March 2026 - Scheduling seminar - list.iid.ciirc.cvut.cz

Tonguc Unluyurt (Sabanci University) | April 1 | A review of the sequential testing problem and its extensions
by Zdenek Hanzalek 31 Mar '26

31 Mar '26

Dear scheduling researcher, We are delighted to announce the talk given by Tonguc Unluyurt (Sabanci University). The title is "A review of the sequential testing problem and its extensions". The seminar will take place on Zoom on Wednesday, April 1 at 13:00 UTC. Join Zoom Meeting https://cesnet.zoom.us/j/93686715380?pwd=d53hcHC4TuTkOg2Zk6eFJl36ifV9rJ.1 Meeting ID: 936 8671 5380 Passcode: 103626 You can follow the seminar online or offline on our Youtube channel as well: https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A The abstract follows. In the sequential testing problem, the goal is to evaluate a Boolean (or discrete) function with the minimum expected cost, where the values of the variables can be learned by paying a cost. The variables take values independent of each other with known probabilities. For a simple series system, a solution is a permutation of the variables, whereas in the general case, a solution can be described by a binary decision tree. The problem has been studied in different domains for various applications. In this talk, we will define various extensions of the problem and focus on works published in the last 20 years to provide a comprehensive review of the results obtained. We also provide insights to explore potential areas for future research. The next talk in our series will be Bruno Escoffier (LIP6, Sorbonne) | April 15 | Resource Leveling For Scheduling Problems: Some Complexity And Approximation Results. For more details, please visit https://schedulingseminar.com/ With kind regards Zdenek Hanzalek, Michael Pinedo and Guohua Wan -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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Postdoc position in Prague: Solving Large-Scale Scheduling Problems: Hybridization, Parallelism, and Model Diversity in Constraint Programming
by Zdenek Hanzalek 24 Mar '26

