Modern Research in Decision Making

Abstract A project is a temporary effort to achieve a predetermined goal. Construction projects are generally considered as one of the foundations of economic development in the world and therefore millions of dollars are invested in them annually. Successful development projects lead to resource conservation and economic growth. To achieve the success of the project, recognizing the effective variables and the relationships between them is the most important principle. The purpose of this research is to map the success of the project using the approach of analysis and development of strategic options. Research data were obtained through interviews and meetings with experts, professors and experts, and maps were drawn and analyzed using Decision Explorer software. Employer history, consultant history, contractor expertise, coordination in employer departments and dependence of consultant payment on project progress were identified as the main and important sources in achieving project success, focusing on which strengthens the main variables in the plan and in the stage Next leads to the success of the project.

Abstract One of the ways to increase resilience in any country and subsequently in any industry is to increase capabilities in the field of technology. Acquiring strategic technologies from countries with technology has always been a challenge. The main goal of the current research is to provide a model of local resilience with the approach of endogenous acquisition of technology in one of the Iran's strategic industries. The research method is mixed. The first stage has been done with a qualitative strategy using the phenomenological method. At this stage, interviews were conducted with 20 experts in this field and after analyzing the data, effective dimensions and components were extracted with the help of ATLAS.ti software. In the second stage, in order to evaluate the extracted dimensions and criteria, using the quantitative method of structural equation analysis and with the help of smart PLS software and asking the opinions of 76 experts in this industry, the extracted criteria were confirmed by factor analysis. Finally, the dimensions of leading technology governance, dynamic organization, reliable management, organizational social capital, knowledge-based mediating institutions and local product commercialization are 6 dimensions of the model, which are identified from 28 sub-categories, 2 sub-categories are removed in the quantitative evaluation stage and finally the model obtained was extracted along with 26 confirmed components. The results of this research can help the managers and policy makers of this field to provide a suitable platform for making decisions for the endogenous development of technology in the conditions of sanctions.

Abstract Supply chains are among the most prominent infrastructures of the world’s complex economy. In global supply chains, parts and raw materials are received at warehouses and production lines from suppliers scattered throughout the world to be used for manufacturing products and delivered into sales channels. Optimal control of inventory and operations throughout the supply network is an intricate problem that needs the development of proportional methods. This study aims at providing a method for simultaneous decisions about determining the type and parameters of the inventory control policy, and suppliers. A simulation-based optimization framework is selected for solving this problem. First, a mathematical programming model is developed. Next, a discrete-event simulation model is developed, verified, and embedded into the mathematical model. Finally, the hybrid model is solved using Golden Eagle Optimizer. Results reveal that the continuous review policy is optimal for all of the parts. In addition, all of the parts procurement orders should be submitted to only two of the five suppliers.

Abstract The aggregate production planning has been constantly among the inseparable elements of the production. Regarding the complexity of production conditions, this planning plays a critical role in the success of large manufacturing companies. Demand forecasting is among the effective factors that lead to cost reduction in aggregate production planning. Since the forecast does not exactly match reality, it is necessary to minimize the prediction error as much as possible. A wrong forecast of demand leads to a production stock-out or backlog. On the other hand, the correct forecasting in the production planning leads to risk reduction and performance improvement in the company's business. Due to the fluctuating and nonlinear trend of demand and the variables affecting it in the different periods, the linear models have low efficiency in achieving asymmetric changes. Therefore, in this study, for the first time, the Markov switching model is applied to predict demand in the problem of aggregate production planning. In this regard, after demand predicting, the decision variables and total costs are estimated, and the results are compared with the actual total costs. The obtained results showed that the Markov switching model has better performance compared to the autoregressive moving average (ARMA) and vector autoregressive (VAR) models based on the cumulative absolute forecast error (CAFE) criteria and the aggregate production planning costs.

Abstract Providing financial resources is necessary for the survival of any business. In supply chain networks, bank loans and commercial credits play a crucial role in financing. Supply chain networks are always affected by financial disturbances under uncertainty condition, therefore, the design of supply chain networks considering financial flows leads to the improvement of working capital. In this research, the supply chain network is designed and developed considering commercial credibility. Considering commercial credit at all levels in a three-level supply chain network including suppliers, factories and distribution centers can be stated as the main contribution of this study. In addition, considering the timing for the repayment of commercial credits by the factories and distribution centers in uncertainty conditions is another challenge of the present research. Due to the uncertainty of demand, supply chain planning should be done in such a way that the necessary financial resources for the production operations are incorporated. In this regard, the demand is considered under scenario-based uncertainty in the proposed model in which the maximization of the net present value as well as the demand estimate are the main objectives. The CPLEX solver was used for solving the model in small-sized instances and the Bee Colony and Wale multi-objective metaheuristic algorithms were used for solving the large-sized problems. The results show how commercial credit affects physical flow. Also, the Wale metaheuristic algorithm has a better performance than the other algorithm.

Abstract In recent years, with the development of machine learning and big data technology, user data has become an important element in the production processes of companies. By applying data mining approaches to customer data, organizations understand customers' behavioral patterns, their needs, and the hidden relationships in the data, and based on these patterns, they can better use their resources to meet customer needs. Clustering is one of the data mining techniques used to group customers according to their different characteristics. The main goal of this research is to cluster customers based on LRFMSP indicators and finally classify them and predict their buying behavior using decision tree (DTC), multilayer perceptron (MLP) and support vector machine (SVM) classification techniques. The study was conducted on 387,496 transactions from customers of a retail store in Western Europe between February 2018 and February 2019. Each transaction attributed to a customer is part of an individual's behavior that is modeled on a set of transactions to shape the customer's purchasing behavior. Performing K-means++ clustering and determining the optimal K led to identifying three clusters for customers. Also, testing and checking the classifiers showed that the MLP classifier with one hidden layer and six neurons in this layer would be the most accurate and the DTC classifier is the fastest among the classifiers reviewed. Examining the behavior of cluster customers showed that customers can be divided into three categories: loyal, potential, and lost.