Design and optimization of an RFID-enabled automated warehousing system under uncertainties: a multi-criterion fuzzy programming approach

Ahmed Mohammed*, Qian Wang, Saleh Alyahya, Nick Bennett

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

In this paper, we investigated the design and optimization of a proposed radio-frequency identification (RFID)-enabled automated warehousing system in terms of the optimal number of storage racks and collection points that should be established in an efficient and cost-effective approach. To this aim, a fuzzy tri-criterion programming model was developed and used for obtaining trade-off decisions by measuring three conflicting objectives. These are minimization of the warehouse total cost, maximization of the warehouse capacity utilization, and minimization of the travel time of products from storage racks to collection points. To reveal the alternative Pareto optimal solutions using the developed model, a new approach was developed and compared with a recently developed fuzzy approach so-called Selim and Ozkarahan (SO). A decision-making algorithm was used to select the best Pareto optimal solution, and the applicability of the developed model was examined using a case study. Research findings demonstrate that the developed model is capable of generating an optimal solution as an aid for the design of the proposed RFID-enabled automated warehousing system.

Original languageEnglish
Pages (from-to)1661-1670
Number of pages10
JournalInternational Journal of Advanced Manufacturing Technology
Volume91
Issue number5-8
DOIs
Publication statusPublished - 1 Jul 2017

Bibliographical note

Publisher Copyright:
© 2016, Springer-Verlag London.

Keywords

  • Automated warehouse
  • Design
  • Fuzzy approach
  • Multi-criterion optimization
  • RFID

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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