Optimizing workforce reliability using shift scheduling

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AKBARI MOHAMMAD

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Journal Paper

Paper Information

Journal: JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES Year:2019 | Volume:17 | Issue:54 Page(s): 263-290

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Title

Optimizing workforce reliability using shift scheduling

Author(s)

AKBARI MOHAMMAD | Issue Writer Certificate

Keywords

shift schedulingQ1

human factorsQ2

human errorQ1

workforce reliabilityQ1

Abstract

shift scheduling is one of the production planning processes that develop organization and workforces by providing optimized time table. In this study we tried to present mathematical model with minimization function of human errors regarding human factor engineering. Learning, forgetting, fatigue and rest are important factors which increase or decrease human errors and is modeled here. Provided model is nonlinear integer model. To investigate model and study human factors we solved small instances with different parameters in three categories: easy tasks, medium and hard tasks. To solve model we used LINGO software. Results indicated that shift schedules are different regarding different human parameters. With increasing difficulty of tasks and decreasing learning, rest breaks were closer to start of working shift. With decreasing difficulty of tasks and increasing learning, optimized schedule close to schedule without rest breaks. Also results showed that we can use the model to optimize human reliability, and organizations can define optimized shift schedules with considering task types and human parameters.

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APA: Copy

AKBARI, MOHAMMAD. (2019). Optimizing workforce reliability using shift scheduling. JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES, 17(54 ), 263-290. SID. https://sid.ir/paper/213193/en

Vancouver: Copy

AKBARI MOHAMMAD. Optimizing workforce reliability using shift scheduling. JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES[Internet]. 2019;17(54 ):263-290. Available from: https://sid.ir/paper/213193/en

IEEE: Copy

MOHAMMAD AKBARI, “Optimizing workforce reliability using shift scheduling,” JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES, vol. 17, no. 54 , pp. 263–290, 2019, [Online]. Available: https://sid.ir/paper/213193/en

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