NDLI: GA and ICA approaches to job rotation scheduling problem: considering employee’s boredom

Content Provider	Springer Nature Link
Author	Ayough, Ashkan Zandieh, M. Farsijani, H.
Copyright Year	2011
Abstract	This paper presents a new model dealing with the job rotation scheduling problem, which is less studied, focusing on human characteristics such as boredom. Existing literature on conceptualizing boredom shows that researchers evaluate boredom in terms of exposure to the same tasks. We developed it to “exposure to similar tasks” and defined its functionality based on the need of assigning jobs with more similarity to each worker in the smallest period of planning which lowers the external interruption effect on worker’s concentration. To address the imbalance between number of jobs and that of workers in many industrial settings, we developed a multi-period imbalance assignment model. The proposed model is to rotate workers during a given planning horizon such that the total cost including assignment and boring cost will be minimized. The applicability of the model is described by presenting some real cases and validated through solving several randomly produced test problems by using Lingo software. Two search algorithms, genetic algorithm (GA) and imperialist competitive algorithm (ICA), designed to conquer the algorithmic complexity of model and their parameters adjusted using Taguchi’s method were used. The efficiency of algorithms is shown, comparing it with Lingo computation times, and it is shown that ICA solutions have better quality than GA solutions as well.
Starting Page	651
Ending Page	666
Page Count	16
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	60
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-11-22
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Job rotation scheduling Functional and dysfunctional boredom Nonlinear integer programming Genetic algorithm Imperialist competitive algorithm Taguchi’s design of experiments Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics/Supply Chain Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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