A fuzzy economic order quantity model for multiple stage supply chain with fully backlogged shortages derived without derivatives under the effect of human learning

Richi Singh, Ashok Kumar and Dharmendra Yadav

International Journal of Operational Research, 2024, vol. 51, issue 4, 491-536

Abstract: In industries, inventory managers face major difficulties in inventory planning when the available information fluctuates abruptly or is unclear. This ambiguity can be treated appropriately by using fuzzy sets. Moreover, human learning is effective in reducing the level of fuzziness over the infinite horizon. In the present study, a fuzzy three-stage (buyer-distributor-vendor) EOQ model is developed. In this model, all the cost parameters are taken as fuzzy parameters. Shortages are allowed but fully backlogged at the buyer end. The novelty of the paper lies in deriving the fuzzy model by using the arithmetic-geometric inequality method and proposing four theorems based on optimal frequency for vendor and distributor, along with incorporating the concept of learning in fuzziness. Some numerical examples are taken to demonstrate the model in a better way. Also, a comparison among the results of this paper, and other papers are done with the help of an example, which shows that the present model better represents the practical financial situations. At last, sensitivity analysis concerning all parameters and managerial insights are presented to justify the significance of the model.

Keywords: supply chain; arithmetic geometric inequality; fuzzy costs; learning; backlogging. (search for similar items in EconPapers)
Date: 2024
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