The project produces model focusing on optimizing the placement of electric vehicle (EV) charging stations using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to address the growing demand for EV infrastructure. The current model is an enhancement of last year model aiming to simultaneously minimize total costs (including development, installation, operational, queuing, grid power loss, and travel costs) and maximize the popularity index of selected charging sites, representing user preferences. The model&nbsp;incorporates multiple constraints, such as site capacity, demand satisfaction, and maximum allowable distance between demand points and charging stations, to ensure feasible and efficient solutions.The optimization process involves the creation of a multi-objective model that generates a diverse Pareto front of solutions, allowing decision-makers to evaluate trade-offs between cost-effectiveness and user accessibility. The model provide practical insights into strategic planning for EV charging infrastructure, balancing user needs, budgetary limitations, and policy requirements. This approach demonstrates the potential for improving EV adoption rates by developing a well-distributed, user-centric charging network that enhances accessibility and reduces range anxiety.

Multi-Objective Optimization of EV Charging Station Placement Using NSGA-II: Balancing Cost Efficiency and User Accessibility

This project optimizes EV charging station placement using NSGA-II, balancing cost and accessibility. It generates efficient solutions to meet demand, reduce costs, and enhance user convenience.


This research produces a framework that provides guidance for partners in determining the best location for SPKLU placement in the Jadetabek Region. The stages proposed in this framework are as follows:<ol><li>Data collection, there are three types of data that must be collected, namely user demand data (EV vehicle owners), potential SPKLU station data and various related parameters.</li><li>Selection of mathematical models that are tailored to company goals. There are two models prepared, namely a model that aims to minimize the costs of building and operating SPKLU and user costs for using the facilities.</li><li>Coding and executing using Lingo optimization software</li><li>Carry out testing and analysis of results processed by Lingo</li><li>Filter the optimal location further by using a machine learning model to predict the popularity of a station</li><li>Deploying solutions that have been processed by Lingo and Machine Learning.</li></ol>

Development of a Marketing Strategy Optimization Framework and SPKLU Facilities to Accelerate the Adoption of Electric Vehicles (EV)

This industrial research aims to help partner to determine optimal locations for building public electric vehicle charging stations using mathematical programming and machine learning approaches.


Drs. Tuga Mauritsius, M.Si, Ph.D.

With degrees in Mathematics both Undergraduate and Master and a PhD in the area of soft computing, my research interest is mainly in the crossover of the disciplines of mathematics and computer science (computational intelligence and or soft computing) applied to various fields such as e-commerce, retail, green energy, manufacturing, transportation, education and many of government affairs just to mention a few. The goal is to provide innovative solutions to the fields. I realize that computer science and complex mathematics have an ever-increasing role to play in decision making in those fields.The areas of research that I am interested in relate to problems faced by many organizations in Indonesia, be it profit or non-profit in harnessing/analysing the large datasets generated by many parties be it internal or external. My work is contributing to expanding the state-of-the-art computational and mathematical methods that can transform large volumes of data into useful knowledge. The work provides new analytical methodologies to unleash the full potential that 21st Century information technologies can give us to improve business practices like targeted promotions, customized inventory, promotions, and price optimization, inventory cost optimization, self-space optimization, optimal facility location problem, customized shopping experience, trend analysis, demand forecasting, transportation cost optimization, on-time product availability, optimize marketing strategies, building retention and satisfaction, serving high LTV customer better, risk analysis, among many others.

Drs. Tuga Mauritsius, M.Si, Ph.D.

Faculty Member Information Systems Management

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