EconPapers    
Economics at your fingertips  
 

Two-warehouse inventory model for non-instantaneous deteriorating items with stock-dependent demand and inflation using particle swarm optimization

Sunil Tiwari (), Chandra K. Jaggi (), Asoke Kumar Bhunia (), Ali Akbar Shaikh () and Mark Goh ()
Additional contact information
Sunil Tiwari: University of Delhi
Chandra K. Jaggi: University of Delhi
Asoke Kumar Bhunia: The University of Burdwan
Ali Akbar Shaikh: Tecnológico de Monterrey
Mark Goh: National University of Singapore

Annals of Operations Research, 2017, vol. 254, issue 1, 401-423

Abstract: Abstract We investigate a two-warehouse inventory model for non-instantaneous deteriorating items with partial backlogging and stock-dependent demand under inflationary conditions. Shortages are allowed. The backlogging rate is variable and depends on the waiting time for the next replenishment. This paper seeks to determine an optimal replenishment policy that minimizes the present value of the total cost per unit time. The necessary and sufficient conditions for the existence and uniqueness of the optimal solution are found. The corresponding problems are formulated and solved with particle swarm optimization. Numerical experimentation and post-optimality analysis are conducted.

Keywords: Inventory; Non-instantaneous deterioration; Partial backlogging; Stock-dependent demand; Inflation; Particle swarm optimization; 90B05 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2492-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2492-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2019-11-06
Handle: RePEc:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2492-5