Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times
This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/23/11210 |
| _version_ | 1827675140631035904 |
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| author | Mohammed Alnahhal Diane Ahrens Bashir Salah |
| author_facet | Mohammed Alnahhal Diane Ahrens Bashir Salah |
| author_sort | Mohammed Alnahhal |
| collection | DOAJ |
| description | This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries. |
| first_indexed | 2024-03-10T04:56:59Z |
| format | Article |
| id | doaj.art-28c12365726c4727b8130b99ef5b5251 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:56:59Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-28c12365726c4727b8130b99ef5b52512023-11-23T02:04:21ZengMDPI AGApplied Sciences2076-34172021-11-0111231121010.3390/app112311210Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery TimesMohammed Alnahhal0Diane Ahrens1Bashir Salah2Mechanical and Industrial Engineering Department, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 10021, United Arab EmiratesTechnology Campus Grafenau, Deggendorf Institute of Technology, 94469 Deggendorf, GermanyIndustrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaThis study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.https://www.mdpi.com/2076-3417/11/23/11210inventory replenishmentmixed-integer programmingdynamic programminginventory holding costssoft drinks industry |
| spellingShingle | Mohammed Alnahhal Diane Ahrens Bashir Salah Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times Applied Sciences inventory replenishment mixed-integer programming dynamic programming inventory holding costs soft drinks industry |
| title | Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times |
| title_full | Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times |
| title_fullStr | Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times |
| title_full_unstemmed | Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times |
| title_short | Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times |
| title_sort | optimizing inventory replenishment for seasonal demand with discrete delivery times |
| topic | inventory replenishment mixed-integer programming dynamic programming inventory holding costs soft drinks industry |
| url | https://www.mdpi.com/2076-3417/11/23/11210 |
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