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

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


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.


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.


  1. Berendt C, Ritter M, McClive T, Augustine P, Galaas T, Hunt C (2007) CO2 allowance price forecast. PACE Global Energy ServiceGoogle Scholar
  2. Birge JR and Louveaux F (2011) Introduction to stochastic programming. Springer, New YorkGoogle Scholar
  3. Capoor K, Ambrosi P (2010) State and trends of the carbon market 2009. World Bank and International Emissions Trading Association (IETA), Washington, D.C.Google Scholar
  4. Carnegie Mellon University, Green Design Institute (2011) Economic input output life cycle assessment. Homepage of the Green Design Institute. Available online: Assessed on 2 Nov 2015
  5. Duerr D (2007) EU emission trading fact book. Inagendo Energy Policy ConsultingGoogle Scholar
  6. Ellerman AD, Joskow PL (2008) The European union’s emissions trading system. Pew CenterGoogle Scholar
  7. EPA (United States Environmental Protection Agency) (2011) Report on climate change indicators in the United States. Available online: Assessed on 22 Oct 2015
  8. Federal Leadership in Environmental, Energy, and Economic Performance (2009) Executive Order 13514 of 4 Oct 2009. Federal Register 74(194)Google Scholar
  9. Ford Motor Company (2005) Voluntary reporting of 2004 greenhouse gas emissionsGoogle Scholar
  10. General Motors Corporation (2008) Voluntary reporting of General Motors Corporation United States greenhouse gas (GHG) emissions for calendar years 1990–2007Google Scholar
  11. GNU Linear Programming Kit, Version 4.47,
  12. Hashim H, Douglas P, Elkamel A, Croiset E (2005) An optimization model for energy planning with CO2 emission considerations. Ind Eng Chem Res 44:879–890CrossRefGoogle Scholar
  13. Huang YA, Weber CL, Matthews HS (2009) Categorization of Scope 3 emissions for streamlined enterprise carbon footprinting. Environ Sci Technol 43(22):8509–8515Google Scholar
  14. International Organization for Standardization (2006) ISO 14064, Geneva, SwitzerlandGoogle Scholar
  15. IPCC (Intergovernmental Panel on Climate Change) (1995) Climate change 1995: the science of climate change (second assessment report). Cambridge University Press, CambridgeGoogle Scholar
  16. Iyer R, Grossmann E, Vasantharajan S, Cullick S (1998) Optimal planning and scheduling of offshore oil field infrastructure investment and operations. Ind Eng Chem Res 37:1380–1397Google Scholar
  17. Jognston L, Hausman E, Biewald B, Wilson R, David W (2011) carbon dioxide price forecast. Synapse Energy Economics, Inc.Google Scholar
  18. Kleywegt AJ, Shapiro A, Homem-de-Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J Optim 12(2):479–502MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kyoto Protocol to the United Nations Framework Convention on Climate Change (2011) Available online: Assessed on 22 Oct 2015
  20. Leontief W (1970) Environmental repercussions and the economic structure: an input-output approach. Rev Econ Stat 52(3):262–277CrossRefGoogle Scholar
  21. Mani M, Lyons KW, Rachuri S, Subrahmanian E, Sriram R (2008) Introducing sustainability early into manufacturing process planning. In: Proceedings of the 14th international conference on manufacturing science and engineering, Evanston, IL, USAGoogle Scholar
  22. Mo B, Hegge J, Wangenstee I (1991) Stochastic generation expansion planning by means of stochastic dynamic programming. IEEE Trans Power Syst 6:662–668CrossRefGoogle Scholar
  23. Oh S-C, Hildreth A (2013) Decisions on energy demand response option contracts in smart grids based on activity-based costing and stochastic programming. Energies 6:425–443CrossRefGoogle Scholar
  24. Oh S-C, Hildreth A (2014) Estimating the technical improvement of energy efficiency in the automotive industry—stochastic and deterministic frontier benchmarking approaches. Energies 7:6198–6222Google Scholar
  25. Oh S-C, Shin J (2015) The impact of mismeasurement in performance benchmarking: a monte carlo comparison of SFA and DEA with different multi-period budgeting strategies, European. J Oper Res 240:518–527CrossRefGoogle Scholar
  26. Sirikitputtisak T, Mirzaesmaeeli H, Douglas P, Croiset E, Elkamel A, Gupta M (2009) A multi-period optimization model for energy planning with CO2 emission considerations. Phys Proc 1:4339–4346Google Scholar
  27. US Department of Energy (2008) Technology roadmap for energy reduction in automotive manufacturing. Office of Energy Efficiency & Renewable Energy, Industrial Technologies Program and U.S. Council for Automotive Research, Washington, DC, USAGoogle Scholar
  28. U.S. Energy Information Administration (2010) Annual energy outlook 2011 with projections to 2035Google Scholar
  29. World Resource Institution (WRI) and World Business Council for Sustainable Development (WBCSB) (2010) The greenhouse gas protocol corporate value chain (Scope-3) accounting and reporting standard, Washington D.C., USAGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.TroyUSA
  2. 2.RochesterUSA

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