A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations
| dc.contributor.author | Li, Yue | |
| dc.contributor.author | Mendez-Mediavilla, Francis A. | |
| dc.contributor.author | Temponi, Cecilia | |
| dc.contributor.author | Kim, Junwoo | |
| dc.contributor.author | Jimenez, Jesus | |
| dc.date.accessioned | 2021-07-30T18:24:10Z | |
| dc.date.available | 2021-07-30T18:24:10Z | |
| dc.date.issued | 2021-02-19 | |
| dc.description.abstract | Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders. | |
| dc.description.abstract | Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders. | |
| dc.description.department | Business Administration | |
| dc.description.department | Engineering | |
| dc.description.department | Business Administration | |
| dc.description.department | Engineering | |
| dc.format | Text | |
| dc.format | Text | |
| dc.format.extent | 15 pages | |
| dc.format.extent | 15 pages | |
| dc.format.medium | 1 file (.pdf) | |
| dc.format.medium | 1 file (.pdf) | |
| dc.identifier.citation | Li, Y., Méndez-Mediavilla, F. A., Temponi, C., Kim, J., & Jimenez, J. A. (2021). A heuristic storage location assignment based on frequent itemset classes to improve order picking operations. Applied Sciences, 11(4), 1839. | |
| dc.identifier.citation | Li, Y., Méndez-Mediavilla, F. A., Temponi, C., Kim, J., & Jimenez, J. A. (2021). A heuristic storage location assignment based on frequent itemset classes to improve order picking operations. Applied Sciences, 11(4), 1839. | |
| dc.identifier.doi | https://doi.org/10.3390/app11041839 | |
| dc.identifier.doi | https://doi.org/10.3390/app11041839 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | https://hdl.handle.net/10877/14142 | |
| dc.language.iso | en | |
| dc.language.iso | en | |
| dc.publisher | Multidisciplinary Digital Publishing Institute | |
| dc.publisher | Multidisciplinary Digital Publishing Institute | |
| dc.rights.holder | © 2021 The Authors. | |
| dc.rights.license | This work is licensed under a Creative Commons Attribution 4.0 International License. | |
| dc.rights.license | This work is licensed under a Creative Commons Attribution 4.0 International License. | |
| dc.source | Applied Sciences, 2021, Vol. 11, No. 4, Article 1839. | |
| dc.source | Applied Sciences, 2021, Vol. 11, No. 4, Article 1839. | |
| dc.subject | Business Administration | |
| dc.subject | Engineering | |
| dc.subject | distribution centers | |
| dc.subject | order picking | |
| dc.subject | data mining | |
| dc.subject | association rule mining | |
| dc.subject | distribution centers | |
| dc.subject | order picking | |
| dc.subject | data mining | |
| dc.subject | association rule mining | |
| dc.title | A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations | |
| dc.title | A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations | |
| dc.type | Article | |
| dc.type | Article |