Designing Individual Material Recovery in Reverse Supply Chain Using Linear Physical Programming at the Digital Transformation Edge

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説明

<p>In order to save individual virgin materials by recycling products, manufacturing companies must adopt new information technology to evaluate the environmental impact of their management. The transformative adoption of digital technology is referred to as digital transformation (DX), and the DX technique can be used to digitalize reverse supply chain management strategies for environmental issues. Before the use of the DX technique for a reverse supply chain, a decision maker (DM) was not be able to know the material types of the end-of-life (EOL) products based on their status until they had been recycled. However, using the DX technique, the DM is able to understand EOL product data such as the number and the amount of each material within the EOL products in advance. Therefore, the usage of the DX technique enables the DM to design the reverse supply chain network not only environmentally-friendly but also economical. In applying DX to supply chain networks, supply chain managers must cut costs and evaluate environmental impacts. In order to balance environmental and economic concerns, the DM can design a reverse supply chain network using a solving method for multi-criteria decision making such as linear physical programming (LPP). Using the LPP algorithm, the DM can calculate a harmonized weight for objective functions with a trade-off relationship. This study designs a reverse supply chain network for individual material recovery environmentally-friendly and economical by using LPP as one of the support methods in the era of DX. The academic contribution of this study is that the effect of objective functions for individual material types can be identified, and the practical contribution is that it enables us to find a reverse network solution based on individual materials being collected from EOL products recycled throughout numerical experiments with LPP.</p>

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詳細情報 詳細情報について

  • CRID
    1390009294937939968
  • NII論文ID
    130008141884
  • DOI
    10.11221/jima.72.259
  • ISSN
    21879079
    13422618
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • JaLC
    • CiNii Articles
    • KAKEN
  • 抄録ライセンスフラグ
    使用不可

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