Hatice Çalık

 

European projects and projects with international partners

   

E4-share – Models for ecological, economical, efficient, electric car-sharing (Funded by Innoviris via JPI Urban Europe-Horizon 2020), 2015-2017

 

Role: Work package lead; leading and execution of technical research of the ULB team. E4-share is a European project with partners from University of Vienna, Austrian Institute of Technology, University of Bologna, ULB and tbw research GesmbH (Austria). In this project, I investigated one-way car sharing systems under pre-booked and ad-hoc (uncertain) demand settings. In addition to effective mathematical programming models, I introduced efficient Benders Decomposition algorithms for determining station location and sizing as well as vehicle allocation decisions. Several enhancement techniques including stabilization and valid inequalities that I developed provided considerable performance improvements. My research work resulted in 2 peer-reviewed papers indexed in Web of Science Core Collection and two technical reports focusing on demand forecasting methods and heuristics for one-way electric car sharing systems. Budget: 1,008,721 EUR

 

Designing the future offshore grids with emerging cable technologies. , (The Sterna 2050 Project, Funded by FOD Economie), 2025-2026

Role: Work package lead; lead technical contributor to a work package. This is an international collaboration with SuperNode (Ireland), 3E, DEME and KU Leuven as the main technical partners. We investigated how the future offshore grid may look like in the North Sea by 2050. How to design the offshore grid and how to effectively and efficiently connect it to onshore with the current and emerging superconducting cable technologies and under certain design assumptions. See also the webinar on the insights from Sterna 2050.

   

Large-scale projects as (co-)promotor

 

Combinatorial bounds for heuristic neighbourhood search (Funded by KU Leuven Impulsfonds), 2021-2022

Role: Project ideation, co-promotor, PhD supervision and technical contribution. Investigated the effectiveness of combinatorial bounds into exact and heuristic algorithms. Developed mathematical models with high quality relaxation bounds as well as efficient algorithms for several location, routing, scheduling, cutting and packing problems. Computationally analyzed the systems under parametric changes and provided managerial insights. I published 11 peer-reviewed papers utilizing the bounding and filtering techniques, supervised 1 PhD student and led collaborations with many junior and senior researchers.

 

Optimizing the location of electric vehicle charging stations on the low-voltage network (PhD Project of Bryan Coulier, Funded by VITO, joint supervision with VITO), 2022-2026

 

Role: Project ideation, co-promotor, PhD supervision and technical contribution. This is a PhD project which I have been supervising since November 2022. Prior to the official start of the project, I significantly contributed to the proposal writing. The project investigates strategies for location of EV charging stations by taking into account the low-voltage grid (LVG) stability. The approach is novel as the LVG restrictions were never considered when placing charging stations although excessive energy demand could cause undesirable impacts on the grid. My expertise in EV charging station location optimization has been a valuable asset in both setting up the goals and work plan as well as the succesful progress of the project so far. This project already led to a journal paper which is currently under review.

 

Optimization algorithms for vehicle routing problems with multiple decision levels (PhD Project of Dr. Vinícius Gandra), 2019-2023

 

Role: Co-promotor, PhD supervision and technical contribution. This is another PhD project which I supervised and has successfully completed in July 2023. In parallel to the combinatorial bounds project, we investigated the integration of aforementioned bounding and filtering methods into heuristics and metaheuristic frameworks to tackle optimization problems with multiple decision levels. These levels included two or more of location, routing, scheduling and loading decisions. In addition to supervision, I also contributed with mathematical models and conceptualization of methods and ideas. This project led to two journal and three conference papers.

 

Restoration of power systems using distributed, renewable energy resources , (PhD Project of Wout Stockman), 2025-2026

 

Role: Co-promotor, PhD supervision. This is a PhD project that I supervised between October 2025 and July 2026. My intensive research work on the power system restoration including islanding and generator start-up sequencing since October 2023 largely motivated us to initiate this project. After a joint collaboration of many months, I discontinued my involvement as my priorities shifted towards my new business.

 

Predictive Maintenance Models for Wind Farms and Their Optimization for Power Purchase Agreements , (PhD Project of Lara Hamdar), 2025-2026

Role: Co-promotor, PhD supervision. This is a PhD project that I supervised between January 2025 and July 2026. Within this project, we developed machine learning techniques for predicting hotspot temperature of winf farm transformers and underground cable temperatures leading to two scientific publications. We also developed an interactive asset management tool for wind farm operators. After a joint collaboration of nearly two years, I discontinued my involvement as my priorities shifted towards my new business.

