Economic order quantity models for items with imperfect quality and emission considerations

Nima Kazemi (Corresponding author), Salwa Hanim Abdul-Rashid, Raja Ariffin Raja Ghazilla, Ehsan Shekarian, Simone Zanoni

Research output: Contribution to journalArticleAcademicpeer-review

84 Citations (Scopus)

Abstract

Incorporation of quality and environmental concerns in production and inventory models has received considerable attention in the inventory management literature; however, researchers studied these topics mostly independently. Thus, it is required to jointly incorporate those two relevant aspects in a single research to support decisions, compare the results and obtain new insights for complexities in practice. This paper takes a step in this line of thought and revisits some economic order quantity (EOQ) models with imperfect quality from a sustainable point of view. The objective is to investigate the impact of emission costs on the replenishment order sizes and the total profit of a buyer (retailer) in an imperfect supply process, where the buyer receives the batches containing a percentage of imperfect quality items. First, an EOQ model with imperfect quality items and emission costs, which are the result of warehousing and waste disposal activities, is formulated. Next, the model is extended to account for the situations where the buyer considers different areas for stocking the imperfect and good quality items, learning occurs in imperfect quality and the inspection process at the buyer's end contains error. The developed models are tested numerically and compared to investigate the optimal policies considering emission costs.

Original languageEnglish
Pages (from-to)99-115
Number of pages17
JournalInternational Journal of Systems Science: Operations & Logistics
Volume5
Issue number2
DOIs
Publication statusPublished - 3 Apr 2018
Externally publishedYes

Keywords

  • EOQ
  • Sustainability
  • Carbon emission
  • Imperfect quality
  • Learning
  • Inspection error
  • carbon emission
  • imperfect quality
  • learning
  • inspection error
  • sustainability

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