24 Mar '26

Offer Description: Scheduling problems such as the Resource-Constrained Project Scheduling Problem (RCPSP) remains one of the central challenges in combinatorial optimization, particularly in large-scale industrial settings. As instance sizes grow and objective functions become more sophisticated, classical exact or single-strategy heuristic approaches are no longer sufficient. Future progress requires carefully designed hybrid and parallel solution frameworks. Today, Large Neighborhood Search (LNS) is the dominant heuristic paradigm in modern constraint programming (CP) scheduling solvers such as IBM ILOG CP Optimizer, Google OR-Tools, and OptalCP. Compared to traditional local search, LNS offers improved diversification and a stronger ability to escape local optima by iteratively destroying and repairing large fragments of a schedule. In parallel, Failure-Directed Search (FDS) provides a systematic mechanism for exploring the entire search space using a fail-first principle to prove infeasibility or optimality. While this combination is highly effective for classical objectives such as makespan minimization, CP solvers become less efficient when handling more complex criteria, such as cost-aware scheduling, or specific constraints, such as sequence dependent setup times. In such cases, purely generic search strategies may struggle to quickly produce high-quality incumbents, which are crucial for pruning the search space. A promising research direction is the integration of problem-oriented heuristics within the CP solving process. Fast constructive or improvement heuristics tailored to specific RCPSP structures can generate strong feasible solutions early in the search. These solutions provide tight upper bounds that can be injected into the CP model as hard objective constraints, significantly reducing the search space explored by FDS. The stronger the incumbent, the more aggressively the complete search can prune suboptimal regions. Beyond hybridization, large-scale RCPSP strongly benefits from parallel search architecture. Modern CP solvers, such as OptaCP, support multiple solver workers running concurrently. Instead of replicating identical models across workers, we propose exploiting model diversity: each worker can employ a different CP model formulation, search strategy, or propagation emphasis. For example, one worker may use a time-indexed formulation, another a start-time interval-based model, and another a precedence- or flow-oriented reformulation. Similarly, workers can differ in symmetry braking, variable ordering, restart policies, or LNS neighborhood design. Such heterogeneous parallelism increases robustness and coverage of the search space. Workers can share global incumbents, objective bounds, and nogoods during execution. When one worker discovers a high-quality solution, all others immediately benefit through tighter pruning. Conversely, proofs of infeasibility or bound improvements obtained by one model can accelerate convergence across the entire portfolio. This cooperative, portfolio-based architecture combines intensification within each worker with diversification across workers. The proposed research aims to design and analyze these hybrid and parallel CP frameworks namely for large-scale RCPSP. Emphasis will be placed on principled model reformulation, effective bound sharing, and scalable synchronization mechanisms that preserve solver efficiency while maximizing information exchange. [PER] L. Perron, P. Shaw, V. Furnon, Propagation guided large neighborhood search, in: M. Wallace (Ed.), Principles and Practice of Constraint Programming – CP 2004, Springer Berlin Heidelberg, Berlin, Heidelberg, 2004, pp. 468–481. [OPT] ScheduleOpt: OptalCP’s solver landing page (2023). URL https://scheduleopt.com/ [VIL ]P. Vilím, P. Laborie, P. Shaw, Failure-directed search for constraint-based scheduling, in: International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, Springer, 2015, pp. 437–453. Contract details and dates Hours Per Week: 40 Offer Starting Date: April 1, 2026 Application Deadline Date and Time: March 31, 2026 - 23:45 (Europe/Prague) Company/Institute: Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, City: Prague Postal Code: 160 00 Street: Jugoslávských partyzánů 1580/3 Skills/Qualifications: Motivation to perform excellent research, become part of the world's research communities in your field, and publish in first-tier scientific conferences and journals, Ph.D. degree or equivalent (awarded or to be completed soon), Co-author of at least 3 papers published in impact factor journals or prestigious conferences, Professional proficiency in spoken/written English (knowledge of the Czech language is not required). Specific Requirements: good background in scheduling, combinatorial optimization and algorithm design/implementation. Benefits: An initial appointment for 1 year (with an extension of up to 2 years) Salary around 70000 CZK gross monthly; check the Numbeo database for the cost of living in Prague Full social and health insurance 30 days of paid annual leave Children’s corner, kindergarten, and elementary school operated by the Czech Technical University in Prague Additional benefits such as subsidized meals, yearly benefits supporting recreational and sports activities, as well as health care programs An informal and inclusive international working environment at the Industrial Informatics Department, CIIRC, CTU in Prague. Selection process: Interested candidates are invited to submit their applications at: https://forms.gle/hj7bMTuKM4ghzPC17 [using Postdoc Position ID: 03-Postdoc-Hanzalek] The application package should contain: Motivation letter (up to two pages), stating personal goals and research interests Academic curriculum vitae, including a list of publications highlighting the three most important ones Contact details for two to three referees who could support your application, A copy or a link to your Ph.D. thesis, Date of your Ph.D. award or the expected date of your Ph.D. thesis defense. Euraxess code: https://www.euraxess.cz/jobs/415419 -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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Postdoc position in Prague: Learning-Augmented Combinatorial Optimization Algorithms for Scheduling and Packing
by Zdenek Hanzalek 24 Mar '26

