game between players to determine price and replenishment period. Integer multiplier method adopted as replenishment mechanism and three-level nested Stackelberg-Nash game used to formulate the optimization problem that produces a three-level programming model. Retailers formulate the bottom-level Nash game, the whole retailers play the middle-level Stackelberg game with manufacturer, and both levels as a group formulate the top-level Stackelberg game with supplier. Questions that answered at the end are for supplier; (1) what is the optimal price for raw material sold to manufacturer? In addition (2) how frequent should the raw material procurement take place? For manufacturer: (1) what is the optimal product wholesale price? In addition (2) how frequent should the production process take place? For each retailer: (1) what is the optimal price for products sold to market? and (2) how frequent should the product procurement take place?

Abstract In this paper, a fuzzy optimization model, including the location of the tank and the wastewater treatment plant and wastewater allocation to agricultural areas for network design is presented. Firstly to determine the strategic decisions to locate and tactical allocation decisions for certain modes, the base model proposed then the model has been developed with regarding the uncertainties in the parameters using fuzzy approach. In addition, this paper has proposed a new approach according to using of wastewater in agriculture to increase crop health and reducing microbial contamination, with considering the different quality of effluent. Finally, a case study in Megacity of Tehran for model validation is applied with helping Agriculture Organization and Tehran Regional Water Company and, also, sensitivity analyzes are conducted. The results shown that the proposed model can help decision-makers to manage the water and sewage considering the consideration of proposed model and the contribuionts of that.

Abstract Investing in the stock market, is a significant part of the country's economy. Increasing profits and reducing the risk of investing in the stock exchange has always been a major concern for investors. Also, Stock markets are affected not only by macroeconomic parameters but also by thousands of other factors. This research aims to provide a model in which future stock potential is forecasted by considering the technical analysis indicators by the fuzzy neural network. According to the forecasts, the mathematical model based on factors such as the return, variance, and skewness of the stock portfolio will be optimized. Then, this model is solved using the genetic algorithm. This research is an applied research in terms of purpose and is descriptive in terms of method. The empirical results shows that the proposed models will provide more profit to investors regarding variance and skewness comparing to traditional models and stock market index.

Abstract Due to the changes in the condition of every supply chain it is possible that the first optimal network design become inefficient .These changes can be caused by different reasons like changes in amounts or location of demands, changes in suppliers, costs, taxes, government regulations etc. So, it is necessary to redesign networks to obtain their new optimal configuration for adopting to new conditions. This paper proposes a new mathematical model for a multi-product supply chain network redesign problem. Since one of the main problems of real-world cases deals with is transportation, routing constraints in every echelon (plant to warehouse and warehouse to customer) are considered in the proposed model. In addition, a capacitated and heterogeneous fleet of transportation in a split delivery system is used to deliver demands of the first echelon to the warehouses and demands of the second echelon to customers. On the other hand, due to the production capacity of the manufacturers, some portions of demands is satisfied by outsourcing. The outsourced products are moved to the warehouses directly. The proposed model is validated through an illustrative example by some strong servers in a website which is used to solve the mathematical modeling problems. Then, some sensitive analysis are performed in order to investigate the effects of some key parameters on the optimal cost and figure of the network. Finally, we can reach the conclusion of need to consider the transportation and routing costs, and it is necessary to account outsourcing and some other costs and benefits in our redesign problems as well.

Abstract Cross docking is a new strategy in supply chain management. In a cross docking system, items move directly from receiving dock to shipping dock, without being stored in the warehouse or distribution center. This paper studies the problem of truck scheduling in a distribution system including multiple cross docks with no intermediate storage. In this model of cross docking scenario preemption of trucks is allowed. Therefore, both inbound and outbound trucks can arrive and move out the distribution center intermittently. In this paper a mixed integer linear programming model has been developed. Moreover, to solve the problem we proposed three metaheuristics algorithm based on tabu search, simulated annealing and genetic algorithm. In this regard, we propose a novel solution encoding scheme for the problem. And the algorithms are tuned by Taguchi method. Using numerical experiments, the genetic algorithm outperforms the other two algorithms Besides.

