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/~hanzale/
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