24 Mar '26

Offer Description: The project addresses difficult scheduling and packing problems in the sense of computational complexity, for which classical exact approaches are often impractical at realistic scales. The goal is to design new algorithmic frameworks that combine established tools from Operations Research with modern Machine Learning methods to produce high-quality solutions within acceptable computational times. While rooted in OR, the project requires and will further develop strong competencies in Machine Learning. Over the past decade, learning-augmented optimization has emerged as a promising paradigm. In exact methods, machine learning has been used to tune solver parameters or guide search in tree-based algorithms for mathematical programming. In heuristic optimization, learning has supported diversification strategies, automated selection of algorithms for specific instances, and even direct construction of solutions. More recently, reinforcement learning and deep learning techniques have been used to guide local search procedures, particularly for transportation and routing problems, demonstrating substantial performance improvements. This project will focus on scheduling and packing settings, developing general methodologies rather than problem-specific tricks. Two complementary research directions will be pursued. First, machine learning will be used to improve the parameterization of heuristic algorithms. Second, learning methods—especially reinforcement learning—will be employed to guide the exploration of solution spaces. This includes selecting promising neighborhoods, prioritizing moves in local search, or constructing solutions incrementally. The research will build on several successful applications of ML in combinatorial optimization. These include reinforcement learning–guided greedy procedures for graph optimization problems, predictive models for deciding when decomposition techniques should be applied, and classifiers that identify structural characteristics of high-quality solutions. Additional directions involve predicting optimal objective values for complex engineering design problems and developing reinforcement learning–enhanced metaheuristics, such as iterated local search for makespan minimization in advanced manufacturing scheduling. The developed methods will be evaluated on challenging NP-hard scheduling and packing problems, including both well-studied benchmark problems with strong existing heuristics and more applied, real-world problems where current methods remain insufficient. The objective is to demonstrate that the integration of ML and OR techniques can yield robust improvements across different problem types. About the group: The Optimization Group, led by Zdenek Hanzalek, focuses on scheduling and combinatorial optimization. The group collaborates strongly with high-tech companies (CEZ – Czech Energy Group, Porsche Engineering Services, EATON, Skoda Auto, ST Microelectronics, Volkswagen, DHL, …). Zdenek is the principal investigator of the Roboprox project and organizer of SchedulingSeminar.com. [1] Bengio, Y., Lodi, A. Prouvost, A. (2021). Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon, European Journal of Operational Research, 290:405-421. [2] Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev, Reinforcement learning for combinatorial optimization: A survey, Computers & Operations Research, Volume 134, 2021. [3] Heinz, V.; Hanzálek, Z.; Vilím, P.: Reinforcement Learning for Search Tree Size Minimization in Constraint Programming: New Results on Scheduling Benchmarks, Computers & Industrial Engineering, Volume 209, November 2025, 111413. [4] Roman Václavík, Antonín Novák, Přemysl Šůcha, Zdeněk Hanzálek, Accelerating the Branch-and-Price Algorithm Using Machine Learning, European Journal of Operational Research, Volume 271, Issue 3, 2018, Pages 1055-1069. [5] Grus, J.; Hanzalek, Z.: Automated placement of analog integrated circuits using priority-based constructive heuristic, Computers & Operations Research, Volume 167, 106643, July 2024. Contract details and dates Hours Per Week: 40 Offer Starting Date: April 1, 2026 Application Deadline Date and Time: March 31, 2026 - 23:45 (Europe/Prague) Company/Institute: Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, City: Prague Postal Code: 160 00 Street: Jugoslávských partyzánů 1580/3 Skills/Qualifications: Motivation to perform excellent research, become part of the world's research communities in your field, and publish in first-tier scientific conferences and journals, Ph.D. degree or equivalent (awarded or to be completed soon), Co-author of at least 3 papers published in impact factor journals or prestigious conferences, Professional proficiency in spoken/written English (knowledge of the Czech language is not required). Specific Requirements: good background in scheduling, combinatorial optimization and algorithm design/implementation. Benefits: An initial appointment for 1 year (with an extension of up to 2 years) Salary around 70000 CZK gross monthly; check the Numbeo database for the cost of living in Prague Full social and health insurance 30 days of paid annual leave Children’s corner, kindergarten, and elementary school operated by the Czech Technical University in Prague Additional benefits such as subsidized meals, yearly benefits supporting recreational and sports activities, as well as health care programs An informal and inclusive international working environment at the Industrial Informatics Department, CIIRC, CTU in Prague. Selection process: Interested candidates are invited to submit their applications at: https://forms.gle/hj7bMTuKM4ghzPC17 [using Postdoc Position ID: 04-Postdoc-Hanzalek] The application package should contain: Motivation letter (up to two pages), stating personal goals and research interests Academic curriculum vitae, including a list of publications highlighting the three most important ones Contact details for two to three referees who could support your application, A copy or a link to your Ph.D. thesis, Date of your Ph.D. award or the expected date of your Ph.D. thesis defense. Euraxess code: https://www.euraxess.cz/jobs/415421 -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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Kevin Schewior (University of Cologne) | March 18 | Combinatorial Perpetual Scheduling
by Zdenek Hanzalek 17 Mar '26

