@misc{BeckerLoefflerHofstedt, author = {Becker, Ilja and L{\"o}ffler, Sven and Hofstedt, Petra}, title = {ML-based Automation of Constraint Satisfaction Model Transformation and Solver Configuration}, series = {Distributed Computing and Artificial Intelligence, Special Sessions - DCAI 2022, 19th International Conference}, journal = {Distributed Computing and Artificial Intelligence, Special Sessions - DCAI 2022, 19th International Conference}, editor = {Machado, Jose Manuel F. and Chamoso, Pablo and Hernandez, Guillermo and Bocewicz, Grzegorz and Loukanova, Roussanka and Jove, Esteban and del Rey, Angel Martin and Ricca, Michela}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-23209-1}, pages = {177 -- 183}, language = {en} } @misc{LoefflerBeckerHofstedt, author = {L{\"o}ffler, Sven and Becker, Ilja and Hofstedt, Petra}, title = {A Finite-Domain Constraint-Based Approach on the Stockyard Planning Problem}, series = {Database and Expert Systems Applications : 34th International Conference, DEXA 2023, Penang, Malaysia, August 28-30, 2023, Proceedings, Part II}, journal = {Database and Expert Systems Applications : 34th International Conference, DEXA 2023, Penang, Malaysia, August 28-30, 2023, Proceedings, Part II}, editor = {Strauss, Christine and Amagasa, Toshiyuki and Kotsis, Gabriele and Tjoa, A Min and Khalil, Ismail}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-39820-9}, pages = {126 -- 133}, language = {en} } @misc{BelsLoefflerBeckeretal., author = {Bels, Maximilian and L{\"o}ffler, Sven and Becker, Ilja and Hofstedt, Petra}, title = {Constraint-Based Filtering and Evaluation of CSP Search Trees}, series = {Proceedings of the 15th International Conference on Agents and Artificial Intelligence - ICAART 2023, Volume 3}, journal = {Proceedings of the 15th International Conference on Agents and Artificial Intelligence - ICAART 2023, Volume 3}, editor = {Rocha, Ana Paula and Steels, Luc and van den Herik, H. Jaap}, publisher = {Scitepress}, isbn = {978-989-758-623-1}, doi = {10.5220/0011641100003393}, pages = {220 -- 227}, language = {en} } @misc{LoefflerBeckerBueckertetal., author = {L{\"o}ffler, Sven and Becker, Ilja and B{\"u}ckert, Carlo and Hofstedt, Petra}, title = {Enhanced Optimal Beacon Placement for Indoor Positioning: A Set Variable Based Constraint Programming Approach}, series = {Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics : November 13-15, 2023, in Rome, Italy, volume 1}, journal = {Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics : November 13-15, 2023, in Rome, Italy, volume 1}, editor = {Gini, Giuseppina and Nijmeijer, Henk and Filev, Dimitar}, publisher = {Scitepress}, isbn = {978-989-758-670-5}, doi = {10.5220/0012203400003543}, pages = {70 -- 79}, language = {en} } @misc{LoefflerBeckerHofstedtetal., author = {L{\"o}ffler, Sven and Becker, Ilja and Hofstedt, Petra and Nitze, Andre and Hennig, Silvia and Klinge, Alexander}, title = {Planung des L{\"a}ndlichen On-Demand-Verkehr - Probleme, Analyse und Algorithmen}, series = {Informatik 2023 : Designing Futures - Zuk{\"u}nfte gestalten : 26.-29. September 2023, Berlin}, journal = {Informatik 2023 : Designing Futures - Zuk{\"u}nfte gestalten : 26.-29. September 2023, Berlin}, editor = {Klein, Maike and Krupka, Daniel and Winter, Cornelia and Wohlgemuth, Volker}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Berlin}, isbn = {978-3-88579-731-9}, doi = {10.18420/inf2023_177}, pages = {1739 -- 1750}, language = {de} } @misc{PissarekHofstedtLoeffler, author = {Pißarek, Adele and Hofstedt, Petra and L{\"o}ffler, Sven}, title = {Stockyard Planning and Optimization Using Intelligent Search}, series = {Innovative Intelligent Industrial Production and Logistics}, volume = {2}, journal = {Innovative Intelligent Industrial Production and Logistics}, editor = {Dassisti, Michele and Madani, Kurosh and Panetto, Herve}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {978-3-031-80775-6}, issn = {1865-0937}, doi = {10.1007/978-3-031-80775-6_7}, pages = {94 -- 110}, language = {en} } @incollection{LoefflerHofstedt, author = {L{\"o}ffler, Sven and Hofstedt, Petra}, title = {A constraint-based savings algorithm for the Traveling Salesman Problem}, series = {Machine Learning, Optimization and Data Science : 10th International Conference, LOD 2024, Castiglione