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An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

Wang ST, Li MH - ScientificWorldJournal (2014)

Bottom Line: Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number.The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate.This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO.

View Article: PubMed Central - PubMed

Affiliation: Department of Commerce Automation and Management, National Pingtung Institute of Commerce, No. 51, Min Sheng E. Road, Pingtung 900, Taiwan.

ABSTRACT
When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.

Show MeSH
Convergence of 3 algorithms.
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Related In: Results  -  Collection


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fig3: Convergence of 3 algorithms.

Mentions: The MS convergence curve suggests that the convergence of three algorithms is similar. However, the convergence of the proposed algorithm is superior to the remaining two algorithms, as shown in Figure 3.


An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

Wang ST, Li MH - ScientificWorldJournal (2014)

Convergence of 3 algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4150494&req=5

fig3: Convergence of 3 algorithms.
Mentions: The MS convergence curve suggests that the convergence of three algorithms is similar. However, the convergence of the proposed algorithm is superior to the remaining two algorithms, as shown in Figure 3.

Bottom Line: Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number.The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate.This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO.

View Article: PubMed Central - PubMed

Affiliation: Department of Commerce Automation and Management, National Pingtung Institute of Commerce, No. 51, Min Sheng E. Road, Pingtung 900, Taiwan.

ABSTRACT
When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.

Show MeSH