Modern Research in Decision Making

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 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.

Abstract In the past few decades, due to negative environmental and social impacts of companies, there has been more pressure from the government and non-governmental organizations on companies to observe these issues in their operations, including their supply chain operations. To achieve an environment-friendly supply chain, the role of suppliers as the initial point of the supply chain is very crucial. So in addition to traditional criteria for evaluating and selecting suppliers such as price, delivery, and quality, it is essential to use new criteria, that while encompassing environmental issues, are fit to the characteristics of the industry under study. In this study, taking into account a set of economic and environmental criteria, a multi-objective integer mathematical programming model is proposed for solving the green supplier selection and order allocation problem under the multi-product, multi-sourcing, and single period assumptions. The case of this study is part of the IRAN KHODRO supply chain and focuses on the supplier selection and order allocation processes for 31 components of four types of the final products provided by 45 suppliers. The proposed mathematical model for the supply chain under study has 507 constraints and 449 decision variables. Then proposed model is solved via the ε–constraint method and Total Value of Green Purchasing (TVGP) is calculated. Results of the solution and sensitive analysis of the proposed model demonstrate that with increasing the importance of environmental issues in the supply chain, TVGP will increases.

Abstract One of the most critical issues for setting up a ‎‎‎DEA model is identification of the technological returns to scale‎. We refer to it as the technological returns to scale (TRTS) to completely separate the technology’s RTS from the DMU’s RTS. The only existing objective approaches for the TRTS identification are statistical based approach. While they are supported by strong theories, they might be problematic in practice. In this paper, we introduce a novel and objective non-statistical method for the identification of the data’s TRTS. Our proposed approach is called the Angles method since it utilizes the angles between the hyperplanes to calculate the gap between the constant and variable TRTS assumptions. The gap is calculated for both the increasing and the decreasing sections of the frontier. The larger the gap in the increasing and/or the decreasing sections of the frontier, the more the TRTS approaches the increasing and/or the decreasing assumptions. The novelty of the Angles method is that it determines the TRTS by utilizing only the dataset without any statistical assumptions. Besides, unlike the existing statistical tests that merely accept or reject some hypothesis, the introduced gap represents the rate of increase or decrease of the TRTS. ‎To validate the proposed method‎, ‎we consider six sample with one input/one output and a two inputs/one output sample‎. Moreover, the Angles method is applied on a real world data set of province gas companies.

Abstract Due to the causal nature of maritime transport sector and interaction of variables in this section as well as the complexities, Systemd ynamics (SD) approach is used to measure and forecast port performance. This paper concerns the performance in terms of the number of container loading and unloading of container terminal, ship turnaround time in port and berth occupied percentage. Historical data of Shahid Rajae port from 1384 to 1394 is used to valid proposed model. The results of simulation show that by increasing one more gantry crane, the container throughput increase 12/5% and the average service time decrease about 30%. By investing and developing one berth more, the throughput increase 4% and average service time decrease about 37%. Whereas by increasing one more gantry crane and berth, the throughput increase 37% and average service time decrease about 45%. The proposed model can help port managers to see the effects of their decisions on future port performance and design policies that lead to desired consequences.

Abstract  One of investments to increase the wealth of investors is investment in the stock exchange. The purpose of this paper is ranking of cement and plaster, petroleum products (production of petrochemicals refined products), tile and ceramic, automotive (manufacture of motor vehicles) industries of stock exchange to help the investors for investment decisions. This research aims to develop a methodology for solving MADM problems with both ratings of alternatives on attributes and weights being expressed with interval-valued intuitionistic fuzzy sets. In this methodology, a weighted absolute distance between intuitionistic fuzzy sets is defined using weights of intuitionistic fuzzy sets, two simpler auxiliary linear programming models being used to calculate the relative closeness coefficient of ideal solution, based on the concept of likelihood of interval numbers, we rank alternatives. The required data is extracted during 1393 to 1394 mainly through audited financial statements data from rahavard novin software. the final results of this study indicate that petroleum products, cement and plaster, tile and ceramic, automotive industries respectively, have the highest rank.

