EconPapers    
Economics at your fingertips  
 

Optimisation of the national grain reserve system using a two-phase algorithm

Fan Zhang, Rongjinzi Wang, Jie Song and Yebing He

Journal of Simulation, 2023, vol. 17, issue 6, 746-764

Abstract: The local grain reserve system is widely recognised as the key to ensure Chinese grain security in response to emergency events. Hence, the government should optimize the amounts and locations of grain reserves. Nevertheless, the grain supply process for emergencies is hard to be analytically model due to the complexity and uncertainty. In this paper, we propose an off-site storage structure to balance the high storage cost and the lack of storage capacity. Based on the off-site storage structure, we build a simulation model of the local grain reserve system and develop a systematic two-phase optimisation algorithm to achieve the optimal scheme. The numerical results show that the optimal off-site grain storage scheme can reduce the total annual operation cost of the entire system by 16%. Finally, other managerial suggestions are proposed for the government to build a more efficient local grain reserve system.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/17477778.2022.2077664 (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:tjsmxx:v:17:y:2023:i:6:p:746-764

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20

DOI: 10.1080/17477778.2022.2077664

Access Statistics for this article

Journal of Simulation is currently edited by Christine Currie

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

 
Page updated 2025-03-20
Handle: RePEc:taf:tjsmxx:v:17:y:2023:i:6:p:746-764