The optimal ordering quantity model under the uncertain food safety environment with a risk aversion concept

Shu Yen Hsu, Tyrone T. Lin, Chiao Chen Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Under the deterioration of the food safety environment of food suppliers and the uncertainty of the food safety of the purchased food by individual manufacturers, this study aims to establish a mathematical model standard basis as a reference for decision evaluation on the optimal order quantity of food raw materials to satisfy the maximum expected returns, according to the food safety situations and market acceptance (optimistic/normal/pessimistic),thus affecting procurement costs and selling prices. Assume that product costs and selling prices under different market environments will change;at the same time, importers are allowed to urgent order processing when out of stock issues and surplus products will be sold at discounted prices. Under the importers' attitudes to risk aversion, mathematical models are established to meet the importers' maximum gain and maximum utility function of optimal order quantity decision.

Original languageEnglish
Title of host publicationIEEM 2015 - 2015 IEEE International Conference on Industrial Engineering and Engineering Management
PublisherIEEE Computer Society
Pages470-474
Number of pages5
Volume2016-January
ISBN (Electronic)9781467380669
DOIs
Publication statusPublished - Jan 18 2016
Externally publishedYes
EventIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015 - Singapore, Singapore
Duration: Dec 6 2015Dec 9 2015

Other

OtherIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015
Country/TerritorySingapore
CitySingapore
Period12/6/1512/9/15

Keywords

  • Decision-making
  • expected profit
  • food raw material suppliers
  • optimal order quantity
  • uncertain food safety environment

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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