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hassan-nayebi--erfan
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Operations Optimization in Supply Chain Systems using Simulation and Reinforcement Learning
, M.Sc. Thesis Sharif University of Technology ; Hassan Nayebi, Erfan (Supervisor)
Abstract
The inventory costs constitute a significant portion of the supply chain costs. Therefore, choosing an optimal inventory policy for orders is of great importance. The aim of this research is to find the optimal inventory policy for a distribution center in a three-tier supply chain consisting of a manufacturer, a distribution center, and a retailer. This research simulates a supply chain in agent-based framework and optimizes it using reinforcement learning. The optimization KPI in this research is the mean daily cost of the supply chain. Finally, the result obtained from reinforcement learning is compared with the optimized result of AnyLogic and the mean daily cost in the model optimized...
Customer Journey Analytics using Process Mining Based on the Markov Model
,
M.Sc. Thesis
Sharif University of Technology
;
Hassan Nayebi, Erfan
(Supervisor)
Abstract
The analysis of customer journeys has gained significant attention due to the critical role of customer behavior data in enhancing business decision-making and formulating strategies for customer acquisition and retention. By segmenting customers based on their journey patterns, businesses can offer personalized recommendations, thereby improving customer engagement and loyalty. Additionally, predicting the next steps in a customer’s journey based on historical data allows for timely and appropriate interventions at various touchpoints. By understanding where customers are in their journey, businesses can provide targeted recommendations that increase the likelihood of converting potential...
Discovering and Improving the Processes of an Iranian Psychiatric Hospital Using Process Mining
, M.Sc. Thesis Sharif University of Technology ; Hassan Nayebi, Erfan (Supervisor)
Abstract
Providing quality hospital services depends on the efficient and correct implementation of processes. Therapeutic care processes are a set of activities that are carried out with the aim of diagnosing, treating and preventing any disease in order to improve and promote the patient's health. The purpose of this study is to use process mining techniques to discover and improve healthcare processes. The case study of this research is a psychiatric hospital in Shiraz. The approach implemented in this research consists of three main stages including data pre-processing, model discovery phase, and analysis phase. Three algorithms including Heuristic Miner, Inductive Miner, and ILP Miner were used...
Analysis and Improvement of Agile Software Development Process Using Process Mining and Machine Learning Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hassan Nayebi, Erfan (Supervisor)
Abstract
In the competitive world of software development, IT product development teams face numerous challenges and issues within business processes. In recent years, the combination of process mining and machine learning algorithms to optimize software development processes has gained attention. However, there is still a lack of a comprehensive model for identifying and conducting root-cause analysis of problems and providing solutions based on them in agile software development systems. The aim of this research is to find a solution for identifying and addressing issues in the software development process by modifying the change request workflow and examining various aspects of incidents. To...
Improve Performance of Process Mining Algorithms in Low-Level Event Log with Machine Learning Methods
, M.Sc. Thesis Sharif University of Technology ; Hassan Nayebi, Erfan (Supervisor)
Abstract
This thesis abstract addresses the use of process mining techniques when event data is stored at varying levels of granularity. While most techniques assume that events have the same level of granularity, real data is often stored differently. Pre-processing techniques allow for appropriate summarization of the data, which simplifies the output while retaining important process details. The goal is to ensure an interpretable output for stakeholders and different business teams without losing critical process points. However, adding new data as a feature to the dataset can be expensive, and at times, infeasible. Therefore, existing data is the only solution. To overcome this challenge, this...
Simulation-Based Optimization for IoT-Enabled Epidemic Patients Care Systems
,
M.Sc. Thesis
Sharif University of Technology
;
Hassan Nayebi, Erfan
(Supervisor)
Abstract
This research focuses on examining and improving healthcare systems, particularly during crises and pandemics. Following disasters such as natural calamities and pandemics like COVID-19, healthcare systems face significant challenges due to the increased demand for medical services, creating a substantial threat to the population in the affected regions. This study emphasizes the importance of utilizing modern technologies such as the Internet of Things (IoT) and telemedicine systems in alleviating the pressure on healthcare systems. A combined approach of prediction and multi-objective optimization based on simulation is proposed in this study to improve resource allocation and demand...
A Process Mining Approach to Analyze Customer Journeys to Improve Customer Experience
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
With the growth of the number of online service providers and the need to innovate in these services, in this study, the processes and the journeys taken by visitors of a website that provides employment services and employment insurance has been analyzed. In this research, process mining techniques and predictive process monitoring were implemented. With the use of a supervised and unsupervised learning algorithm, it attempted to identify the customer journeys' output and the existing patterns that lead to the complaint. In the first step, the website event log is extracted. Afterward, by using frequency-based encoding methods, the journeys traveled by users were clustered based on the...
