scholarly journals Two Stages Best First Search Algorithm Using Hard and Soft Constraints Heuristic for Course Timetabling

2020 ◽  
Vol 34 (4) ◽  
pp. 413-418
Author(s):  
Marvin Chandra Wijaya
Keyword(s):  
Search Algorithm ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Best First Search ◽  
Hard And Soft Constraints ◽  
Two Stages

scholarly journals Sequential Integer Programming for Solving Curriculum-Based University Course Timetabling Problem

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1455-1462 ◽  
Keyword(s):  
Integer Programming ◽  
Mathematical Formulation ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Second Stage ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Course Timetabling Problem ◽  
Two Stages ◽  
University Course

This research propose two stages sequential integer programming (IP) approach for solving curriculum-based university course timetabling problems (CB-UTT) in University Malaysia Sabah, Labuan international campus (UMSLIC). Like other timetabling problems, CB-UTT in UMSLIC has its own rules and features. The problem involves several hard constraints which need to be fully satisfied and soft constraints which satisfaction are very highly desirable. In this research mathematical formulation and two stages sequential IP search methodology based on UMSLIC is proposed. The IP search methodology is tested over two real-world instances, semester 1, session 2016/2017 and semester 2, session 2016/2017. The objective of this research is to generate high quality feasible CB-UTT which satisfies all peoples affected by the timetable. The results show that, the IP formulation proposed in this research is able to produce feasible solution in the first stage, and further improve by 10.99% and 8.92% respectively by solving soft constraints in the second stage without violating any hard constraints solved in the first stage. This IP approach is applicable towards the CB-UTT in UMSLIC

scholarly journals An Intelligent Technique for Solving Timetable Problem

2021 ◽  
Vol 23 (04) ◽  
pp. 317-327
Author(s):  
Abdalla El-Dhshan ◽  
◽  
Hegazy Zaher ◽  
Naglaa Ragaa ◽  
◽  
...  
Keyword(s):  
Search Algorithm ◽  
Optimization Techniques ◽  
Separate Experiment ◽  
Soft Constraints ◽  
Intelligent Technique ◽  
Hard Constraints ◽  
Intelligent Behavior ◽  
Best Parameter ◽  
Hard And Soft Constraints ◽  
Timetable Problem

Timetabling problem is complex combinatorial resources allocation problems. There are two hard and soft constraints to be satisfied. The timetable is feasible if all hard constraints are satisfied. Besides, satisfying more of the soft constraints produces a high-quality timetable. Crow Search Algorithm (CSA) as an intelligence technique presents for solving timetable problem. CSA like all meta-heuristic optimization techniques is a nature-inspire of intelligent behavior of crows. The proposed CSA tested using the well-known benchmark of hard timetabling datasets (hdtt). Taguchi’s method used to tune the best parameter combinations for the factors and levels. The tuned parameters of CSA are applied on datasets in separate experiment. The results show that the proposed CSA is superior to generate solutions in reasonable CPU time when compared with other literature techniques.

scholarly journals Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ahmad Miftah Fajrin ◽  
Chastine Fatichah
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Local Optimum ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Fitness Value ◽  
Hard And Soft Constraints ◽  
The Right ◽  
Course Timetabling Problem

A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

scholarly journals Answer set programming as a modeling language for course timetabling

2013 ◽  
Vol 13 (4-5) ◽  
pp. 783-798 ◽  
Author(s):  
MUTSUNORI BANBARA ◽  
TAKEHIDE SOH ◽  
NAOYUKI TAMURA ◽  
KATSUMI INOUE ◽  
TORSTEN SCHAUB
Keyword(s):  
The Body ◽  
Modeling Language ◽  
Soft Constraints ◽  
Penalty Cost ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Problem Instances ◽  
Hard And Soft Constraints ◽  
Course Timetabling Problem ◽  
Effective Use

AbstractThe course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language for course timetabling is required to be expressive enough to specify a wide variety of soft constraints and objective functions. Furthermore, the resulting encoding is required to be extensible for capturing new constraints and for switching them between hard and soft, and to be flexible enough to deal with different formulations. In this paper, we propose to make effective use of ASP as a modeling language for course timetabling. We show that our ASP-based approach can naturally satisfy the above requirements, through an ASP encoding of the curriculum-based course timetabling problem proposed in the third track of the second international timetabling competition (ITC-2007). Our encoding is compact and human-readable, since each constraint is individually expressed by either one or two rules. Each hard constraint is expressed by using integrity constraints and aggregates of ASP. Each soft constraint S is expressed by rules in which the head is the form of penalty(S,V,C), and a violation V and its penalty cost C are detected and calculated respectively in the body. We carried out experiments on four different benchmark sets with five different formulations. We succeeded either in improving the bounds or producing the same bounds for many combinations of problem instances and formulations, compared with the previous best known bounds.

Parallel Scatter Search Approach for the MinMax Regret Location Problem

2018 ◽  
Vol 9 (2) ◽  
pp. 1-17
Author(s):  
Sarah Ibri ◽  
Mohammed EL Amin Cherabrab ◽  
Nasreddine Abdoune
Keyword(s):  
Location Problem ◽  
Search Algorithm ◽  
Discrete Event ◽  
Scatter Search ◽  
Search Time ◽  
Computation Time ◽  
Minmax Regret ◽  
Search Approach ◽  
Two Stages

In this paper we propose an efficient solving method based on a parallel scatter search algorithm that accelerates the search time to solve the minmax regret location problem. The algorithm was applied in the context of emergency management to locate emergency vehicles stations. A discrete event simulator was used to test the quality of the obtained solutions on the operational level. We compared the performance of the algorithm to an existing two stages method, and experiments show the efficiency of the proposed method in terms of quality of solution as well as the gain in computation time that could be obtained by parallelizing the proposed algorithm.

Application of best first search algorithm to demand control

2016 ◽  
Author(s):  
Kashale Chimanga ◽  
Josephat Kalezhi ◽  
Phillimon Mumba
Keyword(s):  
Search Algorithm ◽  
Best First Search ◽  
Demand Control

A multi-objective approach to nurse scheduling with both hard and soft constraints

1996 ◽  
Vol 30 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Ilham Berrada ◽  
Jacques A. Ferland ◽  
Philippe Michelon
Keyword(s):  
Soft Constraints ◽  
Nurse Scheduling ◽  
Multi Objective ◽  
Hard And Soft Constraints

scholarly journals Accelerating backtrack search with a best-first-search strategy

2014 ◽  
Vol 24 (4) ◽  
pp. 901-916
Author(s):  
Zoltán Ádám Mann ◽  
Tamás Szép
Keyword(s):  
Search Strategy ◽  
Search Algorithm ◽  
Search Space ◽  
New Approach ◽  
Backtrack Search ◽  
Speed Up ◽  
Hard Problems ◽  
Best First Search ◽  
Problem Instances ◽  
Np Hard Problems

Abstract Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because “fortunate” branching decisions can lead to finding a solution quickly, whereas “unfortunate” decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.

Design and statistical analysis of a hybrid local search algorithm for course timetabling

2011 ◽  
Vol 15 (1) ◽  
pp. 49-61 ◽  
Author(s):  
Ruggero Bellio ◽  
Luca Di Gaspero ◽  
Andrea Schaerf
Keyword(s):  
Statistical Analysis ◽  
Local Search ◽  
Search Algorithm ◽  
Local Search Algorithm ◽  
Course Timetabling
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