Ray Jasson
Yi Qing
24/07/2020
Presentation Scheduling problem, which is analogous to the famous University Course Timetabling Problem (UCTP), involves allocating a set of presentations and resources including speakers, supervisors and venues to different time slots while considering various constraints. Supervisors have different preferences including choosing to attend certain number of consecutive presentations, choosing number of days to complete all the presentations and deciding whether they want to change venue while attending consecutive presentations. The problem is defined according to the following groups:
- Presentations
- Slots (Time slots and Venues)
- Supervisors
- Preferences
n presentations will be scheduled to m slots in which each slot is a combination of a venue and a time slot. For example, the timetable below has a total of 10 slots for 2 venues:
| Day | Venue | 0900-1000 | 1000-1100 | 1100-1200 | 1300-1400 | 1400-1500 |
|---|---|---|---|---|---|---|
| Monday | Room A | 1 | 2 | 3 | 4 | 5 |
| Monday | Room B | 6 | 7 | 8 | 9 | 10 |
Each presentation is presented by a speaker and supervised by three supervisors. There are k supervisors available. There are two types of constraints: hard constraints and soft constraints. Hard constraints cannot be violated to prevent generating an infeasible schedule, whereas soft constraints might be violated, however, the number of violations must be minimized.
- Hard Constraints
- HC01: All presentations must be scheduled and each presentation can be scheduled only once
- HC02: No supervisors can attend two or more presentations concurrently
- HC03: Some venues are unavailable on specific time slots
- HC04: Some supervisors are unavailable on specific time slots
- HC05: All presentations must be scheduled to a slot without sharing of venues
- Soft Constraints
- SC01: Number of consecutive presentations should not exceed supervisor’s preference
- SC02: Number of days in which a supervisor needs to attend a presentation should not exceed supervisor’s preference
- SC03: Some supervisors prefer not to change venue while attending consecutive presentations
In this repository, there are n = 118 presentations, m = 300 slots and k = 47 supervisors. There are 4 venues: Viva Room (VR), Meeting Room (MR), Interaction Room (IR) and BJIM Discussion Room (BJIM). Each day has 15 slots in which each slot lasts for 30 minutes. 300 slots are shceduled from Monday until Friday.
🔓 Note that each slot is a combination of a venue and a timeslot.
There are 6 input files:
SupExaAssign.csvspecifies supervisors that are in charge of the presentationsHC03specifies unavailable venuesHC04specifies unavailable supervisors on specific slotsSC01specifies supervisors' preferred number of consecutive presentationsSC02specifies supervisors' preferred number of days to complete all the presentationsSC03specifies supervisors' preferences of changing venue during consecutive presentations,yesindicates supervisors prefer to attend consecutive presentations without changing venue, whereasnoindicates supervisors do not want to change venue while attending consecutive presentations
Hybrid genetic algorithm-simulated annealing (HGASA) algorithm is the combination of genetic algorithm (GA) with simulated annealing as a local search method to accelerate the convergence speed. The figure below shows the flowchart of HGASA algorithm.
Flowchart of HGASA algorithm
Refer to hybrid_system function in hybrid_system.py for more details.
The initial set of candidate solutions and sets of constraints are represented using matrix. The matrices are generated using given data from input files and through the process of matrix multiplication. The figure below shows three required matrices that are generated through load() function in data.py.
From left, slot-by-presentation matrix, presentation-by-presentation matrix and supervisor-by-preference matrix
The slot-by-presentation matrix is the chromosome in genetic algorithm and the candidate in simulated annealing. Other matrices are required by the penalty function for evaluation of penalty points. 0 indicates the slots are available, whereas -1 indicates the slots are unavailable due to the hard constraints. When initializing the population, 1 indicates a presentation has been assigned to a specific slot.
The penalty function is used to evaluate the fitness of the solution, which is the resulting presentation schedule. It is used to evaluate any violations of HC02, SC01, SC02 and SC03. Each violation increases the penalty points by 10. The higher the penalty points, the lower the fitness of the solution.
🔓 Note that if the number of consecutive presentations is less than the supervisor's preference, each difference will increase the penalty point by 1 in order to encourage the generated schedule to have consecutive presentations.
Refer to penalty_function.py for more details.
Steady-state genetic algorithm is different from the generational genetic algorithm in which only two chromosomes are selected to undergo crossover and mutation to generate two children. Two worst chromosomes will be chosen from the population to be replaced by the new children. It updates the population in a piecemeal fashion rather than all at one time.
The size of population is initialized to 10 which is an adequate size considering the size of this presentation scheduling problem. A random slot is assigned to each presentation in a chromosome. Note that the slots are assigned in a way such that the schedule does not violate HC03 and HC04. Empty slot indicates no presentation is assigned to this slot previously so HC01 and HC05 will not be violated. Each 1s in the slot-by-presentation matrix (chromosome) represents the assigned presentation in its respective slot. Penalty of chromosome is evaluated and added to the population of penalty points.
Refer to generate_chromosome function in genetic_algorithm.py and hybrid_system.py for more details.