Abstract Needs of affected people for relief commodities in the natural disasters often make crisis worse. Therefore location of distribution of depots and setup these depots wherever accelerate fast distribution have a special importance. In this paper a location-allocation model of distribution facilities with the objective of minimizing the costs has been proposed. So that this model under uncertainty allocates the established depots to affected areas under different scenarios considering dynamic demand in which over the first 72 hours after the beginning of the crisis. Also two kinds of reliable and unreliable depots and backup depots with the possibility of damage to facilities and routs has been considered to increase the reliability. The proposed model has been applied on a case study the earthquake in Tehran city. Then we have solved the problem for a different number of facilities and performed a sensitivity analysis and validation process. Also the demand for relief has been forecasted for the year 1398 with the ARMA method and the results have been reported. Computational results show that location of distribution depots and allocation of the areas according to the proposed modelcan improve the aid and help managers to manage the crisis.

Abstract 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).

Abstract Today, the most important aspect of product design is based on the needs and requirements of customers, therefore, the Quality Function Deployment (QFD) approach is one of the ways that is used to increase customer satisfaction. Due to the uncertainty and inaccuracy of the properties in the relationships, the fuzzy coefficients are often used in planning QFD. In the present study, the fuzzy linear regression approach is used to identify inaccurate and ambiguous functional relationships between customer requirements and engineering indicators. In this approach, for non-fuzzy data situation, the optimal values of the parameters would be determined based on the output set of the regression model so that it has a membership degree greater than or equal to h, which indicates how much fuzzy the output of the fuzzy regression model is being. In this study, the aim of using a fuzzy linear regression model is to identify the functional relationships in QFD to optimize h value in the industrial lama electronics industry. The results of this study indicate that the values of the range of coefficients in optimal h model, when compared to zero, give better data and increase the reliability of the system.

Abstract Tobin index is one of the most important indices in the world of investment used as a criterion for evaluating performance of the firms to decide for the right investments. However, there are some ambiguities in the accuracy of the results based on this index that have prompted researchers to pursue estimation of this index based on the other financial indices. But Tobin index is a dynamic index and as it is based on the market price, may be changed its value at once, therefore it is not logical to be predicted using methods as multiple regression that attempt to predict precise value of depent variable. this research has reviewed methods based on the exact prediction like regression to judge about Tobin index by the other financial indices and it has recommended using discriminant analysis methods such as logistic regression and artificial nervous network. Discriminant analysis is a method to categorize a set of the observations into one of two or several determined groups, so that observations within each group can have the most similarity to each other. Therefore, this research has analyzed Tobin index discrimination using financial information of 184 accepted firms by Tehran Stock Exchange in the financial year leading to 29 of Esfand in 1393 by logistic regression and artificial nervous network and it has reported results of two techniques and has compared output of the two techniques to each other.

Abstract Vehicle routing problem is one of the most important problems in transportation programming. Vehicle routing problem plays an important role in distribution companies because the much of the system costs are related to it. In this paper, a mix integer nonlinear programming model is presented considering the existing demand in distribution companies and real world's restrictions, including Stochastic service time, fuzzy demand and time window limitation. Then, the nonlinear model is equated with the linear model using analytical techniques, for its validity evaluation, GAMS software was utilized. Also, With respect to the fact that this problem is NP-Hard, non-dominated sorting genetic algorithm and multi-objective ant colony optimization algorithm are designed. To demonstrate the efficiency of designed algorithms, evaluation indicators of multi-objective meta-heuristic algorithm's efficiency are utilized. The results indicates that the non-dominated sorting genetic algorithm is more efficient. The issue of the company in questioned via the proposed algorithm. And according to company's management need, practical approach are presented.

Abstract Failure modes and effects analysis technique (FMEA) is One of the high usage tool to identify and prioritization of risks in the industrial, production and service environments. traditional FMEA has many shortcomings, Therefore many researches have been done to enhance the performance of FMEA. Also, in this study, a new approach has been proposed to deal with shortcomings of traditional FMEA. In this new approach Rough numbers have been used for representation of vague and subjective information and an improved method of gray relational analysis (GRA) as the Gray relational projection method (GRP) to prioritize potential failure modes. In the proposed method, evaluation of risk factors by members of the FMEA team has been modeled by Rough numbers and then The GRP method determines the priority of failure modes. To illustrate the performance of the proposed method, an example is used for the ranking of failure modes and evaluating and comparing the proposed model. The new approche have been overcame the shortcomings of traditional FMEA like multiplication of risk factors and the resulted discontinuous amounts and inattention to the weight of risk factors, by considering the weight of risk factors and using a method of prioritization by using GRP method. The proposed approach is achieved more affective and more accurate prioritization by covering ambiguity and uncertainty in experts’ judgements. The results indicates that in comparison with the traditional FMEA, a more reasonable and more accurate ranking have been resulted for FMEA method by combination of Rough numbers and GRP method.

