Optimization for Sustainable Train Shunting Services Using DMAIC Cycle
Mohammed Alshamlan,
Aos Alzahrani,
Musaad BinYousef,
Abdelhakim Abdelhadi,
Khaled Akkad and
Rakan Alyamani
Additional contact information
Mohammed Alshamlan: Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
Aos Alzahrani: Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
Musaad BinYousef: Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
Abdelhakim Abdelhadi: Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
Khaled Akkad: Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
Rakan Alyamani: Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
Sustainability, 2022, vol. 14, issue 3, 1-13
Abstract:
Railway operations is a complicated field that requires a specialized team to operate trains safely and effectively. The focus of this paper was on a Middle Eastern-based railway company to understand the current train shunting process at the north–south passenger line and investigate possible issues in order to resolve them. The aim of this paper is to go through the DMAIC cycle and use it to define, measure, analyze and recommend improvements to enhance and control the current process. The results show that the bottleneck process can be greatly improved and optimized by fixing the root causes identified by the cause and effect diagram, which should positively contribute to increasing the overall performance of train operations and provide more time to maintain the trains.
Keywords: DMAIC; railway; process optimization; public transport; industrial pollution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:3:p:1719-:d:740704
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