Optimal inventory control with sequential online auction in agriculture supply chain: an agent-based simulation optimisation approach

Abstract

With the development of e-commerce, in agriculture supply chain, online auction is adopted as an inventory clearing tool. Comparing to mathematical models studying inventory control over online sequential auctions, our agent-based simulation model could systematically describe the complexities of bidders’ information interactions and behaviour preferences caused from financial and production perspectives, and by other supply chain members. In addition, we take into account the complex and dynamic market environment, which will impact the operation effect of auction policies. With identical auction items, the profit-maximising firm must decide auction lot-size, which is the number of units in each auction, minimum initial bid, and the time interval between auctions. To obtain the optimal solution, nested partitions framework and optimal expected opportunity cost algorithm are integrated to improve computation accuracy and efficiency. A case study based on real data is conducted to implement and validate the proposed approach. Furthermore, based on the model, the paper studies the sensitivities of the decision variables under different supply and demand scenarios.

Keywords:

• simulation optimisation
• multi-agent systems
• budget allocation
• agriculture supply chain
• inventory control
• online auction

Notes

No potential conflict of interest was reported by the authors.

1 China Grain Trade Center. Grain market bidding rules (in Chinese). http://www.grainmarket.com.cn/Publish/01/101/10823/info.html

2 Website of China Grain Data Center. http://datacenter.cngrain.com/

3 Website of National Grain Trade Center. http://www.grainmarket.com.cn/Control/ControlList/18_47

4 Website of China Grain and Oil Network. http://www.chinagrain.cn/

Additional information

Funding

This paper has been supported in part by Special Research Funds in Public Welfare Sector of China [grant number 201413002], [grant number 201413003], by National Natural Science Foundation of China (NSFC) [grant number 71371015].