Advertisement

Supply Chain Coordination with Energy Price Uncertainty, Carbon Emission Cost, and Product Return

  • S. Paul
  • M. I. M. WahabEmail author
  • X. F. Cao
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 197)

Abstract

An EOQ model for a coordinated two-level supply chain is developed under energy (gasoline) price uncertainty and defective items in transhipment. It is assumed that the transportation cost not only depends on the lot size, but also depends on the gasoline price uncertainty. The purpose is to determine the optimal production–shipment policy for the proposed model by taking into account the percentage of defective items, transportation cost, setup cost, screening cost, holding cost, and carbon emission cost. The objective is to determine the optimal number of shipments and the optimal order quantity that minimize the expected total cost per unit time. Expressions for the optimal order quantity, the optimal number of shipments, the optimal number of buyer’s cycle during which the defective items are stored at the buyer’s warehouse before shipping them to the vendor are derived by minimizing the expected total cost per unit time. In order to illustrate the proposed model, a numerical example is presented and results are discussed. It is found that as the gasoline price uncertainty increases, both the total cost and shipment size increase. This shows that the gasoline price influences the supply chain coordination. Moreover, when the fixed gasoline consumption depending on the vehicle size, type, or age increases, shipment size increases, the number of shipments decreases, and the total cost increases. This implies that when the truck size or type used for shipping changes, the supply chain coordination decision will also change. The variable gasoline consumption increases the total cost of the supply chain. Finally, a similar behavior is observed with respect to fixed and variable carbon emissions costs for the buyer.

Keywords

Supply Chain Carbon Emission Transportation Cost Order Quantity Setup Cost 
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.

References

  1. Aderohunmu R., Mobolurin A., Bryson N., (1995). Joint vendorbuyer policy in JIT manufacturing. Journal of the Operational Research Society, 46: 375–385.Google Scholar
  2. Ben-Daya M., Hariga M. (2000). Economic lot scheduling problem with imperfect production processes. Journal of the Operational Research Society, 51: 875–881.Google Scholar
  3. Ben-Daya M., Darwish, M., and Ertogral, K. (2008). The joint economic lot sizing problem: Review and extensions. European Journal of Operational Research, 185: 726–742.Google Scholar
  4. Benerjee A. (1986). A joint economic lot size model for purchaser and vendor. Decision Sciences, 17: 292–311.Google Scholar
  5. Cheng C. E. (1991). An economic order quantity model with demand-dependent unit production cost and imperfect production processes. IIE Transactions, 23: 23–28.Google Scholar
  6. Clewlow L., Strickland C. (2000) Energy Derivatives: Pricing and Risk Management, Lacima Publications.Google Scholar
  7. Darwish M. A. (2008). Joint determination of order quantity and reorder point of continuous review model under quantity and freight rate discounts. Computers & Operations Research, 35: 3902–3917.Google Scholar
  8. Dixit A. K., Pindyck R. S. (1994). Investment under uncertainty, Princeton University Press, Princeton USA.Google Scholar
  9. Ertogral K., Darwish M., Ben-Daya M. (2007). Production and shipment lot sizing in a vendor buyer supply chain with transportation cost. European Journal of Operational Research, 176: 1592–1606.Google Scholar
  10. Fitch J. W. (1994). Motor truck engineering handbook, Society of Automotive Engineers, Inc. USA, 4th Edition. 4: 83–108.Google Scholar
  11. Goyal S. K. (1976). An integrated inventory model for a single supplier-single customer problem. International Journal of Production Research, 15: 107–111.Google Scholar
  12. Goyal S. K. (1988). Joint economic lot size model for purchaser and vendor: A comment. Decision Sciences, 19: 236–2411.Google Scholar
  13. Goyal S. K. (1995). A one-vendor multi-buyer integrated inventory model: A comment. European Journal of Operational Research, 82: 209–210.Google Scholar
  14. Goyal S. K., Nebebe F. (2000). Determination of economic production-shipment policy for a single-vendor single-buyer system. European Journal of Operational Research, 121(1): 175–178.Google Scholar
  15. Ha D., Kim S. L. (1997). Implementation of JIT purchasing: an integrated approach. Production Planning & Control, 8(2): 152–157.Google Scholar
  16. Hahn W. J., Dyer J. S. (2008). Discrete time modeling of mean reverting stochastic processes for real option valuation. European Journal of Operational Research, 184: 534–548.Google Scholar
  17. Hill R. M., Omar M. (2006). Another look at the single-vendor single-buyer integrated production-inventory problem. International Journal of Production Research, 44(4): 791–800.Google Scholar
  18. Hoque M. A., Goyal S. K. (2000). An optimal policy for single-vendor single-buyer integrated production-inventory system with capacity constraint of the transport equipment. International Journal of Production Economics, 65(3): 305–315.Google Scholar
  19. Hull J. (2012). Options Future and other Derivatives, Prentice Hall, NewJersey.Google Scholar
  20. Lee C. Y. (1986). The economic order quantity for freight discount cost. IIE Transactions 18(3): 318–320.Google Scholar
  21. MSNBC and Reuters. October 25, (2005). Is Wal-Mart going green? http://www.msnbc.msn.com/id/9815727
  22. Nylund N.-O., Erkkilä K., (2005). Heavy-duty truck emissions and fuel consumption simulating real-world driving in laboratory conditions. 2005 Diesel Engine Emissions Reduction (DEER) Conference August 21–25, 2005 Chicago, Illinois, USA.Google Scholar
  23. Pagell M., Yang C. L., Krumwiede D. W., Sheu C., (2004). Does the competitive environment influence the efficacy of investment in environmental management? Journal of Supply Chain Management, 40(3): 30–39.Google Scholar
  24. Papachristos S., Konstantaras L. (2006). Economic ordering quantity models for items with imperfect quality. International Journal of Production Economics, 100: 148–154.Google Scholar
  25. Pinto C. B., Brandão L., Hahn W. J. (2007). Modeling Switching Options using Mean Reverting Commodity Price Models, 11th International Conference on Real Options. 6–9.Google Scholar
  26. Salameh M. K., Jaber M. Y. (2000). Economic Production quantity model for items with imperfect quality. International Journal of Production Economics, 64: 59–64.Google Scholar
  27. Sathaye N., Horvath A., and Madanat S. (2010). Unintended impacts of increases truck loads on pavement supply-chain emission. Transportation Research Part A, 44: 1–5.Google Scholar
  28. Schwartz E., Smith J. E. (2000). Short-term variations and long-term dynamics in commodity prices. Management Science, 46: 893–911.Google Scholar
  29. The New York Times. August 3, (2008). Shipping costs start to crimp globalization.Google Scholar
  30. The Wall Street Journal. September 22, (2008). Why high oil prices are upending the way companies should manage their supply chain.Google Scholar
  31. Wahab M. I. M., Jaber M. Y. (2010) Economic order quantity for items with imperfect quality, different holding costs, and learning effects: A note. Computer & Industrial Engineering, 58(1): 186–190.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Ryerson UniversityTorontoCanada

Personalised recommendations