A study on two-warehouse partially backlogged deteriorating inventory models under inflation via particle swarm optimisation

A.K. Bhunia, A.A. Shaikh and R.K. Gupta

International Journal of Systems Science, 2015, vol. 46, issue 6, 1036-1050

Abstract: This paper deals with a deterministic inventory model for linear trend in demand under inflationary conditions with different rates of deterioration in two separate warehouses (owned and rented warehouses). The replenishment rate is infinite. The stock is transferred from the rented warehouse to owned warehouse in continuous release pattern and the associated transportation cost is taken into account. At owned warehouse, shortages, if any, are allowed and partially backlogged with a rate dependent on the duration of waiting time up to the arrival of the next lot. The corresponding problems have been formulated as nonlinear constrained optimisation problems for two different policies (inventory follows shortage (IFS) and shortage follows inventory (SFI)). Finally, the model has been illustrated with a numerical example and to study the effects of changes of different system parameters on initial stock level, maximum shortage level and cycle length with the minimum cost of the system, sensitivity analyses have been carried out by changing one parameter at a time and keeping the others at their original values.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2013.807385 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:46:y:2015:i:6:p:1036-1050

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2013.807385

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().