Abstract The petrochemical industry is one of the most important industries in the world where optimal management and decision-making in its activities will bring about great economic benefits as well as prosperity and development of related industries. This paper deals with the issue of petrochemical product supply chain management. A multi-objective optimization model is developed in which the strategic, long-term economic, social and environmental goals of the petrochemical industry are achieved. For this purpose, first using the Epsilon constrained evolution method, economic objective is considered as objective function and social and environmental goals are constrained as Epsilon. Then, the Pareto front is obtained from efficient solutions and in this front, the solution with the least deviation from the ideal is selected as the most efficient solution and recommended to industry managers. The data uncertainty in the proposed model is controlled using a robust feasibility planning approach. The numerical results show that not only the optimal fluctuation in the proposed robust approach is much less than the nominal value approach but it also significantly reduces the constraint flaw which reduces risk in decision making. In order to solve the proposed large-scale problem, the Banders decomposition method is applied based on the Epsilon multiple-constraint evolution method. Numerical results show that the proposed approach significantly improves the mean, standard deviation, and runtime in three quantitative measures and enables large-scale problem solving.

Abstract Ensuring the disposal of hospital waste is a major challenge for hospital waste management. the outsourcing process waste disposal can be a good alternative. The aim of this study is to select a company to outsourcing waste disposal to the hospital To help right decision making for managers. The research in terms of purpose is applied and the way of doing is descriptive-exploratory. The research community of Ministry of Health and Medical Education experts is about 10 people. By reviewing the theoretical literature, the most important criteria were identifing an screened by experts and Fuzzy Delphi method. Then The combined method of analytical network process based on fuzzy DEMATEL was used to determine the relationships and weights of the criteria, and the modified Grey Relational Analysis method was used to rank the companies. The results of analyzing the data collected through questionnaire and DEMATEL method showed that "financial" criteria is influencing and "serviceability" influenced for success in the outsourcing process. The "service accountability" sub-criterion has the highest weight and the first priority.

Abstract Today, one of the main components of the sustainable development of organizations and among the methods of overcoming economic and technological challenges is innovation. Evaluation of the innovation process is an important issue in knowledge-based companies. One of the innovation evaluation methods is two-stage DEA, which allows considering multiple inputs and outputs and the internal structure without any special assumption regarding the production function. In this research, the theory of cooperative games was used to determine the optimal value of intermediate variables. A non-linear cooperative model for a consecutive two-stage structure with surplus input and output and conditions to the variable scale of development and two calculation procedures of variable first stage efficiency and variable second stage efficiency were proposed to solve it. Considering the two stages of research and development and commercialization for the innovation process, the efficiency of the stages and the total of 9 knowledge-based information and communication technology companies was calculated. The results showed that only one company is efficient in both stages and research and development has a greater contribution to the ineffectiveness of innovation in companies compared to commercialization. The results give important insights to managers to identify the source of inefficiency and prioritize resource allocation.

Abstract Delocation is one of the areas of location science that, according to decision-makers' policies and environmental impacts, the existing facilities are reduced or closed. In this study, a merging facility delocation model for the so-called military banks of the Islamic Republic of Iran has been developed. In terms of governance and in order to convince the stakeholders, an equity approach is considered using fuzzy constraints. Also, due to the diversity of the brands and many factors to determine the importance of branches, the efficiency score of the data envelopment analysis method using the input-based CCR model was considered as the weight of the branches. To solve the problem, the augmented Epsilon-constraint method has been used. The results of the study for branches of banks showed that the objectives of equity and efficiency were inconsistent and produced non-dominated solution. Qom city has been considered to better understand the issue of facility merger. The model has been solved using GAMS software and Bonmin solver.

