Degrees

MSc in mathematics, Mathematics and Computer Science,  School of Applied Mathematics and Informatics, University of Osijek, Croatia, 2023.

BSc in Mathematics and Computer Science, Department of Mathematics, University of Osijek, Croatia, 2021.

Publications

1. J. Sabljo, M. Đumić, M. Đurasević, Analysis of Training Models for Evolving Heuristics in the Unrelated Parallel Machines Scheduling Problem with Additional Resources, 49th ICT and Electronics Convention MIPRO 2026, Opatija, 2026
   Abstract

   The unrelated parallel machines scheduling problem with precedence and additional resource constraints (UPMSP-PR) is an NP-hard problem, which makes exact solution approaches impractical for larger instances. Consequently, heuristic methods are commonly employed to address this problem. The design of such heuristics can be effectively tackled using genetic programming (GP), a hyper-heuristic approach aimed at evolving new heuristics with strong generalization capabilities.To achieve improved generalization, GP must be trained using high-quality training models and a diverse set of problem instances. In this paper, we generate a comprehensive set of UPMSP-PR instances and analyze different training models, with particular emphasis on the impact of instance selection during training on overall model performance. Specifically, we investigate the influence of problem instance size and examine the role of a validation set during the training phase.
2. J. Sabljo, M. Đumić, M. Đurasević, Evolving Dispatching Rules for the Unrelated Parallel Machines Scheduling Problem with Precedence and Resource Constraints with Genetic Programming, GECCO '26: Proceedings of the Genetic and Evolutionary Computation Conference, San Jose, Costa Rica, 2026, 817-825
   Abstract

   The unrelated parallel machines scheduling problem is an important and challenging problem with significant industrial relevance. This paper addresses a specific variant involving precedence constraints between jobs and the requirement of additional resources for job execution. The problem is NP-hard, and practical solution approaches are therefore predominantly heuristic. In dynamic scheduling environments, dispatching rules (DRs) represent one of the most effective and widely used heuristic approaches. A DR consists of a schedule generation scheme (SGS) and a priority function (PF). The manual design of high-quality DRs is difficult and time-consuming, making the problem well suited for hyper-heuristic and evolutionary approaches. In this work, genetic programming (GP) is employed to automatically evolve the PF component of DRs. Additionally, several problem-specific SGSs are proposed and evaluated. The evolved DRs are compared against adapted state-of-the-art DRs from the literature. The results show that the proposed GP-based approach enables the automated generation of high-quality DRs, outperforming manually designed heuristics for the considered problem.
3. J. Sabljo, R. Čorić, M. Đumić, M. Đurasević, Evolving Priority Rules to Optimize Multi-Objective Criteria for the Resource-Constrained Project Scheduling Problem, 2025 MIPRO 48th ICT and Electronics Convention, Opatija, Croatia, 2025, 24-29
   Abstract

   The resource-constrained project scheduling problem (RCPSP) is a combinatorial optimization problem that entails allocating a set of tasks to limited resources, such as budget, workers, or equipment while aiming to optimize a defined objective function. Since this problem belongs to the class of NP-hard problems, heuristic approaches such as priority rules (PRs) are often used to solve them. In literature, genetic programming (GP) was used to evolve PRs for RCPSP while optimizing a single criterion, but optimizing only a single criterion is rare in real-world problems. Usually, realworld problems require the optimization of several criteria simultaneously. In this paper, new PRs are evolved using GP for multi-objective RCPSP. More precisely, sets of Pareto optimal solutions were obtained using non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated sorting genetic algorithm III (NSGA-III). Through experiments, these two algorithms were compared using multi-objective performance measures. Additionally, a comparison between multi-objective PRs and single-objective PR was conducted.

Projects

• Hyper-Heuristic Design for Unrelated Machines Environment with Auxiliary Resources, funded by Croatian Science Foundation, duration: 01.04.2026.-31.03.2031., Project leader: Mateja Đumić (School of Applied Mathematics and Informatics, Osijek).
• Zajedno raSTEMo, duration: 12.03.2025. – 11.03.2028., project leaders: Tomislav Rudec (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek) and Mateja Đumić (School of Applied Mathematics and Informatics, Osijek).
• 2026-2027: bilateral research project entitled “Diophantine m-tuples and related topics”, with Paris Lodron University of Salzburg, Austria (supported by the Croatian Ministry of Science, Education and Youth), project leader (Croatian side : Ivan Soldo (School of Applied Mathematics and Informatics, Osijek))

Professional Activities

Conferences:

• 48th ICT and Electronics Convention – 2025 MIPRO , Opatija, Croatia, June 2-6, 2025

Service activities:

• Festival znanosti:
  • 2024. radionica – Mogu li mikrokontroleri biti inteligentni?
  • 2025. radionica – Scratch-izgradi svoj put u mreži labirinta

Teaching

  Nastavne aktivnosti u zimskom semestru akademske 2026./2027.

• • Uvod u računalnu znanost

  Nastavne aktivnosti u ljetnom semestru akademske 2026./2027.

• • Matematička logika u računalnoj znanosti
  • Moderni sustavi baza podataka

  Konzultacije (Office Hours): Srijedom u 12:00 ili po dogovoru e-mailom.