17 Mar '26

Dear scheduling researcher, We are delighted to announce the talk given by Kevin Schewior (University of Cologne). The title is "Combinatorial Perpetual Scheduling". The seminar will take place on Zoom on Wednesday, March 18 at 14:00 UTC. Join Zoom Meeting https://cesnet.zoom.us/j/94879279269?pwd=f21ZmDYYXpaIa8wH1tq9mTuHGTkSTU.1 Meeting ID: 948 7927 9269 Passcode: 632094 You can follow the seminar online or offline on our Youtube channel as well: https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A The abstract follows. In this talk, I am going to give an overview of recent developments in perpetual scheduling, with a focus on combinatorial versions. Here, given a set system I on a ground set E, a (perpetual) schedule consists of an independent set from I for every discrete time step, with the objective of fulfilling frequency requirements on the occurrence of elements in E. We focus specifically on combinatorial bamboo garden trimming, where elements accumulate height at growth rates g(e) for element e and are reset to zero when scheduled, with the goal of minimizing the maximum height attained by any element. As a normalization, we assume that the vector of growth rates is given as a convex combination of incidence vectors from I. We prove that, when the set system is a matroid, it is possible to guarantee a maximum height of at most 2, which is optimal. For general set systems, one can only guarantee a height that is logarithmic in the cardinality of E. The talk is partially based on joint work with Mirabel Mendoza-Cadena, Arturo Merino, and Mads Anker Nielsen. The next talk in our series will be Tonguc Unluyurt (Sabanci University) | April 1 | A review of the sequential testing problem and its extensions. For more details, please visit https://schedulingseminar.com/ With kind regards Zdenek Hanzalek, Michael Pinedo and Guohua Wan -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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Postdoc position in Prague: Learning-Augmented Combinatorial Optimization Algorithms for Scheduling and Packing
by Zdenek Hanzalek 04 Mar '26

04 Mar '26

Offer Description: The project addresses difficult scheduling and packing problems in the sense of computational complexity, for which classical exact approaches are often impractical at realistic scales. The goal is to design new algorithmic frameworks that combine established tools from Operations Research with modern Machine Learning methods to produce high-quality solutions within acceptable computational times. While rooted in OR, the project requires and will further develop strong competencies in Machine Learning. Over the past decade, learning-augmented optimization has emerged as a promising paradigm. In exact methods, machine learning has been used to tune solver parameters or guide search in tree-based algorithms for mathematical programming. In heuristic optimization, learning has supported diversification strategies, automated selection of algorithms for specific instances, and even direct construction of solutions. More recently, reinforcement learning and deep learning techniques have been used to guide local search procedures, particularly for transportation and routing problems, demonstrating substantial performance improvements. This project will focus on scheduling and packing settings, developing general methodologies rather than problem-specific tricks. Two complementary research directions will be pursued. First, machine learning will be used to improve the parameterization of heuristic algorithms. Second, learning methods—especially reinforcement learning—will be employed to guide the exploration of solution spaces. This includes selecting promising neighborhoods, prioritizing moves in local search, or constructing solutions incrementally. The research will build on several successful applications of ML in combinatorial optimization. These include reinforcement learning–guided greedy procedures for graph optimization problems, predictive models for deciding when decomposition techniques should be applied, and classifiers that identify structural characteristics of high-quality solutions. Additional directions involve predicting optimal objective values for complex engineering design problems and developing