Tournament selection with tournament size of 2 is carried out twice. In each tournament selection, two random chromosomes are selected and the chromosome with the lowest penalty point among them is selected.
Refer to selection function in genetic_algorithm.py for more details.
Two-point crossover is carried out to reduce the probability of breaking up good pairs in the chromosome which is more frequent in one-point crossover and uniform crossover. The parent chromosomes selected in tournament selection exchange their presentations between the cutpoints to produce two new children. The figure below shows two parent chromosomes exchange their presentations between c1 and c2 to generate two new child chromosomes.
Crossover of two parent chromosomes
Repair is carried out after crossover in which the presentation is assigned to another available and empty slot if there are more than 1 presentations assigned for the slot. The purpose of this operation is to ensure HC01 and HC05 are not violated.
Refer to crossover and repair functions in genetic_algorithm.py for more details.
Two random presentations have their slots exchanged. If both presentations have slots that are not exchangeable, indicating the slots are unavailable for either one of the presentation, another presentation and slot are selected randomly. The figure below shows the mutation process.
Mutation of a chromosome
Refer to mutation function in genetic_algorithm.py for more details.
Two chromosomes with the highest penalty points are replaced by two new child chromosomes generated through crossover and mutation. Their penalty points are updated as well.The maximum number of generations is set to be 100 generations in this case. In each generation, 6 processes are executed iteratively: selection, crossover, repair, mutation, penalty evaluation and replacement until the maximum generation is reached.
Refer to replacement and reproduction functions in genetic_algorithm.py for more details.
Simulated annealing (SA) is used in HGASA algorithm as a local search algorithm. SA is a metaheuristic inspired by statistical physics. SA has the ability to avoid being trapped in local minima and it is proven that SA is able to find the global optimum if given infinite time.
The initial candidate of SA is the chromosome with the lowest penalty point from the previous GA. The basic procedure of SA is to generate neighbouring solutions and evaluate them. If the neighbouring solution generated is better than the best solution, the best solution is updated. If otherwise, the neighbouring solution is accepted based on a probability density function. The best solution will only be updated when the neighbouring solution is better than the best solution. A poor neighbouring solution will be accepted by probability as the candidate to generate a new neighbouring solution, but not as the best solution. The figure below shows the process of SA.
Process of Simulated Annealing
In each iteration, one neighbourhood structure will be randomly selected to be applied to the candidate solution to produce a neighbouring solution. A neighbouring solution is a solution that is slightly different from the candidate solution. There are in total four neighbourhood structures implemented:
- Neighbourhood Structure 1
Select a supervisor at random and swap the timeslots of two presentations supervised by the supervisor - Neighbourhood Structure 2
Select a presentation at random and change its assigned venue without changing the assigned day and time - Neighbourhood Structure 3
Select a presentation at random and move it to a randomly selected empty slot - Neighbourhood Structure 4
Select a presentation at random and move another presentation that has at least one same supervisor to the empty slot adjacent to the presentation chosen at random
Refer to neighbourhood_structure1, neighbourhood_structure2, neighbourhood_structure3 and neighbourhood_structure4 functions in simulated_annealing.py for more details.
SA is carried out for a number of iterations until stopping criterion has been met. The procedure is described by the following steps:
- Set Initial Annealing Temperature
The initial temperature of simulated annealing is set to the difference between the lowest and highest penalty points of the population found using GA. - Apply Random Neighbourhood Structure
- Penalty and Acceptance Probability
The penalty of the newly generated neighbouring solution is computed and compared with the penalty of the candidate solution. The neighbouring solution is accepted if it is better than the candidate solution. In the case where there is no improvement, a random number,Rthat is uninformedly distributed between 0 and 1 is generated and the probability density function value, e-δ/T is calculated. If the probability density function value is higher thanR, the neighbouring solution is accepted as the candidate solution to generate a new neighbouring solution. - Cooling Schedule
An exponential cooling scheme (ECS) is used. The temperature decrement rule implemented is Tk + 1 = αTk where α is set to 0.9999, a value very close to 1. The temperature is decreased slowly and continuously. - Final Temperature
The final temperature is the stopping condition. The final temperature is set to 0.0001 of the initial temperature.
Refer to anneal function in simulated_annealing.py for more details.
Four external packages are used to implement HGASA algorithm:
- NumPy
NumPy provides a powerful n-dimensional array structure and numerical computing tools. It is ideal to create matrix, and has a significantly faster data access speed and more efficient memory usage than Python list. - Numba
Numba is a just-in-time (JIT) compiler for Python that can speed up the execution of code that uses NumPy arrays and functions, and loops frequently. Parts of user-defined functions in Python are preceded with@njit(cache=True)decorator.@njit()compiles the decorated function in nopython mode so the compiled code runs without the involvement of Python interpreter.cache=Trueindicates the result of function compilation will be saved into a file-based cache to save compilation time when invoking decorated functions. - Matplotlib
Matplotlib is a comprehensive library to create interactive visualizations in Python. One of its API, pyplot is used to create interactive plots in a figure. The interactive plot shows the graph of penalty points improvement over the number of iterations in HGASA. The graph can be zoomed, panned, configured and saved as a figure. - PrettyTable
PrettyTable can be used to visualize tabular data in ASCII table format. It is used to draw the timetable for the presentation schedule.