Predictive Business Process Monitoring Using Machine Learning Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
In order to survive in today's business world, which is changing at a very fast pace, organizations can detect deviations even before they occur, quickly and with a high percentage of confidence, by analyzing their processes, in order to prevent disruptions in the processes. by monitoring the information systems that automatically execute business processes, it is possible to ensure the correct implementation of the existing processes. For this purpose, various techniques for monitoring business processes have been presented so that managers have a comprehensive and real view of how implement processes and be able to identify possible deviations in the future and try to fix them because the...
Data-Driven Prediction for Monitoring Business Process Pperformances Based on Classification Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
In recent years, several studies have been conducted in the field of data mining techniques in the field of process mining with the aim of improving the performance of organizations. Predictive process monitoring is a data-driven approach that helps business managers to improve the status and conditions of their organization. In this approach, the event log, which includes a set of completed examples of a process, is received as input, and machine learning methods are used to predict the outcome and results of the organization's processes before the process is completed. This prediction can include the prediction of the final result, the next event, the time remaining until the completion of...
Predictive Process Monitoring Based on Optimized Deep Learning Methods
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
Business processes are an essential part of every business as they provide insights on how to optimize and make them more efficient. Predictive Business Process Monitoring has garnered significant attention in recent years due to its capability to forecast process outcomes and predict the next activity within an ongoing process. In the last few years, there have been works that focused on deep learning and its applications in predicting the next activity. Some research used Long Term Short Memory, while others used Convolutional Neural Networks. However, long term short term memory models have the constraint of relatively slow training, while Convolutional Neural Networks are fast but may...
A System Dynamic Simulation Approach to Investigate Economic And Environment Factors Based on VUCA framework: A Case Study in Petrochemical Industry
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
To maintain adaptability, businesses should anticipate changes in their environment. The commonly employed forecasting method within corporate circles is the bottom-up approach, which relies on historical data for projecting future trends. However, research suggests that this approach often falls short of accurately reflecting real-world events. This has led to the adoption of dynamic systems modeling, a technique grounded in the assumption of stable conditions. This method effectively replicates the system's current state, thereby assisting in predicting future behaviors over a longer timeframe. The dynamic systems modeling approach was employed in this study, underscoring the imperative...
Investigation on Phased Arrays with Irregular Overlapped Subarrays
,
M.Sc. Thesis
Sharif University of Technology
;
Nayebi
(Supervisor)
Abstract
In this thesis, we would consentraite in the topic of irregular overlapped subarrays. In general, it would be a combination of topics overlapped and irregual arrays. In phased array topics, one of the important issues is cost. Main cost of array is consist of its phase shifter cost at all. Duo to the cost of phase shofters, overlapped subarray structure might be used. Philosophy of using overlapped subarrays returned to similar phase required for elements adjust together. But this emphasis is true just in limited field of view fields. But grating lobes could be seen even without beam steering. For solving grating lobes problem, overlapped structured has been presented. In overlapped...
The investigation of natural super-cavitation flow behind three-dimensional cavitators: Full cavitation model
, Article Applied Mathematical Modelling ; Volume 45 , 2016 , Pages 165-178 ; 0307904X (ISSN) ; Kadivar, Erfan ; Javadi, K ; Javadpour, S. M ; Sharif University of Technology
Elsevier Inc
2016
Abstract
In this study, natural super-cavitating flow around three different conical cavitators with wedge angles of 30°, 45° and 60° is investigated. We apply the k−ϵ turbulence model and the volume of fluid (VOF) technique to numerically study the three-dimensional cavitating flow around the cavitators. The turbulence approach is coupled with a mass transfer model which is implemented into the finite-volume package. Simulations are performed for different cavitation numbers. Finally, the effects of some important parameters such as the cavitation index, inlet velocity, Froude number and wedge angle of cavitators on the geometrical characteristics of the super-cavities are discussed. Our numerical...
Robust Markov Decision Processes and Applications in Mathematical Finance
, M.Sc. Thesis Sharif University of Technology ; Fotouhi Firouzabadi, Morteza (Supervisor) ; Salavati, Erfan (Supervisor)
Abstract
Dynamic portfolio optimization is one of the prominent problems in financial mathematics, for which numerous theories have been proposed to solve it. One of the solutions to this problem is the use of reinforcement learning. The main challenge with this method is that most reinforcement learning algorithms require a large amount of data, and therefore, the necessary data is often obtained not from the real world, but through simulations by estimating the parameters of a model. However, the approximation error of the parameters can propagate through the final solution, leading to inaccurate results. One approach to addressing this issue is the use of robust Markov processes and robust...