reinforcement learning–enhanced metaheuristics, such as iterated local search for makespan minimization in advanced manufacturing scheduling. The developed methods will be evaluated on challenging NP-hard scheduling and packing problems, including both well-studied benchmark problems with strong existing heuristics and more applied, real-world problems where current methods remain insufficient. The objective is to demonstrate that the integration of ML and OR techniques can yield robust improvements across different problem types. About the group: The Optimization Group, led by Zdenek Hanzalek, focuses on scheduling and combinatorial optimization. The group collaborates strongly with high-tech companies (CEZ – Czech Energy Group, Porsche Engineering Services, EATON, Skoda Auto, ST Microelectronics, Volkswagen, DHL, …). Zdenek is the principal investigator of the Roboprox project and organizer of SchedulingSeminar.com. [1] Bengio, Y., Lodi, A. Prouvost, A. (2021). Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon, European Journal of Operational Research, 290:405-421. [2] Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev, Reinforcement learning for combinatorial optimization: A survey, Computers & Operations Research, Volume 134, 2021. [3] Heinz, V.; Hanzálek, Z.; Vilím, P.: Reinforcement Learning for Search Tree Size Minimization in Constraint Programming: New Results on Scheduling Benchmarks, Computers & Industrial Engineering, Volume 209, November 2025, 111413. [4] Roman Václavík, Antonín Novák, Přemysl Šůcha, Zdeněk Hanzálek, Accelerating the Branch-and-Price Algorithm Using Machine Learning, European Journal of Operational Research, Volume 271, Issue 3, 2018, Pages 1055-1069. [5] Grus, J.; Hanzalek, Z.: Automated placement of analog integrated circuits using priority-based constructive heuristic, Computers & Operations Research, Volume 167, 106643, July 2024. Contract details and dates Hours Per Week: 40 Offer Starting Date: April 1, 2026 Application Deadline Date and Time: March 31, 2026 - 23:45 (Europe/Prague) Company/Institute: Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, City: Prague Postal Code: 160 00 Street: Jugoslávských partyzánů 1580/3 Skills/Qualifications: Motivation to perform excellent research, become part of the world's research communities in your field, and publish in first-tier scientific conferences and journals, Ph.D. degree or equivalent (awarded or to be completed soon), Co-author of at least 3 papers published in impact factor journals or prestigious conferences, Professional proficiency in spoken/written English (knowledge of the Czech language is not required). Specific Requirements: good background in scheduling, combinatorial optimization and algorithm design/implementation. Benefits: An initial appointment for 1 year (with an extension of up to 2 years) Salary around 70000 CZK gross monthly; check the Numbeo database for the cost of living in Prague Full social and health insurance 30 days of paid annual leave Children’s corner, kindergarten, and elementary school operated by the Czech Technical University in Prague Additional benefits such as subsidized meals, yearly benefits supporting recreational and sports activities, as well as health care programs An informal and inclusive international working environment at the Industrial Informatics Department, CIIRC, CTU in Prague. Selection process: Interested candidates are invited to submit their applications at: https://forms.gle/hj7bMTuKM4ghzPC17 [using Postdoc Position ID: 04-Postdoc-Hanzalek] The application package should contain: Motivation letter (up to two pages), stating personal goals and research interests Academic curriculum vitae, including a list of publications highlighting the three most important ones Contact details for two to three referees who could support your application, A copy or a link to your Ph.D. thesis, Date of your Ph.D. award or the expected date of your Ph.D. thesis defense. Euraxess code: https://www.euraxess.cz/jobs/415421 -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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Postdoc position in Prague: Solving Large-Scale Scheduling Problems: Hybridization, Parallelism, and Model Diversity in Constraint Programming
by Zdenek Hanzalek 04 Mar '26

