Finding Effective Item Assignment Plans with Weighted Item Associations Using A Hybrid Genetic Algorithm
| Autor: | Kwang Il Ahn, Kichun Lee, Minho Ryu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Association rule learning Computer science Association (object-oriented programming) Crossover hybrid genetic algorithm 02 engineering and technology computer.software_genre lcsh:Technology association rules lcsh:Chemistry 020901 industrial engineering & automation Operator (computer programming) item assignment Genetic algorithm 0202 electrical engineering electronic engineering information engineering General Materials Science lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes cross-selling lcsh:T Process Chemistry and Technology General Engineering lcsh:QC1-999 Tabu search Purchasing Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Benchmark (computing) 020201 artificial intelligence & image processing Data mining lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences; Volume 11; Issue 5; Pages: 2209 Applied Sciences, Vol 11, Iss 2209, p 2209 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11052209 |
| Popis: | By identifying useful relationships between massive datasets, association rule mining can provide new insights to decision-makers. Item assignment models based on association between items are used to place items in a retail or e-commerce environment to increase sales. However, existing models fail to combine these associations with item-specific information, such as profit and purchasing frequency. To find effective assignments with item-specific information, we propose a new hybrid genetic algorithm that incorporates a robust tabu search with a novel rectangular partially matched crossover, focusing on rectangular layouts. Interestingly, we show that our item assignment model is equivalent to popular quadratic assignment NP-hard problems. We show the effectiveness of the proposed algorithm, using benchmark instances from QAPLIB and synthetic databases that represent real-life retail situations, and compare our algorithm with other existing algorithms. We also show that the proposed crossover operator outperforms a few existing ones in both fitness values and search times. The experimental results show that not only does the proposed item assignment model generates a more profitable assignment plan than the other tested models based on association alone but it also obtains better solutions than the other tested algorithms. |
| Databáze: | OpenAIRE |
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