Forward and backward fuzzy economic order quantity models considering learning theory / Ehsan Shekarian

Ehsan , Shekarian (2017) Forward and backward fuzzy economic order quantity models considering learning theory / Ehsan Shekarian. PhD thesis, University of Malaya.

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      Inventory systems deal with any activities to manage inventory of raw materials, work in process, finished products, spares, and equipment. As uncertainty is an inherent part of the real world, during these processes, the formulated inventory system should come up with uncertain data. Due to the capability of analyzing real situations, fuzzy inventory systems assist decision-making processes and provide a better understanding of the behavior of production and inventory environments. In this research, for the first time, a comprehensive literature review is conducted in the state-of-the-art of fuzzy inventory models where more than 120 papers are carefully and completely investigated according to the previous works. The fuzzy inventory systems that are based on the economic order/production quantity (EOQ/EPQ) settings are reviewed, so as to systematically analyze the fuzzy characteristics involved in capturing the uncertainty. Thereafter, to fill the identified gaps, two fuzzy EOQ models are developed. A fully fuzzy forward EOQ model for items with imperfect quality based on two different holding costs and learning considerations with triangular fuzzy numbers (TFNs) is extended. According to this model, the effect of learning and fuzziness on an inventory system are analyzed simultaneously.

      Item Type: Thesis (PhD)
      Additional Information: Thesis (PhD) - Faculty of Engineering, University of Malaya, 2017.
      Uncontrolled Keywords: Fuzzy economic order quantity models; inventory systems; Economic order/production quantity (EOQ/EPQ)
      Subjects: T Technology > TJ Mechanical engineering and machinery
      Divisions: Faculty of Engineering
      Depositing User: Mr Mohd Safri Tahir
      Date Deposited: 18 Jan 2018 12:55
      Last Modified: 11 Feb 2020 04:08

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