Abstract
This paper presents a model for integrated production and transportation scheduling in order to develop a low-carbon supply chain. A genetic algorithm and heuristic rules are applied to production scheduling problems, and beam search technique is used for transportation scheduling problems in consideration of both the tardiness of products and the amount of carbon dioxide emitted in manufacturing and transportation processes. A simulation system is developed to verify the effectiveness of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ministry of Economy (2008) Kyoto protocol target achievement plan, https://www.env.go.jp/en/earth/cc/kptap.pdf
http://www.meti.go.jp/english/policy/energy_environment/global_warming/kyoto_protocol.html
BearingPoint Inc (2008) 2008 supply chain monitor “How mature is the green supply chain?” http://www.bearingpoint.com
Emerson D, Piramuthu S (2004) Agent-based framework for dynamic supply chain configuration. In: Proceedings of 37th Hawaii international conference on system science, 70168a, CD-ROM, Big Island, Hawaii
Piramuthu S (2005) Knowledge-based framework for automated dynamic supply chain configuration. Eur J Oper Res 165:219–230
Tanimizu Y, Yamanaka M et al (2006) Multi-agent based dynamic supply chain configuration considering production schedules. In: Proceedings of 2006 international symposium on flexible automation, Osaka, pp 572–578
Tanimizu Y, Ozawa C et al (2007) Credibility of supplier in dynamic supply chain. In: Proceedings of 40th CIRP international seminar on manufacturing systems, CD-ROM, Liverpool
Tanimizu Y, Harada K et al (2010) A two-layered model for dynamic supply chain management considering transportation constraint. J Adv Mech Des Syst Manuf 4(5):913–923
Tanimizu Y, Harada K, et al (2010) Lean production and transportation scheduling for dynamic supply chain management. In: Proceedings of the 8th IEEE international conference on industrial informatics, Osaka, pp 869–874
Tanimizu Y, Amano K et al (2012) Multi-objective production and transportation scheduling considering carbon dioxide emissions reductions in dynamic supply chains. Int J Autom Technol 6(3):322–330
Hollanad JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison Wesley, Reading
Tanimizu Y, Sugimura N (2002) A study on reactive scheduling based on genetic algorithm. In: Proceedings of the 35th CIRP-ISMS, Soul, pp 219–224
Tanimizu Y, Sakaguchi T, Iwamura K, Sugimura N (2006) Evolutional reactive scheduling for agile manufacturing systems. Int J Prod Res 44(18–19):3727–3742
Gungor A, Gupta SM (1999) Issues in environmentally conscious manufacturing and product recovery: a survey. Comput Ind Eng 36:811–853
Bloemhof-Ruwaard JM, Beek P, Hordijka L, Wassenhove LN (1995) Interactions between operational research and environmental management. Eur J Oper Res 85(2):229–243
Cholette S, Venkat K (2009) The energy and carbon intensity of wine distribution: a study of logistical options for delivering wine to consumers. J Clean Prod 17:1401–1413
Sarkis J, Zhu Q, Lai K (2011) An organizational theoretic review of green supply chain. Int J Prod Econ 130:1–15
Hassini E, Surti C, Searcy C (2012) A literature review and a case study of sustainable supply chains with a focus on metrics. Int J Prod Econ 140:69–82
Sundarakani B, Souza R, Goh M, Wagner SM, Manikandan S (2010) Modeling carbon footprints across the supply chain. Int J Prod Econ 128:43–50
Seuring S, Muller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16:1699–1710
The Ministry of Environment and the Ministry of Economy (2006) Trade and industry, guideline of calculation methods for carbon dioxide emissions in logistics (in Japanese). http://www.greenpartnership.jp/pdf/co2/guidelinev3.0.pdf
Narita H (2012) Environmental burden analyzer for machine tool operations and its application. Manufacturing system. InTesh 247–260
Winston PH (1984) Artificial intelligence, 2nd edn. Addison-Wesley, Reading
Ito H, Tanimizu Y et al (2014) Work allocation in transportation scheduling considering carbon dioxide emissions reductions in dynamic supply chains. In: Proceedings of ISCIE/ASME 2014 international symposium on flexible automation, 105L, Hyogo
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Japan
About this chapter
Cite this chapter
Tanimizu, Y., Ito, H., Matsui, K. (2017). Integrated Production and Transportation Scheduling for Low-Carbon Supply Chains. In: Matsumoto, M., Masui, K., Fukushige, S., Kondoh, S. (eds) Sustainability Through Innovation in Product Life Cycle Design. EcoProduction. Springer, Singapore. https://doi.org/10.1007/978-981-10-0471-1_27
Download citation
DOI: https://doi.org/10.1007/978-981-10-0471-1_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0469-8
Online ISBN: 978-981-10-0471-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)