Abstract The rapid growth of online retail and the emergence of advanced technologies have necessitated a reassessment of quality maturity. Quality 4.0, as a novel paradigm in the Industry 4.0 era, emphasizes intelligent communication, automation, data analytics, and system integration. This study aims to provide a comprehensive framework for evaluating Quality 4.0 maturity in the Iranian online retail sector. A mixed-methods approach was adopted and carried out in four phases: first, a systematic literature review identified 18 initial dimensions of Quality 4.0; second, the conceptual model was validated through a survey of 194 e-commerce experts and structural equation modeling, resulting in the confirmation of 10 core dimensions; third, a five-level maturity model (ranging from initial to leading) was designed based on literature and expert opinions; and fourth, a fuzzy inference system was developed to assess and measure maturity levels. For validation, the framework was applied as a case study in Kourosh E-commerce Company (Okala). Findings indicated that data, analytics, culture, leadership, and system integration were the most influential dimensions. The proposed framework provides a practical tool for managers and decision-makers to guide the adoption and implementation of Quality 4.0 in online retail.

Abstract Export development has always been regarded as one of the key macroeconomic strategies for countries to overcome economic crises and achieve sustainable growth. Classical economic theories, neoclassical theories, and new growth theories emphasize the simultaneous role of exports and technology as engines of economic growth. In previous research, knowledge-based export development issues have often been examined at three levels—governance, organizational, and marketing—primarily from a macro perspective and with policy-making objectives.
This study aims to provide a practical process model for the export development of Iranian knowledge-based companies through firm internationalization, identifying marketing strategies for their export expansion and presenting an Interpretive Structural Model (ISM) of these strategies. The research adopts a sequential mixed-methods approach. In the first stage, key strategies were extracted through thematic analysis of semi-structured interviews with export managers of knowledge-based companies. In the second stage, using the Interpretive Structural Modeling (ISM) method and the participation of academic experts and professionals in the knowledge-based sector, a three-stage model was developed, comprising market learning, market building, and market leadership. This model, consisting of 15 strategies, outlines an operational pathway for knowledge-based export development.
Moving beyond merely discussing barriers and drivers, this study focuses on the marketing dimension with a novel approach, presenting a multi-stage operational model that can serve as a practical roadmap for knowledge-based export companies at different internationalization levels. Additionally, the research findings can serve as a framework for policymakers at the governance level and managers of knowledge-based companies to strengthen their export position in global markets.

Abstract The primary objective of this research is to propose an expert system for empowering the sales network in the insurance industry using a fuzzy inference system. This study is applied in nature and employs a descriptive-survey methodology. The statistical population consists of insurance industry experts, managers, sales specialists, and agency affairs professionals. In this study, a range of factors influencing the empowerment of insurance agents was first identified and presented to experts. Following in-depth interviews, five main factors were ultimately selected. Structural equation modeling was used to determine the relationships among the identified factors. The Cronbach's alpha coefficients for all factors ranged between 0.876 and 0.963, indicating acceptable reliability of the measurement. Additionally, the correlations among the research variables were assessed using Pearson’s correlation test. Finally, after designing the conceptual model of the research, each factor was evaluated within the proposed conceptual framework using a fuzzy inference system (FIS) implemented in MATLAB. The proposed system was also applied to three groups of insurance agents. The results of this study demonstrate that the proposed model has a high capability in assessing the key factors of insurance agent empowerment and can effectively support decision-making processes in insurance sales network management. The system’s flexibility and interpretability make it a practical tool for improving the performance of both agents and insurance companies, enabling the implementation of empowerment strategies at both operational and strategic levels.

Abstract In this study, the model in the framework of hierarchical covering location by taking a dynamic approach (the radius of coverage and the amount of demand in different periods dynamically) in the fuzzy provide and studied. Location and allocation model for hospitals and facilities that includs rescue helicopter and ambulance to the demand, to establish and cover of demand that they are in the radius of coverage, provided, and also considered the possibility of being busy facility. The status of Subspecialty hospital, hospital and clinic locate and in different periods of time are fixed. Fuzzy concept has been used to draw closer to reality. Site of the service facilities, including ambulances and helicopters are variable in different periods. Also in models for this movement is considered cost. Services machines and hospitals and clinics have limited capacity. Given the fact that the goal is just to validate the model, numerical data is used. The method of solving this problem is using the numerical example of the GAMs software definitive method, and for larger scales, the ABC algorithm and ICA are used. To validate the proposed model, it compares it with Bashiri et al. Model model. The numerical results show the optimal efficiency of the proposed solution method and the problem model.