   

Large-scale projects with multiple industrial and academic partners

 

Offshore Digitilization and Hybrid Energy Integration (ODIN), (Funded by VLAIO), 2024-2026

Role: Work package lead of the KU Leuven team, PhD supervision and guidance, master thesis supervision. The PhD project of L. Hamdar (see above) overlaps largely with the ODIN project. In addition to the asset health prediction and management tools we developed, I also provided guidance and supervision for development of a day-ahead bidding tools for systems with renewables and battery electric storage systems. As part of this project, I also supervised a master thesis that developed long-term and short-term maintenance scheduling tools for wind farms under uncertainty. After a joint collaboration of more than two years, I discontinued my involvement as my priorities shifted towards my new business. Budget: 2,179,031 EUR (1,671,004 EUR subsidies).

 

Data driven optimization models for secure real-time operation of renewable dominated power systems (DISCRETE) (Funded by FOD Economie), 2023-2026

 

Role: Technical lead on power system restoration research. This is a joint project with University of Mons (Belgium). My focus in this project is on investigation of restoration strategies in power systems with renewable participation. The goal is to provide the power system operators with a set of rules for re-energizing the system after complete balckouts. Our first publication provided efficient models through a bi-objective optimization approach. Other challenges include integrated islanding and generator start-up sequencing.

 

Data-driven logistics - ORDinL (Funded by FWO - SBO), 2019-2021

 

Role: Lead senior researcher for a technical work package and final reporting coordinator. ORDinL was a strategic basic research (SBO) project with academic partners from KU Leuven (CODeS and DTAI research groups), University of Hasselt (LOG research group), Vrije Universiteit Brussel (MOBI research center) as well as Flanders Institute for Logistics (VIL), as subcontracting partner to ensure the goals are aligned with industry needs. Throughout the project, close interaction with companies was maintained. Within this project, I developed efficient data-driven algorithms for several optimization problems. The solution methods largely benefited from the concepts investigated in the "Combiantorial Bounds" project.

 

EVERS – Electric vehicles routing and scheduling challenges within urban logistic (Funded by ANR), 2017-2019

 

Role: Lead senior researcher for a technical work package. EVERS was a multi-partner project addressing several strategic, tactical and operational challenges in EV ecosystems. Within this project, I had the opportunity to closely collaborate with researchers from LORIA, Université de technologie de Troyes (France) and University of Kent (the UK). Focused on urban distribution systems utilizing EVs, I developed a mathematical programming model and a Benders Decomposition framework for strategic, tactical and operational decision making. I introduced efficient lower and upper bounds and integrated them into the Benders framework to improve its convergence. My efforts in this project led to a peer-reviewed paper publicaiton in a highly recognized OR journal. Budget: 561,627 EUR

 

Projects for specific industrial partners

 

Customized optimization tools for angular cutting of metal stocks.

 

Industrial manufacturing companies often require materials of irregular shapes to be cut with a non-zero angle. Maximizing yield and minimzing waste are main objectives when cutting such material parts. Prior to this project, no customized tools for angular cutting of stocks in batch were available. I developed a sequence-based mathematical programming formulation together with several enhancement techniques, valid inequalities and symmetry breaking constraints. My colleague developed an efficient heuristic algorithm which is able to solve large-scale problems in a matter of seconds. My formulation played a crucial role in the success of the algorithm which has been integrated in the software solution of our industrial partner. Thanks to the several enhancements to our model, we were able to evaluate and improve the performance of the heuristic considerably.

 

Online simulation of an electric car sharing system under demand uncertainty

 

Our collaboration with a company in Flanders which provides car sharing service exclusively with electric cars was during the Spring 2022 semester. The company inroduced their system to my students in the Software Simulation course. After receiving a detailed historial data from the company, together with my students we analyzed and modeled the system. The students afterwards developed an online discrete-event system simulation and software visualization to analyze the system under multiple scenarios.

 

Machine-personnel assignment with training and interim worker requirements

 

In the production environment of a company from the Food Sector, machines can only be utilized under the supervision of trained employees. Preferably, employees rotate among different machines to gain multiple skills. However, they lose their skills if they do not work with a machine for a certain period. If a job cannot be performed by an employee on a certain day, this incurs costs. Therefore, this optimization problem requires minimizing the total cost while ensuring that the production requirements and training restrictions are satisfied. I developed a mathematical programming formulation for this problem together with several basic valid inequalities. In order to solve realistic problem instances, my colleague developed a heuristic algorithm and we published our work in a conference proceeding which can be accessed online.

Machine-personnel assignment with training and interim worker requirements

 

Production scheduling with stock- and staff-related restrictions

 

Production scheduling with stock- and staff-related restrictions

 

Multi-period service and route assignment for a distribution system

 

Multi-depot periodic vehicle routing with variable visit patterns