Abstract A growing business environment with growing uncertainty, unexpected dangers and quick unavoidable changes increases the probability of intense disturbance in corporations' supply chain. This trend that accompanies by natural disasters e.g. flood, tsunami, earthquake and etc. increases the necessity of resiliency and development of supply chains specially in pharmaceutic supply chains which are more sensitive. Managers require tools monitoring their supply chain resiliency against disturbance. The main purpose of this research is measurement and assessment of supply chain resiliency in pharmaceutical industry. In this study; considering complex adaptive systems (CASs) approach under the title of theory lens; the supply chain of two Iranian pharmaceutical corporations (Iran Daroo and Ghazi pharmaceutical Company) were chosen to be examined. In the following, the SCR dimensions and factors in the CAS framework were identified by systematic literature review. Afterward, this research proposes the integrated and systematic method by combination of Interpretive Structural Modeling (ISM), DEMATEL, graph theory and matrix approach (GTMA) and importance-performance analysis (IPA) to measure and assess the level of resiliency of both supply chains. Finally, conclusions from this research can support the manager’s analysis of resiliency and selection of effectiveness risk mitigation strategies in their supply chain and simplifies decision-making. This novel approach causes a competitive advantage to achieve market share even during a disruption.

Abstract Data envelopment analysis (DEA) is an approach for measuring the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and multiple outputs using mathematical programming. In conventional DEA models, a performance measure whether as an input or output usually has to be known. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Such variables are called flexible measures. This paper introduces a new “double-frontier DEA” approach for classification of flexible measures. In the proposed approach, each flexible measure is classified as either input or output, so that the efficiency of the DMU under evaluation is maximized. Therefore, classification of flexible measures using the proposed DEA approach is simpler and more logical. An example in UK higher education institution shows applicability of the proposed approach.

Abstract Successful entry and competition in the market is accompanied with great sense of unreliability and it contains several limitations. Hence, a great number of firms, especially new firms, will leave the market since after entering. So, only new minority will be survived in some industries or regions. The success and failure of newcomer firms will remain ambiguous, so our knowledge should be improved about growth process of new firms. The aim of present research is to study influences of macroeconomic variables on new firm’s survival time in industries of Mazandaran province during 1364- 1394. Data and information have been collected from mining industries office of Mazandaran province and Central Bank of the Islamic Republic of Iran and for survival analysis Event-History data analysis was used. For data analysis product limit estimator model (Caplan-Maier) and life table approach was used and for hypothesizes test, the Cox regression semi parametric model has been used. Result based on the, a meaningful direct relation between inflation rate and survival time has been confirmed and we find a meaningful and reverse relation between interest rate to survive. Also our finding shows that there is a meaningful and direct relation between inflation and survival. Finally a comparison between companies survival function based on unemployment rate has been carried out.

Abstract Today, more than ever, people spend much of their personal and social life in virtual space. A website is a channel of communication with its stakeholders and audiences. In the marketing literature, there is a great emphasis on the first confrontation with the customers, hence designing a website is known as a significant competitive factor. Neuro marketing Theory that is based on the brain’s function in the field of marketing has been developed in website designing field and has created Neuro Web Design Theory. During literature review analyses the authors discuss the requirement of using this Theory in websites design, especially in shopping websites. The research was based on 60 case studies of shopping websites (collected from Alexa website) in the world and applying correlation test between websites rank and their compliance with Neuro Web Design requirements. The results proved the research hypothesis that there is a significant correlation between Neuro Web Design requirements and websites rank. But results rejected the Hypothesis that there is a significant correlation between Neuro Web Design requirements and the time Spent on the website. Hypotheses of the existence of a significant difference between websites with different Business Models were confirmed.