This project explores the impact of delivery systems on the effectiveness of lockdown measures during the COVID-19 pandemic. By simulating a one-to-one delivery system, we investigate how delivery drivers might contribute to the community transmission of the virus. The model features three types of agents—Restaurants, Delivery Drivers, and Houses—each with five possible health states: Clean, Infected (non-transmissive), Infected (transmissive), Quarantined, and Recovered. Starting with one initially infected delivery driver, the simulation tracks the spread of infection as deliveries are made. Key observations include the potential for widespread infection among delivery drivers and limited household infections, highlighting the importance of quarantine and recovery in maintaining a stable delivery system. This study provides insights into the risks associated with delivery services during lockdowns and aids in developing strategies to mitigate transmission.

This model simulates on-demand food delivery in the west zone of Dubai, encompassing 172 restaurants and 117 residential buildings with randomly generated orders every two hours. A fleet of couriers stationed in a parking area is dispatched based on four delivery queueing methods: single restaurant to single customer (One-to-One), multiple restaurants to multiple customers using either a first-in, first-out sequence (Many-to-Many FIFO), a nearest pickup/delivery node approach (Many-to-Many Nearest), or a heuristic simulated annealing routing sequence (Many-to-Many SA). The model operates as a multi-method simulation where couriers, customers, and restaurants act as agents with custom behaviors within a GIS space, using real road networks for movement. Initial locations are determined via a built-in database, and a GIS search engine places agents accurately on the map. The system features a dynamic diagram of delivery methods and a flowchart to simulate order processing.

This project involves developing a road traffic simulation model to optimize vehicle flow and minimize time spent in congestion. Using AnyLogic simulation software, the model calculates vehicle speeds with a custom function considering maximum random speeds and distances to other vehicles. It features dynamic elements such as moving cameras and traffic lights to enhance visualization and presentability. The study aims to improve traffic management by analyzing the impact of varying vehicle volumes on overall traffic efficiency, providing insights for better road design and event organization in transportation and logistics networks.

This project demonstrates a SEIR-GOH (Susceptible, Exposed, Infectious, Recovered) simulation for modeling virus propagation within a population using system dynamics and flow-based models. The simulation incorporates various key parameters, including: Fatalities, Hospitalized cases, Recovered individuals, Infectious individuals and Exposed population. The model leverages the SEIR framework, which accounts for the transitions between different states (Susceptible, Exposed, Infectious, and Recovered), adding further granularity through the GOH (Generalized Outbreak Handling) approach to track hospitalizations and fatalities. 

This project is a pedestrian safety simulation developed using AnyLogic, focusing on a real-world scenario where pedestrians crossing roads trigger a smart traffic control system. The simulation integrates pedestrian detection technology, such as cameras or sensors, that activate red lights for cars when pedestrians are detected, ensuring safe crossings. The model incorporates GIS-based location data to accurately simulate road layouts and traffic patterns, making it geographically relevant. The simulation manages multiple crossings simultaneously, with cars stopping based on their distance from the crossing, preventing accidents. It dynamically adjusts traffic lights depending on pedestrian and car flow, with customizable settings for the number of crossings and timing. This project offers a clear visualization of pedestrian-vehicle interactions and is designed to prevent collisions while ensuring smooth traffic management.

This project simulates the production of mechanical press components within a Product-Service System (PSS) framework. The model evaluates the economic and environmental sustainability of the system through the total cost of ownership (TCO) analysis and CO2 emission estimates. Key elements include product life cycle phases, maintenance strategies, and resource optimization. Using AnyLogic and MATLAB, the simulation integrates real-world data for production efficiency, resource consumption, and maintenance schedules. The outcome offers insights into improving service delivery, reducing environmental impact, and enhancing overall system performance in a sustainable industrial setup.

This simulation project models the dynamics of forest fire spread and control using AnyLogic, with a focus on understanding how wildfires propagate and optimizing firefighting strategies. The simulation evaluates factors such as fire intensity, wind direction, and firefighting resources to predict fire behavior and assess the effectiveness of intervention methods. By simulating different firefighting strategies, such as deploying aerial or ground resources at varying response times, the model provides insights into minimizing the spread of the fire and reducing overall damage. The goal is to enhance decision-making in forest fire management by optimizing resource allocation and response times for more effective control of wildfires.

This simulation models a production line focused on the cutting and drying processes of a manufacturing setup. The production flow starts with cutting machines, where products are separated into different categories based on cleanliness, and then transferred to trolleys and dryers. The simulation aims to optimize the accumulation of materials at different stages of production, starting from cutting to drying and then assessing quantities in the cooling area. Initial results focus on simple line flow analysis without complex graphics. Future iterations will include more details and calculations regarding the required silo sizes and conveyor belt speeds for an enhanced, optimized production line.

This project involves simulating the implementation of CRM software for a customs agency to enhance their sales process by comparing online sales orders to the traditional physical store. The CRM aims to reduce delays by automating steps such as customer login, quote requests, order tracking, and payments, drastically cutting response times from the current 5-7 days to same-day responses for online orders. The simulation will analyze key metrics like number of customer prospects, quote approval rates, and overall efficiency, showcasing the speed and effectiveness of the CRM system in contrast to the physical store’s manual processes. This simulation serves as a proof of concept, illustrating the significant time savings and increased customer satisfaction achieved through the adoption of the web application.

his project aims to simulate a Vehicle-to-Vehicle (V2V) communication system involving three vehicles (a bus and two cars) traveling along a predefined route. The simulation mirrors a real-time scenario where the bus communicates with the two cars when it arrives at the station, and the yellow car sends a message to the blue car when it enters a parking area. The objective is to establish message exchanges between vehicles, mimicking a real-world V2V communication setup, and extract the structure of the data frames being exchanged during the communication. The simulation will illustrate the process of message transmission, and help analyze how vehicles interact when entering stations or parking lots.