Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route
Online supply chain management (OSCM) is the smart way to deal with the vast amounts of data that come in from customers in a disorganized system to meet the quantities, volumes, and types of customer packages during both delivery and pick-up phases using a new design of vehicle boxes managed by IoT...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023008721 |
| _version_ | 1827310644095877120 |
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| author | Ahmed M. Abed Ali AlArjani Laila f. Seddek Samia ElAttar |
| author_facet | Ahmed M. Abed Ali AlArjani Laila f. Seddek Samia ElAttar |
| author_sort | Ahmed M. Abed |
| collection | DOAJ |
| description | Online supply chain management (OSCM) is the smart way to deal with the vast amounts of data that come in from customers in a disorganized system to meet the quantities, volumes, and types of customer packages during both delivery and pick-up phases using a new design of vehicle boxes managed by IoT and to track their requests based on scheduling requests and sorting them to make a Hamiltonian route that guarantees the shortest travel distance. The OSCM framework consists of two sequential phases. 1st phase has four recruitment stages. The 1st stage discusses exploration resources (the relationship between the client and the vehicle) using IoT to receive customers' requests (Heijunka growth radius), then moves to exploration maturity to build a one-way Hamiltonian growth route direction. The 1st stage is based on tackling a Heijunka matrix fed through deep learning to classify the matrix into many conditional clusters according to customers' request forecasting and make the prediction value, which is the stop condition of cluster radius through next three stages. This study finds that XGboost outperforms Ada-boost by 14.352 % in the prediction stage. A heuristic rule based on NWBS enhances the FP-Growth algorithm over ECLAT by 7.648 % in the classification stage. Phase II is interested in reducing load and unloading activity time. This problem describes needing more than a different service at the same point (i.e., chaotic and unstable interaction leads to unstable delivery). Therefore, the online scheduling and tracking of the logistic routing using the IoT that Smart Lean Heijunka supports will enhance the SCM, increasing the visited points by 31.2 % and improving the profit by 41 %. |
| first_indexed | 2024-03-08T14:49:57Z |
| format | Article |
| id | doaj.art-4c5af68f659f4a41b74b7e5786688809 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T20:03:23Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-4c5af68f659f4a41b74b7e57866888092024-03-24T07:00:29ZengElsevierResults in Engineering2590-12302024-03-0121101745Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian routeAhmed M. Abed0Ali AlArjani1Laila f. Seddek2Samia ElAttar3Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, 16273, Saudi Arabia; Industrial Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, P.O. 44519, Egypt; Corresponding author. Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, 16273, Saudi Arabia.Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, 16273, Saudi ArabiaDepartment of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin AbdulAziz University, PO 11942, Saudi Arabia; Department of Engineering Mathematics and Physics, Faculty of Engineering, Zagazig University, Zagazig, 44519, EgyptDepartment of Industrial & Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Department of Industrial Engineering, Alexandria Higher Institute of Engineering and Technology (AIET), Alexandria 21311, EgyptOnline supply chain management (OSCM) is the smart way to deal with the vast amounts of data that come in from customers in a disorganized system to meet the quantities, volumes, and types of customer packages during both delivery and pick-up phases using a new design of vehicle boxes managed by IoT and to track their requests based on scheduling requests and sorting them to make a Hamiltonian route that guarantees the shortest travel distance. The OSCM framework consists of two sequential phases. 1st phase has four recruitment stages. The 1st stage discusses exploration resources (the relationship between the client and the vehicle) using IoT to receive customers' requests (Heijunka growth radius), then moves to exploration maturity to build a one-way Hamiltonian growth route direction. The 1st stage is based on tackling a Heijunka matrix fed through deep learning to classify the matrix into many conditional clusters according to customers' request forecasting and make the prediction value, which is the stop condition of cluster radius through next three stages. This study finds that XGboost outperforms Ada-boost by 14.352 % in the prediction stage. A heuristic rule based on NWBS enhances the FP-Growth algorithm over ECLAT by 7.648 % in the classification stage. Phase II is interested in reducing load and unloading activity time. This problem describes needing more than a different service at the same point (i.e., chaotic and unstable interaction leads to unstable delivery). Therefore, the online scheduling and tracking of the logistic routing using the IoT that Smart Lean Heijunka supports will enhance the SCM, increasing the visited points by 31.2 % and improving the profit by 41 %.http://www.sciencedirect.com/science/article/pii/S2590123023008721Chaotic systemIoTRecurrent learningSmart schedulingParcel deliveryOptimization |
| spellingShingle | Ahmed M. Abed Ali AlArjani Laila f. Seddek Samia ElAttar Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route Results in Engineering Chaotic system IoT Recurrent learning Smart scheduling Parcel delivery Optimization |
| title | Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route |
| title_full | Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route |
| title_fullStr | Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route |
| title_full_unstemmed | Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route |
| title_short | Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route |
| title_sort | reduce the delivery time and relevant costs in a chaotic requests system via lean heijunka model to enhance the logistic hamiltonian route |
| topic | Chaotic system IoT Recurrent learning Smart scheduling Parcel delivery Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023008721 |
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