majid sheikhmohammady

In today's challenging world, organizational growth is not possible without the efficient use of data. Process mining uses machine learning methods and business process management concepts to extract hidden knowledge about business processes from data stored in information systems. Process Discovery is the first step in process mining. The main goal of process discovery is to transform the event log into a process model. However, using process discovery methods will not be possible without appropriate data because any analysis based on low-quality data will lead to poor insights and bad decisions that will negatively affect the performance of the organization or business. This paper aims to provide a new conceptual framework for preprocessing data input into process discovery methods to improve the quality of the extracted model. The proposed conceptual framework has been developed using a qualitative research process based on grounded theory. For this purpose, 102 articles related to the domain of data quality in process mining were reviewed, and the most critical challenges of data quality in this field have been identified after filtering and integrating them from the literature, including: Are "noisy/infrequent events," "outlier events," "anomalous events," "missing values," "incorrect time format," "ambiguous timestamps," "synonymous activities" and "size and complexity." Then, the basic steps for data preprocessing and cleaning tasks are defined, which include the activities of "repair," "anomaly detection," "filtering," and "dimensional reduction." The final preprocessing framework then builds on data quality issues and identified activities. Four standardized datasets derived from real-world processes were used to assess the proposed framework's performance. Firstly, these data are raw, and secondly, four standard process discovery algorithms are applied after preprocessing by the introduced framework. The results showed that the preprocessing of the input data leads to the improvement of the model quality criteria extracted from the process discovery algorithms. Furthermore, to evaluate the validity of the proposed framework, its performance was compared with three preprocessing methods"sampling," "statistical preprocessing," and "prototype selection," which the results indicate better efficiency of the proposed approach. The results of this study can be used as guidelines by data and business analysts to identify and resolve data quality problems in process mining projects.

Conflict among stakeholders may be a barrier to the progress of projects and a major cause for significant waste in capital. This research explores modeling and analysis of a conflict among the stakeholders of a high-technology corporation. The interaction among the stakeholders is modeled as a game. In this research, we employ a system’s engineering approach for modeling and analysis of this intra-organizational conflict, using Graph Model for Conflict Resolution (GMCR+). It is an advanced methodology driven from conflict analysis, which has routes in non-cooperative game theory with a non-quantitative approach. This article focuses on a conflict among three parties. The socially optimal solution happens when one player, who has the technical knowledge, finds another investor. Using sensitivity analysis, we find an alternative, more favorable solution to reach a new agreement in setting up two plants (instead of seven) in Tehran and Kermanshah, launching the project with the remaining capital. We demonstrate that taking the case to court does not result in a favorable outcome for the players.

The command and control warfare between hostile countries is a common type of battles. This warfare occurs during the real war surely. Its goal is to destruct the focus of the opposite sidechr(chr(chr('39')39chr('39'))39chr(chr('39')39chr('39')))s commander. This issue possesses a high importance at the beginning of the war as it affects the analysis of the opposite side whether to start a war, or avoid it. One of the main questions of research is whether you can predict these feedbacks and decisions. The main goal of the article is to create a set of possible suggestions based on mutual decisions. It is obviously important to predict the behavior and to estimate the choices of the rival. In this article, we study modeling and analysis of the scenarios for hostile countries (blue and red) in the Command and control war, by non-cooperative game theory method –a very useful tool for qualitative and quantitative analysis of the partnerchr(chr(chr('39')39chr('39'))39chr(chr('39')39chr('39')))s decisions- to predict the most likely outcomes. Recognizing the possible actions of the countries and their preferences over the feasible states make the results and equilibrium more accurate. The results of this study show that low cost actions, such as jamming the network of special link of command and control and disorder in the systems of navigation can reduce power of the competitorchr(chr(chr('39')39chr('39'))39chr(chr('39')39chr('39')))s combat, and pave the way for the next tough actions

With progressing technologies green products are in competition with non-green products. In real world, there exist many firms that produce green and non-green products. In this study, a green supply chain(GSC) with a supplier and a manufacturer is considered. The supply chain(SC) produces green and non-green products. Market demands for both non-green and green products are available. These products might be replaced with each other. If the demand isn't satisfied by green products, hybrid production mode involving green and non-green products is chosen by the manufacturer. The government is considered as the leader of the game and fixes special tariffs(tax and subsid) for all products to control the market demand. A game theoretical model is formulated in four scenarios by considering collaboration of members in the SC. The ideal prices of raw materials, selling prices, and demand for green and non-green products are calculated. A numerical example that consists of sensitivity analysis of some main parameters is presented to compare the outcomes of various scenarios. The results indicate that collaboration between supplier and manufacturer has significant impact on a profit of GSC. Besides, Various consumer’s priorities are covered in hybrid production mode. The results show that by choosing hybrid production mode, the profit of SC decreases and hybrid production mode hasn't positive role on profit of SC and membership. Sensitive analysis shows that increasing tariffs by government causes an increase in summation profit function of GSC and profit function of member of GSC. Moreover, the demand for non-green products increases.

