Multi-store collaborative delivery optimization based on Top-K order-split
Regarding the fulfillment optimization of online retail orders, many researchers focus more on warehouse optimization and distribution center optimization. However, under the background of new retailing, traditional retailers carry out online services, forming an order fulfillment model with physica...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997996/?tool=EBI |
| _version_ | 1811154350842576896 |
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| author | Yanju Zhang Liping Ou Jiaxu Liu |
| author_facet | Yanju Zhang Liping Ou Jiaxu Liu |
| author_sort | Yanju Zhang |
| collection | DOAJ |
| description | Regarding the fulfillment optimization of online retail orders, many researchers focus more on warehouse optimization and distribution center optimization. However, under the background of new retailing, traditional retailers carry out online services, forming an order fulfillment model with physical stores as front warehouses. Studies that focus on physical stores and consider both order splitting and store delivery are rare, which cannot meet the order optimization needs of traditional retailers. To this end, this study proposes a new problem called the “Multi-Store Collaborative Delivery Optimization (MCDO)”, in which not only make the order-split plans for stores but also design the order-delivery routes for them, such that the order fulfillment cost is minimized. To solve the problem, a Top-K breadth-first search and a local search are integrated to construct a hybrid heuristic algorithm, named “Top-K Recommendation & Improved Local Search (TKILS)”. This study optimizes the search efficiency of the breadth-first search by controlling the number of sub-orders and improving the initial solution of the local search using a greedy cost function. Then achieve the joint optimization of order-split and order-delivery by improving the local optimization operators. Finally, extensive experiments on synthetic and real datasets validate the effectiveness and applicability of the algorithm this study proposed. |
| first_indexed | 2024-04-10T04:14:10Z |
| format | Article |
| id | doaj.art-c320778876d44d89ba11bd2ecf52f913 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T04:14:10Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c320778876d44d89ba11bd2ecf52f9132023-03-12T05:32:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01183Multi-store collaborative delivery optimization based on Top-K order-splitYanju ZhangLiping OuJiaxu LiuRegarding the fulfillment optimization of online retail orders, many researchers focus more on warehouse optimization and distribution center optimization. However, under the background of new retailing, traditional retailers carry out online services, forming an order fulfillment model with physical stores as front warehouses. Studies that focus on physical stores and consider both order splitting and store delivery are rare, which cannot meet the order optimization needs of traditional retailers. To this end, this study proposes a new problem called the “Multi-Store Collaborative Delivery Optimization (MCDO)”, in which not only make the order-split plans for stores but also design the order-delivery routes for them, such that the order fulfillment cost is minimized. To solve the problem, a Top-K breadth-first search and a local search are integrated to construct a hybrid heuristic algorithm, named “Top-K Recommendation & Improved Local Search (TKILS)”. This study optimizes the search efficiency of the breadth-first search by controlling the number of sub-orders and improving the initial solution of the local search using a greedy cost function. Then achieve the joint optimization of order-split and order-delivery by improving the local optimization operators. Finally, extensive experiments on synthetic and real datasets validate the effectiveness and applicability of the algorithm this study proposed.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997996/?tool=EBI |
| spellingShingle | Yanju Zhang Liping Ou Jiaxu Liu Multi-store collaborative delivery optimization based on Top-K order-split PLoS ONE |
| title | Multi-store collaborative delivery optimization based on Top-K order-split |
| title_full | Multi-store collaborative delivery optimization based on Top-K order-split |
| title_fullStr | Multi-store collaborative delivery optimization based on Top-K order-split |
| title_full_unstemmed | Multi-store collaborative delivery optimization based on Top-K order-split |
| title_short | Multi-store collaborative delivery optimization based on Top-K order-split |
| title_sort | multi store collaborative delivery optimization based on top k order split |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997996/?tool=EBI |
| work_keys_str_mv | AT yanjuzhang multistorecollaborativedeliveryoptimizationbasedontopkordersplit AT lipingou multistorecollaborativedeliveryoptimizationbasedontopkordersplit AT jiaxuliu multistorecollaborativedeliveryoptimizationbasedontopkordersplit |