Optimized radio-frequency identification system for different warehouse shapes

Biswajit Sarkar, Dalila Takeyeva, Rekha Guchhait, Mitali Sarkar

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Advanced technologies are receiving increased attention in every sector of industry. One of such technologies is radio-frequency identification. In some business models, a manufacturer's responsibility is to correctly manage inventories for supply chain while maximizing profit for every member. On the other hand, the issue of supply chain member unreliability persists in the real situation. The manufacturer uses radio-frequency identification system in order to better control inventory, which then may manage inventory pooling and labor investment. An efficient number of a radio-frequency identification readers are required for detecting radio-frequency identification tags. However, radio-frequency identification system is different for different cases, especially when there are different layouts even in the same system. If radio-frequency identification technology is not deployed correctly according to the warehouse specifics, the issue of optimized benefits may arise. This study explores how radio-frequency identification technology can be used to increase profit of supply chain members in an uncertain situation like unreliability within the supply chain management. In terms of deployment, the optimal gap between the readers while having optimal number of readers for most common warehouse shapes is investigated in this study. Because the life of product sometimes is unpredictable, it is possible that no specified form of distribution function will be followed. To identify the global optimal solution, the Kuhn–Tucker approach is utilized. The numerical study shows that the manufacturer may gain more profit by implementing revenue sharing and the optimal spacing between readers while optimizing radio-frequency identification system costs for different layouts.

Original languageEnglish
Article number109811
JournalKnowledge-Based Systems
Publication statusPublished - 2022 Dec 22

Bibliographical note

Funding Information:
This research was supported by the Yonsei University Research Fund of 2021 (Project number 2021-22-0305). This work is supported by National Research Foundation of Korea (NRF) grant, funded by the Korea Government (MSIT) ( NRF-2020R1F1A1064460 ).

Publisher Copyright:
© 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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