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Energy Decision-Making 1: Strategic Planning of Sustainable Manufacturing Projects Based on Stochastic Programming

  • Seog-Chan Oh
  • Alfred J. Hildreth
Chapter
Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

The need of energy decision making happens in realizing sustainable manufacturing. Many companies in the manufacturing industry have realized the importance of sustainability and have made a strategic move toward sustainable manufacturing to face the uncertainty of future energy availability and stringent environment regulations enacted around the world. However, it is a challenge to build a strategic plan for implementing sustainable manufacturing projects in such a way as to optimize energy efficiency opportunities while remaining in compliance with environmental regulations especially when future uncertainties, such as a fluctuation in energy prices or CO2 credit costs, are involved. This chapter proposes a new stochastic programming approach to identify the optimal investment plan for sustainable manufacturing projects to reduce energy and CO2 emission costs for manufacturing processes subject to various time, budget, technology and environmental constraints. The principle underlying the proposed approach is to solve a multi-period stochastic programming involving uncertain decision parameters, such as future CO2 credit market price, through the use of sample averaging approximation (SAA). An illustrative example application of the proposed model to an automotive company is presented. In Appendix, this chapter also provides an overview of the available standards and methods that can be used for preparing Scope 3 green house gas inventories and carbon footprints for organizations and their specific products or services.

Keywords

Carbon Credit Energy Information Administration Sustainable Manufacturing Expect Value Sample Average Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.TroyUSA
  2. 2.RochesterUSA

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