Abstract Activity-based costing since it's introduction has attracted so much attention. There are, however, practical problems in implementing this costing system, which, in spite of the computational superiority of activity-based costing compared to traditional costing, organizations and companies are still not interested in using this costing method. In the present study, implementation problems that are practically related to implementation of activity-based costing have been investigated and artificial neural networks have been used to solve the problem of estimating the cost-activity relationship (CER) as well as reducing the costs of doing timing in organizations. The statistical population of the research is all branches of Maskan Bank which has been clustered using CI-DEA Data Envelopment Analysis (CI-DEA) and based on performance similarity in 1395. 450 branches were selected as samples and used to train and test the model of neural networks. The distinctive feature of this pattern is to consider non-linear relationship between cost-activity and other patterns. The proposed architecture of network makes it possible, in addition to the cost-of-activity forecast, to be extrapolated from the model, the amount of cost-driven input (time) used as a cost-sharing actuator to the activity in the conventional executive model. The results of the RMSE and MAE models showed that the proposed model has the capability to estimate the cost-activity relationship.

Abstract Given the intense competition in international markets, small and medium-sized enterprises (SMEs) need strategies and tools to compete in global markets. Business intelligence is considered one of the key tools for improving the performance of these companies. The objective of this study is to examine the impact of business intelligence on the internationalization performance of SMEs and to clarify the mediating role of open innovation, organizational learning, and competitive advantage. This research is applied in nature and employs a descriptive-analytical method. The statistical population consists of all small and medium-sized enterprises (SMEs) active in the chemical industry of Gilan province. A non-random sampling method was used, resulting in a sample of 77 companies. Data were collected using a standardized Likert scale questionnaire and analyzed using SPSS and Smart PLS software. The results of the study indicated that all hypotheses, except for the mediating role of competitive advantage in the relationship between business intelligence and internationalization performance, were confirmed. This study is the first to investigate the impact of business intelligence on internationalization performance with the mediating role of organizational learning, open innovation, and competitive advantage.

Abstract Considering the importance of assessing the maturity level of knowledge management in organizations, this research assesses the maturity level of 300 top Iranian company using APQC model. Survey research is used in this research and data collection tool is questionnaire and t-test, Fridman and correlation test is used to analysis of data. Among 300 companies 99 companies attended in the survey. The Results show that Iranian companies are located on third level of KM maturity. There is no significant relationship between size of organization, industry type, the use of contractors and external consultants and knowledge management maturity. However there is relationship between existing knowledge management unit, experience regarding knowledge management and knowledge management maturity. There is significant relationship between post of senior managers and knowledge management maturity. Culture, technology, process, leadership and assessment, s aspects have better conditions. Key words: Knowledge management, Knowledge management maturity, maturity model, Knowledge management assessment tool

Abstract This research with goal of applying human factor engineering into the dual resource constraint system (DRC) studied and modeled significant human factors in staff scheduling problem. In previous studies more staff scheduling has been conducted based on machine constraints and production planning and less human factors has been considered in scheduling system optimizing. So, in this article human factor engineering was studied in shift scheduling and planning. Human factors which were modeled are learning, forgetting, fatigue and recovery by rest and objective function is minimizing ratio of number of employee on productivity. To study performance of mathematical model and examine effects of human factors on staff scheduling efficiency four scheduling scenarios with different human parameter sets were solved and analyzed. Results indicated that human parameters have impact on performance of dual resource constrained system and choosing good scheduling scenario can increase employees’ efficiency up to 20 percent. Also because of complicated impact of human parameters on system performance, any organization should define shift scheduling based on human parameters for their jobs. Regarding defined parameters in this article, scenario with one rest break time is more efficient compared to scenario with two or more rest break times. Also in average rate of fatigue, scenarios used by production workshops are more efficient compared to the other defined scenarios.