Postdoc position in Prague: Solving Large-Scale Scheduling Problems: Hybridization, Parallelism, and Model Diversity in Constraint Programming

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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