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Optimization and Inventory Management pp 137–159Cite as

Imperfect Quality Item Inventory Models Considering Carbon Emissions

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Part of the book series: Asset Analytics ((ASAN))

Abstract

Research on inventory models with environmental consideration has recently become a popular research stream. The amount of energy consumption and greenhouse gas emissions is influenced by inventory decisions such as delivery quantity and delivery frequencies. This chapter focuses on a supply chain system which contains a percentage of imperfect quality items in its delivered lot; we also consider carbon emission costs under a carbon tax policy. Processing the defective items, which increases carbon emission, affects supply chain decisions. We present two economic order quantity models considering carbon emission and defective items with different shortage conditions. We then study low-carbon two-echelon supply chain inventory model considering supply chain integration and imperfect quality items. Numerical examples are provided to illustrate how these models can be applied in practice. Sensitivity analysis is performed to gain more insight on changing parameters in the numerical studies.

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan City, Taiwan

    Hui-Ming Wee & Yosef Daryanto

  2. Department of Industrial Engineering, Universitas Atma Jaya Yogyakarta, Yogyakarta, Indonesia

    Yosef Daryanto

Authors
  1. Hui-Ming Wee
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  2. Yosef Daryanto
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Corresponding author

Correspondence to Hui-Ming Wee .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Gujarat University, Ahmedabad, India

    Nita H. Shah

  2. Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, India

    Mandeep Mittal

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Wee, HM., Daryanto, Y. (2020). Imperfect Quality Item Inventory Models Considering Carbon Emissions. In: Shah, N., Mittal, M. (eds) Optimization and Inventory Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-9698-4_8

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