Governments apply economic incentives and penalties to manage environmental effects of enterprises. Market demands is considered in this research such that non-green and green products can be substituted with each other. We study the competition between two supply chains (SCs) in the market which are called regular and closed-loop SCs. We then formulate a game theoretical model in two scenarios based on collaboration in closed-loop SC and the selling prices, prices of raw materials, and profit of SCs in each scenarios. Numerical examples are presented to make  results of the models more tangible. The effects of main parameters of the models are also evaluated through a sensitivity analysis. The results indicate that collaboration between recycler and retailer in the closed-loop SC has significant impact on the profits of the SC’s members. Moreover, the profits of SCs in collaboration between recycler and retailer is higher than non-collaboration scenario.

This paper is considering the competition between two multi-echelon supply-chains on price and service under balance and imbalance of market power between the chains which are analyzing through Nash and Stackelberg game approach. The problem is categorized as the centralized or decentralized structure of each chain, which means a few different possible scenarios are developing based on the Nash and Stackelberg games. The aim of the paper is to investigate the simultaneous effect of the chains’ structure and market power on the decision variables. As a surprise result, we show that in the Stackelberg game, the chain will not always have the second-mover advantage. Furthermore, the results demonstrate that the leader''s presence in the market may have different impacts on the situation depending on the structure of the chains. Also, when the chains take their decisions sequentially, the service and the price jointly play a strategic role in earning profits.

The hard conflict between the Islamic Republic of Iran (IRAN) and the United States of America (US) has been discussed for decades by bothdomestic and external analysts. In this paper, the conflict between the US and IRAN has been modeled and analyzed, using game theory approach in non-cooperative mode. The analysis of this conflict has been conducted based on the graph model for conflict resolution and using GMCR+ decision support system. This analysis considers two decision makers (DMs) containing the US and IRAN.Sixteenfeasible states have been analyzed among 1024 possible combinationsand accordingly, six states are predicted as the equilibria. Based on different solution concepts for stability definition and the preferences vector of DMs on different scenarios, the model’sequilibrium is determined.In other words, the most likely outcome of the conflict is predicted.The equilibrium resulted from the analysis of the model,expresses that, the possibilityof a severe conflict between the US and IRAN is very unlikely.The other equilibria can determine the behavior of each of the battle players depending on the different circumstances of the conflict. The analyses results show that using this method enables us to analyze the behavior of the battle players in a rational process of decision making to reach the self-desired state or outcome. Findings of this research might help both players to make the right decision and to adopt an appropriate response in confronting the enemy and to change the behavior and decision of the enemy.

Modeling tools describe the real world complex problems well and might help analysts to explore constructive solutions for resolving the problems. In this paper, competition between malware authors and security analysts is modeled and analyzed, using Graph Model for Conflict Resolution which is a comprehensive methodology in a non-quantitative non-cooperative perspective of game theory. This methodology has two main steps: Modeling and Analysis. After removing infeasible combinations, 15 states which are possible to occur in reality, are studied in the modeling phase. Then, the ordinal preferences of the players over the states are represented. Various solution concepts are employed in this research for stability definition. Stability analysis shows that two states are predicted to be equilibria. The predicted outcomes indicate that malware authors employ environment diagnostics and security analysts use system event monitoring along with global system state anomaly detection. Recent evidences are in conformity with the finding of this research.

Supply chain coordination aims at improving supply chain performance by aligning the decisions and the objectives of individual firms. Supply chain participants can coordinate on different decisions such as pricing, inventory management, and marketing decisions to gain more profit. The current research considers coordination of these decisions in a multi-product multi-echelon supply chain composed of multiple suppliers, single manufacturer, and multiple retailers. It is assumed that the demand of each product is non-linearly influenced by retailing price and marketing expenditure. Since all the supply chain members possess equal power in the market and make their decisions simultaneously, the three echelon supply chain problem is a non-cooperative Nash game. In order to find the Nash equilibrium, we formulate a Non-linear Complementarity Problem (NCP) based on the optimality conditions, and also an iterative algorithm is proposed to solve large size instances. Finally, a numerical example is presented, and a comprehensive sensitivity analysis is conducted to discuss important managerial insights.