https://github.com/lucadiba/timetabling-problem-solver
A C++ metaheuristics solver for the ETP (examination timetabling problem)
https://github.com/lucadiba/timetabling-problem-solver
Last synced: 4 months ago
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A C++ metaheuristics solver for the ETP (examination timetabling problem)
- Host: GitHub
- URL: https://github.com/lucadiba/timetabling-problem-solver
- Owner: LucaDiba
- License: mit
- Created: 2020-01-14T13:29:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-17T23:43:23.000Z (over 5 years ago)
- Last Synced: 2025-01-01T19:12:55.483Z (6 months ago)
- Language: C++
- Homepage:
- Size: 106 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Examination Timetabling Problem Solver
[](https://opensource.org/licenses/MIT)
A C++ metaheuristics solver for the ETP (examination timetabling problem).
# Data representation
- Vector of exams: `v[i]` is the timeslot assigned to exam `i`
- Vector of timeslots: `v[t]` is a list of exams scheduled on timeslot `t`
# Metaheuristic
## 1. Multi-start: Genetic Algorithm
Solution → chromosome
Exam (element of the solution) → gene
#### Initial population
- Population size: max(#exams * 0.1, 10)
- Population: random chromosomes (feasible solution):
1. exams are sorted by number of conflicts
2. exams are placed in random timeslots
3. restart from scratch when stuck
#### Evolution
1. Sorting chromosomes according to the fitness score (`1/penalty`)
2. Offspring generation
- Elite Chromosomes: 20%
- Crossover (adapted for feasibility): 30%
- Mutation: 30%
- Inversion (adapted for feasibility): 20%
#### Stopping condition
(used > 80% of total time) OR ( (used > 50% of t.t.) AND (no improving solutions from more than 8% of t.t) )
## 2. Neighborhood Search: Simulated Annealing
#### Initialization
- Initial solution: the best one found by GA
- Initial temperature: `(F(x_AVG) - F(x*)) / ln(0.5)`
- Cooling rate: `0.99`
- Plateau: 10 * `n_exams` * `n_timeslots`
#### Neighbour
1. Pick a random exam
2. Pick a random timeslot (different from current one)
3. Move the exam to that timeslot if is feasible to do it
4. Move it if `rand(0, 1) < p`, where `p` is a function of the candidate and the current penalties
# Posible improvements
- Use more C++ syntax instead of C's one
- Multi-thread solution generation (and the entire problem itself)
- Incremental penaly computation when moving examx