The table below shows the experimental result of running HGASA algorithm using the given input files where there are n = 118 presentations, m = 300 slots and k = 47 supervisors.
| Experimental Run | Run 1 | Run 2 | Run 3 |
|---|---|---|---|
| Hard Constraints Violated | 0 | 0 | 0 |
| Soft Constraints Violated | 2 | 1 | 2 |
| Penalty Points | 255 | 245 | 245 |
| Runtime (seconds) | 62.60 | 55.75 | 54.02 |
🔓 Note that all the parameters used in HGASA algorithm are purely empirical and should be adjusted for other problems.
The generated presentation schedule is in csv format as shown below:
P9, null, null, P48, P36, null, ...
The fragmented schedule above indicates that P9, P48 and P36 are scheduled for slot 1, slot 4 and slot 5 respectively. null indicates no presentation is scheduled for a particular slot. An example of a timetable generated by PrettyTable that shows the final presentation schedule is shown below.
+------+------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| Day | Venue | 0900-0930 | 0930-1000 | 1000-1030 | 1030-1100 | 1100-1130 | 1130-1200 | 1200-1230 | 1230-1300 | 1400-1430 | 1430-1500 | 1500-1530 | 1530-1600 | 1600-1630 | 1630-1700 | 1700-1730 |
+------+------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| Mon | Viva Room | P58 | P35 | | P73 | P118 | | | | | | | | | | |
| | Meeting Room | P78 | P100 | P116 | | P40 | P3 | | | P92 | P50 | P75 | | | | |
| | Interaction Room | | P6 | | | | | | | P71 | P11 | | | | | |
| | BJIM | | | | | P57 | P24 | P2 | | | P25 | P88 | P81 | P96 | | |
| | | | | | | | | | | | | | | | | |
| Tues | Viva Room | P70 | P80 | | P39 | P99 | P23 | P98 | | | | P14 | | | | |
| | Meeting Room | | | P77 | P52 | P17 | | | | P105 | P62 | P61 | P18 | P9 | | |
| | Interaction Room | P84 | P91 | | | | P65 | | | | P5 | | P83 | P108 | P112 | |
| | BJIM | | P48 | P32 | P86 | P34 | P21 | | | | P33 | P117 | P37 | P38 | | |
| | | | | | | | | | | | | | | | | |
| Wed | Viva Room | | | | | | | | | | | | P95 | P51 | P106 | |
| | Meeting Room | | | | | P69 | P1 | P90 | | | | | | | | |
| | Interaction Room | | | | P103 | | | | | | | P19 | P15 | | | |
| | BJIM | P49 | P13 | P101 | | | | P41 | | | | | | | | |
| | | | | | | | | | | | | | | | | |
| Thu | Viva Room | P102 | P85 | P56 | P79 | P115 | P16 | | | | | | | | | |
| | Meeting Room | | | | P26 | P109 | P113 | | | | P68 | P82 | P46 | | | |
| | Interaction Room | | P60 | P114 | P36 | P10 | | | | P87 | P28 | P94 | P53 | P30 | | |
| | BJIM | | | P27 | P89 | P55 | | | | | | P31 | P76 | | | |
| | | | | | | | | | | | | | | | | |
| Fri | Viva Room | | | P44 | P63 | P67 | P7 | | | | | P20 | P47 | P22 | | |
| | Meeting Room | | | P72 | P110 | P64 | | | | | | | | P42 | P43 | |
| | Interaction Room | P29 | P93 | P97 | P111 | | P59 | | | | | P45 | P66 | P4 | | |
| | BJIM | | P8 | P74 | P54 | P107 | | P104 | | | | | | P12 | | |
| | | | | | | | | | | | | | | | | |
+------+------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
A graph of penalty points over number of iterations will be saved in png format. An example of the graph is shown below. The graph illustrates the improvement of presentation scheduling as number of iterations increases.
Graph of penalty points over number of iterations
Refer to write function in data.py for more details.
Windows commands for Python package installation:
NumPy$ pip install numpyNumba$ pip install numbaMatplotlib$ pip install matplotlibPrettyTable$ pip install PTable
There should be a folder named input_files in the same directory that contains all the csv files (SupExaAssign.csv, HC03.csv, HC04.csv, SC01.csv, SC02.csv and SC03.csv).
Run hybrid_system.py.
🔓 Modify data.py and input_files for other data formats, such as json or txt.
- Luke, S. (2015, October). Essentials of Metaheuristics. Retrieved June 8, 2020, from https://cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf
- Kohshori, M. S., & Abadeh, M. S. (2012, March). Hybrid Genetic Algorithms for University Course Timetabling. IJCSI International Journal of Computer Science, 9(2), 446-455.
- Evolutionary Timetable Scheduling in Python
- Nice Introduction to Genetic Algorithm on GitHub
- Algorithm for creating a school timetable on Stack Overflow
- International Timetabling Competition 2002 Results