Inventory classification by multiple objective particles swarm optimization
Inventory classification is one of important techniques in inventory control context. Managers have to classify inventories because of their variety and high volume. So a stream of research has been to attempt to find methods that increase the management control by determining the number of inven...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
Allameh Tabataba'i University Press
2014-07-01
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| Series: | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| Subjects: | |
| Online Access: | http://jims.atu.ac.ir/article_173_0cc51711bb8e2af307fe223bd97a3272.pdf |
| Summary: | Inventory classification is one of important techniques in inventory control
context. Managers have to classify inventories because of their variety and
high volume. So a stream of research has been to attempt to find methods
that increase the management control by determining the number of inventory
classes. In this paper the multiple objective particle swarm optimization
algorithm has been used. This algorithm has been presented by Chi-Yang Tsai
and Szu-Wei Yeh in 2008. Multiple objective particle swarm optimization algorithm
is an evolutionary algorithm that enables the management to optimize
multiple objectives simultaneously. Minimizing costs of inventory holding
and ordering and maximizing inventory turnover ratios are this model’s objectives.
We write the software program of this model and then test it on a sample
of 100 items. Results show that this algorithm can decrease costs of holding &
ordering and also increase the inventory turnover ratios significantly. |
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| ISSN: | 2251-8029 |