scholarly journals Courses timetabling based on hill climbing algorithm

2020 ◽  
Vol 10 (6) ◽  
pp. 6558
Author(s):  
Abdoul Rjoub
Keyword(s):  
Optimal Solution ◽  
Initial Solution ◽  
Hill Climbing ◽  
Soft Constraints ◽  
Hill Climbing Algorithm ◽  
Hard Constraints ◽  
Side Constraints ◽  
Time Requirements ◽  
Hard And Soft Constraints ◽  
Hill Climbing Algorithms

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

scholarly journals Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2177
Author(s):  
Jun-Ho Huh ◽  
Jimin Hwa ◽  
Yeong-Seok Seo
Keyword(s):  
Genetic Algorithm ◽  
Software Architecture ◽  
Large Scale ◽  
Structural Characteristics ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Software Systems ◽  
Small Scale ◽  
Hill Climbing Algorithm ◽  
Architecture Management

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

scholarly journals University Course Timetabling using Bayesian based Optimization Algorithm

2018 ◽  
Vol 6 (2) ◽  
pp. 14
Author(s):  
Alinaswe Siame ◽  
Douglas Kunda
Keyword(s):  
Mathematical Optimization ◽  
Soft Constraints ◽  
Simulation Research ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Time Simulation ◽  
Feasible Solutions ◽  
Hard And Soft Constraints ◽  
University Course

<p>The timetabling problem has traditionally been treated as a mathematical optimization, heuristic, or human-machine interactive problem. The timetabling problem comprises hard and soft constraints. Hard constraints must be satisfied in order to generate feasible solutions. Soft constraints are sometimes referred to as preferences that can be contravened if necessary. In this research, we present is as both a mathematical and a human-machine problem that requires acceptable and controlled human input, then the algorithm gives options available without conflicting the hard constraints. In short, this research allows the human agents to address the soft-constraints as the algorithm works on the hard constraints, as well as the algorithm being able to learn the soft constraints over time. Simulation research was used to investigate the timetabling problem. Our proposed model employs the use a naïve Bayesian Algorithm, to learn preferred days and timings by lecturers and use them to resolve the soft constraints.  </p>

Business Service Scheduling

2010 ◽  
Vol 1 (4) ◽  
pp. 52-63
Author(s):  
Jürgen Dorn
Keyword(s):  
Optimal Solution ◽  
Service Level ◽  
Service Process ◽  
Soft Constraints ◽  
Scheduling Problem ◽  
Business Service ◽  
Business Services ◽  
Service Scheduling ◽  
Production Processes ◽  
Hard Constraints

The management and predictive planning of the processes to create business services is more difficult than the planning of production processes, because services cannot be produced in stock and customers are involved in their creation. In this paper, the author proposes a method for service scheduling and optimization based on an ontology to describe business services and related concepts. The author schedules operations required to create a service. With each service process and its operations, soft and hard constraints on the execution of operations and the required resources are posted. These constraints are derived from service level agreements. A legal plan must then satisfy all hard constraints. All soft constraints are matter of optimization. Using a tabu search, a near-optimal solution of the service scheduling problem is achieved.

scholarly journals Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wu ◽  
Arunita Jaekel ◽  
Ataul Bari ◽  
Alioune Ngom
Keyword(s):  
Cellular Networks ◽  
Channel Assignment ◽  
Network Performance ◽  
Resonance Condition ◽  
Soft Constraints ◽  
Hard Constraints ◽  
Call Dropping ◽  
Incoming Call ◽  
Hard And Soft Constraints ◽  
Assignment Scheme

In cellular networks, it is important to determine an optimal channel assignment scheme so that the available channels, which are considered as “limited” resources in cellular networks, are used as efficiently as possible. The objective of the channel assignment scheme is to minimize thecall-blockingand thecall-droppingprobabilities. In this paper, we present two efficient integer linear programming (ILP) formulations, foroptimallyallocating a channel (from a pool of available channels) to an incoming call such that both “hard” and “soft” constraints are satisfied. Our first formulation, ILP1, does not allow channel reassignment of the existing calls, while our second formulation, ILP2, allows such reassignment. Both formulations can handle hard constraints, which includesco-siteandadjacent channelconstraints, in addition to the standardco-channelconstraints. The simplified problem (with only co-channel constraints) can be treated as a special case of our formulation. In addition to the hard constraints, we also consider soft constraints, such as, thepacking condition, resonance condition,andlimiting rearrangements, to further improve the network performance. We present the simulation results on a benchmark 49 cell environment with 70 channels that validate the performance of our approach.

