Storage Allocation Algorithm with Multi-Objective Optimization in High-Density Tridimensional Warehouse
Storage allocation in a high-density tridimensional warehouse has more constraints and optimization objectives than that in a single or dual rack warehouse. As a result, algorithms for storage allocation in this kind of warehouse must be different. This paper mapped the storage allocation problem to...
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| Vydané v: | 2019 6th International Conference on Systems and Informatics (ICSAI) s. 195 - 201 |
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01.11.2019
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| Abstract | Storage allocation in a high-density tridimensional warehouse has more constraints and optimization objectives than that in a single or dual rack warehouse. As a result, algorithms for storage allocation in this kind of warehouse must be different. This paper mapped the storage allocation problem to the knapsack problem (KP) and introduced a dynamic programming (DP) algorithm to solve the problem. A penalty score strategy was defined for the DP algorithm, and the optimization objective is to minimize the penalty score while satisfying the constraints. A multi-index strategy was used for preprocess according to the constraints, and this strategy also helped to reduce the scale of DP algorithm. Finally, the simulation showed that the time complexity of DP algorithm was greatly reduced compared with depth-first-search based recursive algorithm. While compared with particle swarm optimization (PSO), DP algorithm can run faster in actual projects problem scale, and more importantly, DP can get the optimal solution, not a suboptimal one. |
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| AbstractList | Storage allocation in a high-density tridimensional warehouse has more constraints and optimization objectives than that in a single or dual rack warehouse. As a result, algorithms for storage allocation in this kind of warehouse must be different. This paper mapped the storage allocation problem to the knapsack problem (KP) and introduced a dynamic programming (DP) algorithm to solve the problem. A penalty score strategy was defined for the DP algorithm, and the optimization objective is to minimize the penalty score while satisfying the constraints. A multi-index strategy was used for preprocess according to the constraints, and this strategy also helped to reduce the scale of DP algorithm. Finally, the simulation showed that the time complexity of DP algorithm was greatly reduced compared with depth-first-search based recursive algorithm. While compared with particle swarm optimization (PSO), DP algorithm can run faster in actual projects problem scale, and more importantly, DP can get the optimal solution, not a suboptimal one. |
| Author | Huang, Qiubo Liu, Guohua Xie, Shuda |
| Author_xml | – sequence: 1 givenname: Qiubo surname: Huang fullname: Huang, Qiubo organization: School of Computer Science and Technology, Donghua University,Shanghai,China – sequence: 2 givenname: Shuda surname: Xie fullname: Xie, Shuda organization: School of Computer Science and Technology, Donghua University,Shanghai,China – sequence: 3 givenname: Guohua surname: Liu fullname: Liu, Guohua organization: School of Computer Science and Technology, Donghua University,Shanghai,China |
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| Snippet | Storage allocation in a high-density tridimensional warehouse has more constraints and optimization objectives than that in a single or dual rack warehouse. As... |
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| SubjectTerms | Computer science Heuristic algorithms high-density tridimensional warehouse Mathematical model multi-index multi-objective optimization Optimization Pallets penalty score strategy Resource management storage allocation Time complexity |
| Title | Storage Allocation Algorithm with Multi-Objective Optimization in High-Density Tridimensional Warehouse |
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