An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem
Abstract In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving prac...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84663-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559737489948672 |
|---|---|
| author | Zhenzhong Zhang Ling Zhang Weichun Li |
| author_facet | Zhenzhong Zhang Ling Zhang Weichun Li |
| author_sort | Zhenzhong Zhang |
| collection | DOAJ |
| description | Abstract In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving practical allocation problems. This study focuses on the problem of allocating orders to parallel machines with varying efficiencies under uncertain and high-dimensional conditions. To maximize the expected profit of order processing, a mathematical model for a high-dimensional stochastic optimization problem is developed, considering the uncertainty due to potential customer order cancellations in a real-world production. By integrating an intelligent optimization algorithm for the order assignment problem with a scenario generation approach, a novel framework for intelligent stochastic optimization is proposed. This framework employs an intelligent optimization algorithm suitable for the generalized assignment problem to search for improved solutions and utilizes the scenario generation method to produce the necessary scenarios for evaluating solutions in high-dimension. Experimental results demonstrate that the proposed approach effectively addresses the high-dimensional stochastic order allocation problem, outperforming the compared method in terms of efficiency and capability. |
| format | Article |
| id | doaj-art-63b3b2bd9d414b0d95698cfd6436e923 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-63b3b2bd9d414b0d95698cfd6436e9232025-01-05T12:13:32ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84663-yAn improved adaptive variable neighborhood search algorithm for stochastic order allocation problemZhenzhong Zhang0Ling Zhang1Weichun Li2CAAC Academy, Civil Aviation Flight University of ChinaSchool of Modern Posts, Chongqing University of Posts and TelecommunicationsCAAC Academy, Civil Aviation Flight University of ChinaAbstract In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving practical allocation problems. This study focuses on the problem of allocating orders to parallel machines with varying efficiencies under uncertain and high-dimensional conditions. To maximize the expected profit of order processing, a mathematical model for a high-dimensional stochastic optimization problem is developed, considering the uncertainty due to potential customer order cancellations in a real-world production. By integrating an intelligent optimization algorithm for the order assignment problem with a scenario generation approach, a novel framework for intelligent stochastic optimization is proposed. This framework employs an intelligent optimization algorithm suitable for the generalized assignment problem to search for improved solutions and utilizes the scenario generation method to produce the necessary scenarios for evaluating solutions in high-dimension. Experimental results demonstrate that the proposed approach effectively addresses the high-dimensional stochastic order allocation problem, outperforming the compared method in terms of efficiency and capability.https://doi.org/10.1038/s41598-024-84663-yOrder uncertaintyOrder allocationScenario generationStochastic optimization algorithm |
| spellingShingle | Zhenzhong Zhang Ling Zhang Weichun Li An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem Scientific Reports Order uncertainty Order allocation Scenario generation Stochastic optimization algorithm |
| title | An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem |
| title_full | An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem |
| title_fullStr | An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem |
| title_full_unstemmed | An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem |
| title_short | An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem |
| title_sort | improved adaptive variable neighborhood search algorithm for stochastic order allocation problem |
| topic | Order uncertainty Order allocation Scenario generation Stochastic optimization algorithm |
| url | https://doi.org/10.1038/s41598-024-84663-y |
| work_keys_str_mv | AT zhenzhongzhang animprovedadaptivevariableneighborhoodsearchalgorithmforstochasticorderallocationproblem AT lingzhang animprovedadaptivevariableneighborhoodsearchalgorithmforstochasticorderallocationproblem AT weichunli animprovedadaptivevariableneighborhoodsearchalgorithmforstochasticorderallocationproblem AT zhenzhongzhang improvedadaptivevariableneighborhoodsearchalgorithmforstochasticorderallocationproblem AT lingzhang improvedadaptivevariableneighborhoodsearchalgorithmforstochasticorderallocationproblem AT weichunli improvedadaptivevariableneighborhoodsearchalgorithmforstochasticorderallocationproblem |