Sharif Digital Repository

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... 

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... 

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... 

The foreign exchange market, commonly known as Forex, is one of the largest and most significant financial markets in the world, attracting the attention of numerous investors on a daily basis. One of the main challenges faced by traders in this market is the accurate prediction of currency prices. Although Forex market forecasting is highly popular, the inherent complexity of this market continues to make accurate prediction a persistent concern. In recent decades, remarkable advancements have occurred in the field of machine learning, particularly in deep learning. These developments have also influenced the Forex market, resulting in the publication of numerous research articles aimed at... 

Forecasting demand for fast-moving consumer goods (FMCG) is a fundamental yet challenging issue in retail management due to highly volatile demand, short product life cycles, low profit margins, and limited customer loyalty. Customer purchase behavior reflects their response to a set of concurrent product attributes in the retail environment; price, discounts, product placement, and other stimuli influence the final purchase decision not independently, but in combination and in interaction with related products. Therefore, modeling this behavior realistically requires considering the dynamic interactions among products, and relying solely on univariate time series analysis is insufficient.... 

In the age of information and technology, online stores have significantly expanded as one of the most prominent manifestations of e-commerce. With increasing competition among businesses, leveraging modern data mining techniques to identify potential customers, predict customer churn, and enable more precise targeting in direct marketing has become a necessity. This study integrates data mining methods with marketing concepts to analyze the behavior of online store customers using the RFM model (Recency, Frequency, and Monetary value of purchases) and employs the K-Means clustering algorithm to segment customers. Furthermore, to more accurately predict customer behavior, two modeling... 

Last-mile delivery, as the final stage of the retail supply chain, accounts for more than 50% of logistics costs and, with the growth of e-commerce and home delivery, exerts increasing pressure on urban networks. Forecasts indicate that the demand for last-mile delivery services will grow by approximately 78% by 2030, a trend that will lead to increased freight transport, pollutant emissions, and traffic congestion. The traditional truck-based delivery method, which requires separate visits to each customer, not only exacerbates traffic but also faces the challenge of failed deliveries. Therefore, the development of innovative solutions for last-mile delivery is considered a strategic... 

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... 

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... 

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... 

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... 

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... 

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... 

The supplier selection problem is considered as one of the most strategic and critical issues for any organization. This issue is mainly relevant to traditional and manufacturing businesses. However, if we examine its relationship with the emerging concept of Vendor Acquisition, which has become increasingly significant in modern businesses and startups, the importance of supplier selection becomes even greater. Today, choosing the right supplier or vendor determines the level of success organizations achieve in any new project, and the performance of vendors plays a crucial role in shaping and directing these projects. This study aims to present a comprehensive framework for supplier... 

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... 

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... 

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... 

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... 

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...