Mansour Esmaeilzadeh; Amin Hoseinpoor; Mohammad Reza Namdar
Volume 11, Issue 30 , October 2014, , Pages 23-50
Abstract
Inventory classification is one of important techniques in inventory controlcontext. Managers have to classify inventories because of their variety andhigh volume. So a stream of research has been to attempt to find methodsthat increase the management control by determining the number of inventoryclasses. ...
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Inventory classification is one of important techniques in inventory controlcontext. Managers have to classify inventories because of their variety andhigh volume. So a stream of research has been to attempt to find methodsthat increase the management control by determining the number of inventoryclasses. In this paper the multiple objective particle swarm optimizationalgorithm has been used. This algorithm has been presented by Chi-Yang Tsaiand Szu-Wei Yeh in 2008. Multiple objective particle swarm optimization algorithmis an evolutionary algorithm that enables the management to optimizemultiple objectives simultaneously. Minimizing costs of inventory holdingand 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 sampleof 100 items. Results show that this algorithm can decrease costs of holding &ordering and also increase the inventory turnover ratios significantly.
