scholarly journals The Implementation of Genetic Algorithm in The Vehicle KIR Test Scheduling System

CCIT Journal ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 57-69
Author(s):  
Suprihadi Suprihadi ◽  
Rama Aufadha Risqullah
Keyword(s):  
Genetic Algorithm ◽  
Data Collection ◽  
Test Scheduling ◽  
Service Capacity ◽  
Time Schedule ◽  
Web Based ◽  
Scheduling System ◽  
Vehicle Owner ◽  
Scheduling Method ◽  
Working Day

Based on the results of observations and data collection, the vehicle parking test services at the Salatiga City Transportation Agency have not implemented a structured scheduling system in managing their service schedules. This has resulted in an accumulation of the number of vehicles that exceed the service capacity on a working day. Therefore, a scheduling system is needed to assist the agency in arranging the due time schedule for the KIR test of a vehicle, and helping the vehicle owner community to find out the due time for the next KIR test. The scheduling method used is a genetic algorithm that is applied to a web-based application. The application is built using the PHP language and MySql database. With this website-based scheduling system application, it has made it easier for service officers to manage the KIR test service schedule, especially if there is a change in the number of vehicles.

Test Scheduling Method Based on Cellular Genetic Algorithm for System on Chip

2010 ◽  
Vol 663-665 ◽  
pp. 670-673
Author(s):  
Zhong Liang Pan ◽  
Ling Chen
Keyword(s):  
Genetic Algorithm ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Scheduling Problem ◽  
Good Test ◽  
Cellular Genetic Algorithm ◽  
Scheduling Method ◽  
On Chip ◽  
Feasible Solutions

The main aspects for the test of system on chip (SoC) are designing testability architectures and solving the test scheduling. The test time of SoC can be reduced by using good test scheduling schemes. A test scheduling method based on cellular genetic algorithm is presented in this paper. In the method, the individuals are used to represent the feasible solutions of the test scheduling problem, the individuals are distributed over a grid or connected graph, the genetic operations such as selection and mutation are applied locally in some neighborhood of each individual. The test scheduling schemes are obtained by carrying out the evolutionary operations for the populations. A lot of experiments are performed for the SoC benchmark circuits, the experimental results show that the better test scheduling schemes can be obtained by the method in this paper.

Structural Validity and Reliability of The Urdu Translated Version of IPIP - NEO - 300 Web - Based Inventory

2020 ◽  
Author(s):  
Minhaaj Rehman ◽  
John Anthony Johnson
Keyword(s):  
Data Collection ◽  
Measurement Invariance ◽  
Internal Consistency ◽  
Ocean Model ◽  
Full Model ◽  
Validity And Reliability ◽  
Personality Tests ◽  
Personality Preferences ◽  
Web Based ◽  
Invariance Test

The NEO-IPIP-300 is a 300-item version scale of freely available personality tests based on the OCEAN Model of 30 distinctive personality traits. The scale measures human personality preferences and groups them into five distinct factors, namely Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The scale has been translated into many languages before, but there was no translation and norms available for the Urdu language.Paper reports the translation, creation of web version, data collection (N=869), and reliability of Urdu version of NEO-IPIP-300. We also did a CFA Analysis and Measurement Invariance test as part of the paper. Full measurement invariance was met for the full model, and partial measurement invariance was met for neuroticism (metric and scalar) and extraversion (metric). In general, all models fit well and suggest that the Urdu IPIP-300-NEO aligns well with the English IPIP-300-NEO. In some cases, the Urdu inventory performed better (e.g., higher internal consistency) than the English inventory.

Effect of Incentive Amount on U.S. Adolescents’ Participation in an Accelerometer Data Collection Component of a National Survey

Field Methods ◽  
2021 ◽  
pp. 1525822X2198984
Author(s):  
April Y. Oh ◽  
Andrew Caporaso ◽  
Terisa Davis ◽  
Laura A. Dwyer ◽  
Linda C. Nebeling ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
National Survey ◽  
Monetary Incentive ◽  
Behavioral Research ◽  
Accelerometer Data ◽  
Web Based ◽  
Demographic Subgroups

Behavioral research increasingly uses accelerometers to provide objective estimates of physical activity. This study extends research on methods for collecting accelerometer data among youth by examining whether the amount of a monetary incentive affects enrollment and compliance in a mail-based accelerometer study of adolescents. We invited a subset of adolescents in a national web-based study to wear an accelerometer for seven days and return it by mail; participants received either $20 or $40 for participating. Enrollment did not significantly differ by incentive amount. However, adolescents receiving the $40 incentive had significantly higher compliance (accelerometer wear and return). This difference was largely consistent across demographic subgroups. Those in the $40 group also wore the accelerometer for more time than the $20 group on the first two days of the study. Compared to $20, a $40 incentive may promote youth completion of mail-based accelerometer studies.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

Paired Comparisons or Sorting? Comparing Web-Based Methods for Collecting Similarity Data for Large Stimulus Sets for Destination Image Positioning

2016 ◽  
Vol 56 (4) ◽  
pp. 482-495
Author(s):  
Ilona Pezenka
Keyword(s):  
Data Collection ◽  
Rating Scale ◽  
Destination Image ◽  
Alternative Methods ◽  
Paired Comparisons ◽  
Large Stimulus ◽  
Web Based ◽  
Perceptual Maps ◽  
Perceived Difficulty ◽  
Collection Methods

Destination image is among the most studied constructs in tourism research. Many researchers are still convinced that the rating scale method is the most accurate for assessing destination image. This study presents alternative methods of data collection, namely, free-sorting and reduced paired comparisons, and investigates their applicability in a Web-based environment. The study then subjects these data collection methods to empirical analysis and compares the judgment task’s effects on perceived difficulty, fatigue, and boredom, on data quality, and on perceptual maps derived with MDS. The findings demonstrate that these methods are more accurate whenever a large number of objects have to be judged, which is particularly the case for positioning and competitiveness studies.

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