Della Pescaia, Italy, September 22-25, 2024, Revised Selected Papers}, volume = {2}, booktitle = {Machine Learning, Optimization and Data Science : 10th International Conference, LOD 2024, Castiglione Della Pescaia, Italy, September 22-25, 2024, Revised Selected Papers}, editor = {Nicosia, Guiseppe and Ojha, Varun and Giesselbach, Sven and Pardalos, M. Panos and Umeton, Renato}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031824838}, issn = {0302-9743}, doi = {10.1007/978-3-031-82484-5_15}, pages = {203 -- 217}, abstract = {There exists a plethora of NP-hard problems for which obtaining an exact solution is computationally intensive while an approximate solution can be rapidly computed. The Traveling Salesman Problem (TSP) stands out as a prominent example within this problem class, being equally significant in both research and industry sectors. Exact methods for solving the TSP are typically infeasible within acceptable timeframes beyond a certain instance size. Due to the NP-hardness of the problem, computational time requirements would quickly escalate to weeks, months, years, or even millennia. In contrast, heuristic approaches often yield sufficiently good solutions rapidly, typically within seconds or minutes. However, the drawback lies in their tendency to produce only locally optimal solutions, which may deviate significantly from a global optimum. Nevertheless, heuristic methods remain preferred in industry due to their ability to find satisfactory solutions within reasonable timeframes. It is worth noting that even marginal improvements in solutions can translate into significant cost savings in practice. Hence, there is a demand for methods capable of swiftly providing high-quality solutions while also continuously searching for further, better solutions over time. This work aims to combine two approaches: heuristic search (utilizing two simple greedy algorithms and the savings algorithm developed by Clarke and Wright) and exact search (constraint programming), to solve TSPs. Using various TSP instances, we demonstrate that our approach yields a better solution than purely local methods in at least 80\% of the cases. Furthermore, within the given time limit of 5 min, the new approach identifies a solution that is more than twice as good compared to traditional COP methods.}, language = {en} } @incollection{LoefflerHofstedt, author = {L{\"o}ffler, Sven and Hofstedt, Petra}, title = {Greedy and local search-based constraint optimization of the travelling salesman problem}, series = {Agents and Artificial Intelligence : 16th International Conference, ICAART 2024 Rome, Italy, February 24-26, 2024 : revised selected papers, Part II}, booktitle = {Agents and Artificial Intelligence : 16th International Conference, ICAART 2024 Rome, Italy, February 24-26, 2024 : revised selected papers, Part II}, editor = {Rocha, Ana Paula and Steels, Luc and van den Herik, H. Jaap}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031873294}, issn = {0302-9743}, doi = {10.1007/978-3-031-87330-0_11}, pages = {212 -- 233}, abstract = {Constraint optimization problems offer a way to obtain a global solution for a given problem. However, the promise of finding a global solution often comes at the cost of significant time and computational resources. Greedy search, local search, and cluster identification methods represent alternative approaches, which can quickly lead to local optima. In our previous work presented at the 16th International Conference on Agents and Artificial Intelligence (ICAART) 2024 [19], we introduced new methods to combine constraint programming with greedy search and clustering methods in the context of the traveling salesman problem. In this paper, we explore the advantages of incorporating greedy search and local search, which can be seen as a clustering technique in this case, into constraint optimization methods without forsaking the pursuit of a global solution. The global search process is initially designed to behave similarly to a greedy search or local search. Compared to [19], we now considered more diverse greedy and iterated local search techniques as a new clustering method. Our approach aims to achieve two key objectives: first, to accelerate the process of finding of an initial solution, and second, to ensure that this solution possesses a high level of optimality. Our approach is an enhancement of the general branch-and-bound method in constraint programming. Finally, we validate our results using the traveling salesman problem as a case study.