Structural Design of a Launch Vehicle with Reusable First Stage
, M.Sc. Thesis Sharif University of Technology ; Haddadpour, Hassan (Supervisor)
Abstract
Aerodynamics loads and aeroheating during reentry, are one of the challenging issues in designing One of the main challenges in the design and application of reusable spacecrafts is the aerodynamic and thermal loads acting on their structures during reentry into the atmosphere. For this reason, all the concepts/prototypes presented in the 20th century were either not fully reusable, or were partially canceled, or did not have economic justification. In this report, the aerodynamic loads on the first stage of a launch vehicle during the launch and return phases have been investigated. Due to the fact that the main part of the first stage of the vehicle includes fuel and oxidizer tanks, the...
Business Processes Deviation Analysis Using Process Mining Algorithms
,
M.Sc. Thesis
Sharif University of Technology
;
Akbari Jokar, Mohammad Reza
(Supervisor)
;
Hassannayebi, Erfan
(Co-Supervisor)
Abstract
Deviations in business processes consistently impose significant financial and temporal costs on business owners and can lead to decreased customer satisfaction with organizations. Therefore, timely identification of deviations is a crucial and significant issue for business process managers. While extensive research has been conducted on the detection of antecedent deviations, predicting deviations before they occur—which could facilitate preemptive actions to prevent these deviations—has received less attention. In this context, the aim of this study is to predict two types of process deviations—temporal deviations and Rework deviations—using machine learning and deep learning algorithms,...
Optimum hello interval for a connected homogeneous topology in mobile wireless sensor networks
, Article Telecommunication Systems ; Vol. 52, issue. 4 , April , 2013 , pp. 2475-2488 ; Online ISSN: 1572-9451 ; Sarbazi-Azad, H ; Sharif University of Technology
2013
Abstract
Topology of a mobile wireless sensor/ad hoc network is prone to link breakages due to node mobility which compels the periodic re-construction of the topology in so called ""hello"" intervals. The problem addressed in this paper is determining the maximum ""hello"" interval preserving the connectivity of a homogeneous topology with high probability. In order to study the optimum ""hello"" interval, statistical topology lifetime (STL) is formally defined as a value of ""hello"" interval that ensures statistical connectivity with a given threshold. Temporal properties of the topology are studied based on two phenomena; one concerning a connectivity phase transition with time and the other one...
Performance modeling of the LEACH protocol for mobile wireless sensor networks
, Article Journal of Parallel and Distributed Computing ; Vol. 71, issue. 6 , 2011 , p. 812-821 ; ISSN: 07437315 ; Sarbazi-Azad, H ; Sharif University of Technology
2011
Abstract
In many wireless sensor network applications, nodes are mobile, while many protocols proposed for these networks assume a static network. Thus, it is important to evaluate if a traditional protocol designed for a static network can tolerate different levels of mobility. This paper provides an analytic model to investigate the effect of mobility on a well-known cluster-based protocol, LEACH. The model evaluates data loss after construction of the clusters due to node mobility, which can be used to estimate a proper update interval to balance the energy and data loss ratio. Thus, the results can help the network designer to adjust the topology update interval given a value of acceptable data...
Analysis of link lifetime in wireless mobile networks
, Article Ad Hoc Networks ; Volume 10, Issue 7 , September , 2012 , Pages 1221-1237 ; 15708705 (ISSN) ; Sarbazi Azad, H ; Sharif University of Technology
2012
Abstract
Link lifetime (LL) analysis is crucially important in designing and evaluating wireless mobile network protocols. Although a considerable amount of research has been conducted on this issue, the current paper studies several aspects of link lifetime for the first time, e.g. a closed form expression for the probability distribution of the residual link lifetime (RLL) for the constant velocity mobility model is obtained, which facilitates marginal studies on the behavior of link lifetime. As a case, using a tail analysis of LL and RLL distributions, we analytically prove that they are heavy-tailed. Using this outcome, a self-similar behavior in the traffic pattern of a sample delay tolerant...
Optimum hello interval for a connected homogeneous topology in mobile wireless sensor networks
, Article Telecommunication Systems ; Volume 52, Issue 4 , 2013 , Pages 2475-2488 ; 10184864 (ISSN) ; Sarbazi Azad, H ; Sharif University of Technology
2013
Abstract
Topology of a mobile wireless sensor/ad hoc network is prone to link breakages due to node mobility which compels the periodic re-construction of the topology in so called "hello" intervals. The problem addressed in this paper is determining the maximum "hello" interval preserving the connectivity of a homogeneous topology with high probability. In order to study the optimum "hello" interval, statistical topology lifetime (STL) is formally defined as a value of "hello" interval that ensures statistical connectivity with a given threshold. Temporal properties of the topology are studied based on two phenomena; one concerning a connectivity phase transition with time and the other one...