Sharif Digital Repository

This study proposes a deep reinforcement learning approach to solve a perishable inventory allocation problem in a two-echelon supply chain. The inventory allocation problem is studied considering the stochastic nature of demand and supply. The examined supply chain includes two retailers and one distribution center (DC) under a vendor-managed inventory (VMI) system. This research aims to minimize the wastages and shortages occurring at the retailer's sites in the examined supply chain. With regard to continuous action space in the considered inventory allocation problem, the Advantage Actor-Critic algorithm is implemented to solve the problem. Numerical experiments are implemented on... 

Employing wheeled mobile robots is growing in industry, transportation, space and defense industry and many other social fields as well. These robots are used to execute distinct forms of operations and tasks such as exploring the surface of the earth and other planets, serving in public places, backing natural disasters and warehousing, and so forth. In some cases, the assigned mission may not be capable of being performed as intended by a single robot. In this case, several robots will work together to execute a particular mission. Several research topics that are under investigation currently include the interacting procedure of robots as a multi-agent system in order to perform the... 

Hybrid Exoskeletons refer to simultaneous use of wearable robots and functional electrical stimulation technology. Hybrid exoskeletons have many advantages compared to the separate application of each of these technologies, such as reducing the robot’s energy consumption and the need for lighter and cheaper actuators for the robot, using humans muscle power, and reducing muscle fatigue. As a result, these robots have recently attracted a lot of interest in rehabilitation applications for patients suffering from mobility impairment.Control in hybrid exoskeletons is more complicated than control in traditional exoskeletons. Because in addition to robot and functional electrical stimulation... 

Nowadays, one of the challenges in social robotics is to navigate the robot in social environments with moving elements such as humans. The purpose of this study is to navigate the Arash 2 social robot in a dynamic environment autonomously without encountering moving obstacles (humans). The Arash 2 robot was first simulated in the Gazebo simulator environment in this research. The simultaneous location and mapping (SLAM) technique was implemented on the robot using a lidar sensor to obtain an environment map. Then, using the deep reinforcement learning approach, the neural network developed in the simulation environment was trained and implemented on the robot in the real environment. The... 

In recent years with the progress of technology in different areas, the production of self-driving cars has been feasible. We can expect that vehicles of transportation networks will consist of both self-driving and regular cars in the future. In this research, a new method will be proposed for urban transportation networks to change the direction of reversible lanes according to the network's state. These reversible lanes are exclusive for self-driving cars. Human drivers are not allowed to enter these reversible lanes, considering the limitations of human ability compared to a computer in analyzing data and making decisions about moving direction. To achieve this goal, reinforcement... 

The aim of this research is to develop and implement a deep reinforcement learning algorithm for portfolio management in the Tehran stock market, which is considered an emerging market with distinct patterns compared to the stock markets of developed countries. In this study, in addition to the market price data extensively used in previous research, we leverage fundamental ratio data extracted from company financial reports, which have received less attention. Furthermore, the research scope is limited to stocks in the mining and metal industries to enable the utilization of specific industry features, such as susceptibility to global prices of a key commodity. The portfolio management... 

Using an efficient method to manage inventory systems is always a challenging issue in supply chain optimization. In supply chains including perishable goods, it is possible to reduce waste and other costs by identifying uncertain demand patterns and managing inventory levels at different stages of the supply chain. Considering the uncertainty and complex conditions of supply chains in the real world, in order to create a suitable model to express these conditions, various uncertain factors must be considered, each of which affects the supply chain inventory level in some way. In this research, a multi-level perishable supply chain model with uncertain demand, lead time and deterioration...