}, language = {en} } @incollection{AssafLoefflerHofstedt, author = {Assaf, George and L{\"o}ffler, Sven and Hofstedt, Petra}, title = {Optimized scheduling of medical appointment sequences using constraint programming}, series = {Integration of constraint programming, artificial intelligence, and operations research : 22nd International Conference, CPAIOR 2025 Melbourne, VIC, Australia, November 10-13, 2025 : proceedings, Part I}, booktitle = {Integration of constraint programming, artificial intelligence, and operations research : 22nd International Conference, CPAIOR 2025 Melbourne, VIC, Australia, November 10-13, 2025 : proceedings, Part I}, editor = {Tack, Guido}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {978-3-031-95973-8}, issn = {0302-9743}, doi = {10.1007/978-3-031-95973-8_1}, pages = {1 -- 16}, abstract = {We propose a novel constraint-based model to efficiently tackle the medical appointment sequence scheduling problem (MASSP), inspired by a real-world problem at Charit{\´e} - Universit{\"a}tsmedizin Berlin. In many practical medical scenarios, scheduling a sequence of appointments, rather than a single appointment, has become increasingly essential for patients undergoing multi-stage treatments. The goal of the MASSP is to identify a set of medical resources with sufficient, consecutive, and available time slots in their calendars to create a sequence of appointments for effectively managing a treatment plan. The problem comprises various constraints, including the availability of both the intended patient and required medical resources, as well as the time and resource dependencies among the individual appointments that constitute the sequence. To address this, we formulate the problem as a constraint optimization problem () that not only captures the basic constraints of the MASSP but also optimizes resource assignment to ensure a fair workload distribution within the medical facility. The results of our experiments demonstrate that the model performs effectively under diverse conditions, which confirms the utility and robustness of the proposed model in optimizing resource allocation and ensuring equitable workload distribution.}, language = {en} } @misc{LoefflerAbbenhausAssafetal., author = {L{\"o}ffler, Sven and Abbenhaus, Viktoria and Assaf, George and Hofstedt, Petra}, title = {Solving the three-dimensional beacon placement problem using constraint-based methods, large neighborhood search, and evolutionary algorithms}, series = {Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics : Volume 1 : ICINCO}, journal = {Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics : Volume 1 : ICINCO}, editor = {Gini, Giuseppina and Precup, Radu-Emil and Filev, Dimitar P.}, publisher = {SciTePress - Science and Technology Publications}, address = {Set{\´u}bal, Portugal}, isbn = {978-989-758-770-2}, doi = {10.5220/0013724500003982}, pages = {105 -- 116}, abstract = {With the increasing prevalence of large building complexes, indoor localization is becoming an area of growing significance. In critical situations, such as emergencies in factories or care facilities, the ability to locate a person quickly can be a matter of life and death. One possibility for localization are Bluetooth beacons, which are either attached to the person or in rooms. We pursue the latter approach, whereby the beacon signals are used to determine the position of a receiving device, e.g. a mobile phone. At this, the use of a sufficient number of beacons in the building must be ensured in order to guarantee adequate coverage. However, to minimize costs, it is equally important to avoid placing unnecessary beacons. This creates a challenging optimization problem that this paper addresses through three distinct approaches: constraint programming, large neighborhood search, and evolutionary algorithms. Using simulated three-dimensional buildings, we test and evaluate these m ethods, ultimately providing a practical and efficient approach applicable to real-world building environments.}, language = {en} }