04 Mar '26

Offer Description: Scheduling problems such as the Resource-Constrained Project Scheduling Problem (RCPSP) remains one of the central challenges in combinatorial optimization, particularly in large-scale industrial settings. As instance sizes grow and objective functions become more sophisticated, classical exact or single-strategy heuristic approaches are no longer sufficient. Future progress requires carefully designed hybrid and parallel solution frameworks. Today, Large Neighborhood Search (LNS) is the dominant heuristic paradigm in modern constraint programming (CP) scheduling solvers such as IBM ILOG CP Optimizer, Google OR-Tools, and OptalCP. Compared to traditional local search, LNS offers improved diversification and a stronger ability to escape local optima by iteratively destroying and repairing large fragments of a schedule. In parallel, Failure-Directed Search (FDS) provides a systematic mechanism for exploring the entire search space using a fail-first principle to prove infeasibility or optimality. While this combination is highly effective for classical objectives such as makespan minimization, CP solvers become less efficient when handling more complex criteria, such as cost-aware scheduling, or specific constraints, such as sequence dependent setup times. In such cases, purely generic search strategies may struggle to quickly produce high-quality incumbents, which are crucial for pruning the search space. A promising research direction is the integration of problem-oriented heuristics within the CP solving process. Fast constructive or improvement heuristics tailored to specific RCPSP structures can generate strong feasible solutions early in the search. These solutions provide tight upper bounds that can be injected into the CP model as hard objective constraints, significantly reducing the search space explored by FDS. The stronger the incumbent, the more aggressively the complete search can prune suboptimal regions. Beyond hybridization, large-scale RCPSP strongly benefits from parallel search architecture. Modern CP solvers, such as OptaCP, support multiple solver workers running concurrently. Instead of replicating identical models across workers, we propose exploiting model diversity: each worker can employ a different CP model formulation, search strategy, or propagation emphasis. For example, one worker may use a time-indexed formulation, another a start-time interval-based model, and another a precedence- or flow-oriented reformulation. Similarly, workers can differ in symmetry braking, variable ordering, restart policies, or LNS neighborhood design. Such heterogeneous parallelism increases robustness and coverage of the search space. Workers can share global incumbents, objective bounds, and nogoods during execution. When one worker discovers a high-quality solution, all others immediately benefit through tighter pruning. Conversely, proofs of infeasibility or bound improvements obtained by one model can accelerate convergence across the entire portfolio. This cooperative, portfolio-based architecture combines intensification within each worker with diversification across workers. The proposed research aims to design and analyze these hybrid and parallel CP frameworks namely for large-scale RCPSP. Emphasis will be placed on principled model reformulation, effective bound sharing, and scalable synchronization mechanisms that preserve solver efficiency while maximizing information exchange. [PER] L. Perron, P. Shaw, V. Furnon, Propagation guided large neighborhood search, in: M. Wallace (Ed.), Principles and Practice of Constraint Programming – CP 2004, Springer Berlin Heidelberg, Berlin, Heidelberg, 2004, pp. 468–481. [OPT] ScheduleOpt: OptalCP’s solver landing page (2023). URL https://scheduleopt.com/ [VIL ]P. Vilím, P. Laborie, P. Shaw, Failure-directed search for constraint-based scheduling, in: International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, Springer, 2015, pp. 437–453. Contract details and dates Hours Per Week: 40 Offer Starting Date: April 1, 2026 Application Deadline Date and Time: March 31, 2026 - 23:45 (Europe/Prague) Company/Institute: Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, City: Prague Postal Code: 160 00 Street: Jugoslávských partyzánů 1580/3 Skills/Qualifications: Motivation to perform excellent research, become part of the world's research communities in your field, and publish in first-tier scientific conferences and journals, Ph.D. degree or equivalent (awarded or to be completed soon), Co-author of at least 3 papers published in impact factor journals or prestigious conferences, Professional proficiency in spoken/written English (knowledge of the Czech language is not required). Specific Requirements: good background in scheduling, combinatorial optimization and algorithm design/implementation. Benefits: An initial appointment for 1 year (with an extension of up to 2 years) Salary around 70000 CZK gross monthly; check the Numbeo database for the cost of living in Prague Full social and health insurance 30 days of paid annual leave Children’s corner, kindergarten, and elementary school operated by the Czech Technical University in Prague Additional benefits such as subsidized meals, yearly benefits supporting recreational and sports activities, as well as health care programs An informal and inclusive international working environment at the Industrial Informatics Department, CIIRC, CTU in Prague. Selection process: Interested candidates are invited to submit their applications at: https://forms.gle/hj7bMTuKM4ghzPC17 [using Postdoc Position ID: 03-Postdoc-Hanzalek] The application package should contain: Motivation letter (up to two pages), stating personal goals and research interests Academic curriculum vitae, including a list of publications highlighting the three most important ones Contact details for two to three referees who could support your application, A copy or a link to your Ph.D. thesis, Date of your Ph.D. award or the expected date of your Ph.D. thesis defense. Euraxess code: https://www.euraxess.cz/jobs/415419 -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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Helmut Simonis (Insight Centre, UCC) | March 4 | Constraint Based Scheduling: A User Perspective
by Zdenek Hanzalek 02 Mar '26

02 Mar '26

Dear scheduling researcher, We are delighted to announce the talk given by Helmut Simonis (Insight Centre, UCC). The title is "Constraint Based Scheduling: A User Perspective". The seminar will take place on Zoom on Wednesday, March 4 at 14:00 UTC. Join Zoom Meeting https://cesnet.zoom.us/j/98113121925?pwd=WhXaUqchaTaS2xqMQhx7ea1YXgxW9u.1 Meeting ID: 981 1312 1925 Passcode: 559971 You can follow the seminar online or offline on our Youtube channel as well: https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A The abstract follows. Constraint Programming has been very successful in solving scheduling problems. In this talk we present a number of case studies from a user's perspective, focusing on how to develop a solution with off-the-shelf tools, rather than on the underlying technology. We discuss issues of tool choice, modeling approaches, visualization, and overall integration on sample problems. The first case study is a complex scheduling and planning problem for Siemens Energy, developed within the ASSISTANT European project. The second example looks at models for a Hospital Integrated Resource Management problem, based on the IHTC competition of 2024. The third case study is a generic scheduling tool which we have developed to provide a simple-to-use solution for small to medium sized scheduling problems. The next talk in our series will be Kevin Schewior (University of Cologne) | March 18 | Combinatorial Perpetual Scheduling. For more details, please visit https://schedulingseminar.com/ With kind regards Zdenek Hanzalek, Michael Pinedo and Guohua Wan -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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