In the manufacturing industry, it is essential to consider environmental impact during production while meeting diverse customer needs such as varying product types, their quality, and quantity. In particular, to achieve carbon neutrality, the manufacturing industry is focusing on the closed-loop production to reduce carbon emissions by reducing, reusing, and recycling waste resources. However, relying solely on the use of waste resources generated within a single company has limitations in reducing carbon emissions and responding to fluctuating demand. To solve these problems, we focus on the carbon credits, which allow companies to trade carbon emission reductions. In this paper, we propose a closed-loop production planning method to deal with carbon emission reductions while considering the demand uncertainty and evaluate the effectiveness of the proposed method through computer experiments.

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A Proposal for a Closed-Loop Production Planning Method to Deal with Carbon Emission Reduction

  • Araki Kawamura,
  • Toshiya Kaihara,
  • Daisuke Kokuryo,
  • Houei Mizuhara,
  • Toyohiro Umeda,
  • Hideo Ikeda,
  • Akihisa Horio

摘要

In the manufacturing industry, it is essential to consider environmental impact during production while meeting diverse customer needs such as varying product types, their quality, and quantity. In particular, to achieve carbon neutrality, the manufacturing industry is focusing on the closed-loop production to reduce carbon emissions by reducing, reusing, and recycling waste resources. However, relying solely on the use of waste resources generated within a single company has limitations in reducing carbon emissions and responding to fluctuating demand. To solve these problems, we focus on the carbon credits, which allow companies to trade carbon emission reductions. In this paper, we propose a closed-loop production planning method to deal with carbon emission reductions while considering the demand uncertainty and evaluate the effectiveness of the proposed method through computer experiments.