scholarly journals Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

2020 ◽  
Author(s):  
Vinicius Francisco Rofatto ◽  
Marcelo Tomio Matsuoka ◽  
Ivandro Klein ◽  
Mauricio Roberto Veronez ◽  
Luiz Gonzaga Da Silveira, Jr.
Keyword(s):  
Geodetic Network ◽  
Model Parameters ◽  
Soft Constraints ◽  
Test Statistics ◽  
Elimination Procedure ◽  
Outlier Test ◽  
Hard Constraints ◽  
Absolute Correlation ◽  
Hard And Soft Constraints ◽  
Detectable Bias

In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

scholarly journals Effects of Hard and Soft Equality Constraints on Reliability Analysis

2020 ◽  
Author(s):  
Vinicius Francisco Rofatto ◽  
Marcelo Tomio Matsuoka ◽  
Ivandro Klein ◽  
Mauricio Roberto Veronez ◽  
Luiz Gonzaga Da Silveira, Jr.
Keyword(s):  
Reliability Analysis ◽  
Normal Operation ◽  
Model Parameters ◽  
Soft Constraints ◽  
Optical Investigation ◽  
Functional Relations ◽  
Hard Constraints ◽  
Detection And Identification ◽  
Set Up ◽  
Hard And Soft Constraints

The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.

scholarly journals Development of a Land Transportation Recommendation System Using the Hill Climbing Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 7
Author(s):  
Eka Surya Aditya ◽  
Wikan Danar Sunindyo
Keyword(s):  
Public Transportation ◽  
Recommendation System ◽  
Hill Climbing ◽  
The Public ◽  
Hill Climbing Algorithm ◽  
Time Space ◽  
The Right ◽  
Available Information ◽  
Hill Climbing Algorithms ◽  
The Hill

Communities in big cities often encounter problems in using public transportation due to difficulties in accessing available information. The information is not well integrated and scattered in various places. For this reason, an information and recommendation system is needed to facilitate the public in choosing the right mode of land transportation. The recommendation system can be built using the Hill Climbing algorithm. In this paper, I explain the development of a public land transportation recommendation system using three types of Hill Climbing Algorithms. The results of the recommendations are analyzed based on the complexity of asymptotic time, space complexity, and the quality of the results.

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.

Empirical Evaluation of Hill Climbing Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 27-40 ◽  
Author(s):  
Manju Khari ◽  
Prabhat Kumar
Keyword(s):  
Collective Behavior ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Research Community ◽  
Hill Climbing ◽  
Test Cases ◽  
Average Percentage ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Better Than

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

A New Neighborhood Function for Discrete Manufacturing Process Design Optimization Using Generalized Hill Climbing Algorithms

2000 ◽  
Vol 122 (2) ◽  
pp. 164-171 ◽  
Author(s):  
Diane E. Vaughan ◽  
Sheldon H. Jacobson ◽  
Derek E. Armstrong
Keyword(s):  
Design Optimization ◽  
Air Force ◽  
Process Design ◽  
Manufacturing Process ◽  
Input Parameter ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Neighborhood Function ◽  
Discrete Manufacturing ◽  
Hill Climbing Algorithms

Discrete manufacturing process design optimization can be difficult, due to the large number of manufacturing process design sequences and associated input parameter setting combinations that exist. Generalized hill climbing algorithms have been introduced to address such manufacturing design problems. Initial results with generalized hill climbing algorithms required the manufacturing process design sequence to be fixed, with the generalized hill climbing algorithm used to identify optimal input parameter settings. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing algorithm to simultaneously optimize over both the design sequences and the inputs parameters. Computational results are reported with an integrated blade rotor discrete manufacturing process design problem under study at the Materials Process Design Branch of the Air Force Research Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA). [S1050-0472(00)01002-3]

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