single-au.php

IJAT Vol.6 No.3 pp. 322-330
doi: 10.20965/ijat.2012.p0322
(2012)

Paper:

Multi-Objective Production and Transportation Scheduling Considering Carbon Dioxide Emissions Reductions in Dynamic Supply Chains

Yoshitaka Tanimizu, Katuhumi Amano, Kana Harada,
Chisato Ozawa, and Nobuhiro Sugimura

Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

Received:
November 1, 2011
Accepted:
March 27, 2012
Published:
May 5, 2012
Keywords:
green supply chain, scheduling, transportation plan, carbon dioxide emissions, multi-objective genetic algorithm
Abstract

Previous studies have proposed a dynamic configuration method for two-layered supply chains consisting of a set of clients and suppliers. The proposed method provides suitable delivery times and prices of products through the modification process of production schedules of the suppliers and the negotiation process between the clients and the suppliers in consideration of transportation constraints. This research proposes a new supply chain model extended for carbon dioxide emissions reductions in the two-layered dynamic supply chains. The suppliers provide transportation plans to reduce carbon dioxide emissions in transportation processes without losing chances to enter into a large number contracts with the clients. The effectiveness of the proposed model is verified through computational experiments from the viewpoints of both the amount of carbon dioxide emitted in transportation processes and the profits of the suppliers.

Cite this article as:
Y. Tanimizu, K. Amano, K. Harada, <. Ozawa, and N. Sugimura, “Multi-Objective Production and Transportation Scheduling Considering Carbon Dioxide Emissions Reductions in Dynamic Supply Chains,” Int. J. Automation Technol., Vol.6, No.3, pp. 322-330, 2012.
Data files:
References
  1. [1] T. Kaihara and S. Fujii, “IT based Virtual Enterprise Coalition Strategy in Agile Manufacturing Environment,” Proc. of 35th CIRP Int. Seminar on Manufacturing Systems, pp. 333-338, 2002.
  2. [2] S. Piramuthu, “Knowledge-based Framework for Automated Dynamic Supply Chain Configuration,” European J. of Operational Research, Vol.165, pp. 219-230, 2005.
  3. [3] D. Emerson and S. Piramuthu, “Agent-based Framework for Dynamic Supply Chain Configuration,” Proc. of 37th Hawaii Int. Conf. on System Science, 70168a, CD-ROM, 2004.
  4. [4] Y. Tanimizu, M. Yamanaka, et al., “Multi-agent Based Dynamic Supply Chain Configuration Considering Production Schedules,” Proc. of 2006 Int. Symp. on Flexible Automation, pp. 572-578, 2006.
  5. [5] Y. Tanimizu, C. Ozawa, et al., “Credibility of Supplier in Dynamic Supply Chain,” Proc. of 40th CIRP Int. Seminar on Manufacturing Systems, CD-ROM, 2007.
  6. [6] C. Ozawa, Y. Tanimizu, et al., “A Multi-Layered Model for Dynamic Supply Chain Configuration,” Proc. of the 4th Int. Conf. on Leading Edge Manufacturing in 21st Century, pp. 665-668, 2007.
  7. [7] C. Ozawa, Y. Tanimizu, et al., “Cooperative Negotiations in Multi-Layered Dynamic Supply Chains,” Proc. of the ASME Int. Symp. on Flexible Automation 2008, CD-ROM, 2008.
  8. [8] Y. Tanimizu, K. Harada, et al., “A Two-Layered Model for Dynamic Supply Chain Management Considering Transportation Constraint,” J. of Advanced Mechanical Design, Systems, andManufacturing, Vol.4, No.5, pp. 913-923, 2010.
  9. [9] Y. Tanimizu, K. Harada, et al., “Lean Production and Transportation Scheduling for Dynamic Supply Chain Management,” Proc. of the 8th IEEE Int. Conf. on Industrial Informatics, pp. 869-874, 2010.
  10. [10] Ministry of Economy, Trade and Industry, Kyoto Protocol Target Achievement Plan, 2008. (in Japanese)
  11. [11] http://www.meti.go.jp/english/policy/energy_environment/global_warming/kyoto_protocol.html
  12. [12] BearingPoint Inc., “2008 Supply Chain Monitor “How Mature is the Green Supply Chain?”” 2008. (www.bearingpoint.com)
  13. [13] D. E. Goldberg, “Genetic algorithm in search,” Optimization and Machine Learning, Addison Wesley, Reading, Massachusetts, 1989.
  14. [14] Y. Tanimizu, T. Sakaguchi, et al., “Evolutional Reactive Scheduling for Agile Manufacturing Systems,” Int. J. of Production Research, Vol.44, No.18-19, pp. 3727-3742, 2006.
  15. [15] The Ministry of Environment and the Ministry of Economy, Trade and Industry, Guideline of Calculation Methods for Carbon Dioxide Emissions in Logistics, 2006. (in Japanese)

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 18, 2019