Skip to main content

Advanced Planning in Vertically Integrated Wine Supply Chains

  • Conference paper
Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

  • 2304 Accesses

Abstract

This chapter gives detailed insights into a project for transitioning a wine manufacturing company from a mostly spreadsheet driven business with isolated silo-operated planning units into one that makes use of integrated and optimised decision making by use of modern heuristics. We present a piece of the puzzle - the modelling of business entities and their silo operations and optimizations, and pave the path for a further holistic integration to obtain company-wide globally optimised decisions. We argue that the use of “Computational Intelligence” methods is essential to cater for dynamic, time-variant and non-linear constraints and solve today’s real-world problems exemplified by the given wine supply chain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackoff, R.L.: The future of operational research is past. J. Oper. Res. Soc. 30, 93–104 (1979)

    Google Scholar 

  2. Aikens, C.H.: Facility location models for distribution planning. Europ. J. Oper. Res. 22(3), 263–279 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  3. Altiparmak, F., Gen, M., Lin, L., Paksoy, T.: A genetic algorithm approach for multi-objective optimization of supply chain networks. Comput. Ind. Eng. 51(1), 196–215 (2006)

    Article  Google Scholar 

  4. Boulton, R.B., Singleton, V.L., Bisson, L.F., Kunkee, R.E.: Principles and Practices of Winemaking. Springer (1998)

    Google Scholar 

  5. Caggiano, K.E., Jackson, P.L., Muckstadt, J.A., Rappold, J.A.: Optimizing Service Parts Inventory in a Multiechelon, Multi-Item Supply Chain with Time-Based Customer Service-Level Agreements. Oper. Res. 55(2), 303–318 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Caglar, D., Li, C.L., Simchi-Levi, D.: Two-echelon spare parts inventory system subject to a service constraint. IIE Transactions 36(7), 655–666 (2004)

    Article  Google Scholar 

  7. Chandra, P., Fisher, M.L.: Coordination of production and distribution planning. Europ. J. Oper. Res. 72(3), 503–517 (1994)

    Article  MATH  Google Scholar 

  8. Cheng, R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithms—i: representation. Comput. Ind. Eng. 30(4), 983–997 (1996)

    Article  Google Scholar 

  9. Clark, A.J., Scarf, H.: Optimal policies for a multi-echelon inventory problem. Manage. Sci. 50(12 suppl.), 1782–1790 (2004)

    Article  Google Scholar 

  10. Coit, D.W., Smith, A.E.: Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach. Comput. Oper. Res. 23(6), 515–526 (1996)

    Article  MATH  Google Scholar 

  11. Davis, L.: Job shop scheduling with genetic algorithms. In: Proc. 1st Int. Conf. Genetic Algorithms, pp. 136–140 (1985)

    Google Scholar 

  12. Davis, L.: Embracing complexity. Toward a 21st century supply chain solution (2008), Web-resource http://sdcexec.com/online/printer.jsp?id=9012

  13. Hanssmann, F.: Optimal inventory location and control in production and distribution networks. Oper. Res. 7(4), 483–498 (1959)

    Article  MathSciNet  Google Scholar 

  14. Holthaus, O.: Scheduling in job shops with machine breakdowns: an experimental study. Comput. Ind. Eng. 36(1), 137–162 (1999)

    Article  Google Scholar 

  15. Jain, A.K., Elmaraghy, H.A.: Production scheduling/rescheduling in flexible manufacturing. Int. J. Prod. Res. 35(1), 281–309 (1997)

    Article  MATH  Google Scholar 

  16. Kutanoglu, E., Sabuncuoglu, I.: Routing-based reactive scheduling policies for machine failures in dynamic job shops. Int. J. Prod. Res. 39(14), 3141–3158 (2001)

    Article  MATH  Google Scholar 

  17. Lambert, D.M.: Supply chain management: Implementation issues and research opportunities. Int. J. of Logistics Management 9, 1–20 (1998)

    Article  Google Scholar 

  18. Lee, C.Y., Choi, J.Y.: A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights. Comput. Oper. Res. 22(8), 857–869 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  19. Lee, H., Pinto, J.M., Grossmann, I.E., Park, S.: Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Ind. Eng. Chem. Res. 35(5), 1630–1641 (1996)

    Article  Google Scholar 

  20. Levine, J., Ducatelle, F.: Ant colony optimization and local search for bin packing and cutting stock problems. J. Oper. Res. Soc. 55(7), 705–716 (2004)

    Article  MATH  Google Scholar 

  21. Liang, K.H., Yao, X., Newton, C., Hoffman, D.: A new evolutionary approach to cutting stock problems with and without contiguity. Comput. Oper. Res. 29(12), 1641–1659 (2002)

    Article  MathSciNet  Google Scholar 

  22. Martin, C.H., Dent, D.C., Eckhart, J.C.: Integrated production, distribution, and inventory planning at libbey-owens-ford. Interfaces 23(3), 68–78 (1993)

    Article  Google Scholar 

  23. Naso, D., Surico, M., Turchiano, B., Kaymak, U.: Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete. Europ. J. Oper. Res. 177(3), 2069–2099 (2007)

    Article  MATH  Google Scholar 

  24. Oliver, R.K., Webber, M.D.: Supply-chain management: Logistics catches up with strategy. In: Logistics. Chapman and Hall (1982) (Reprint from Outlook)

    Google Scholar 

  25. Petrovic, D., Alejandra, D.: A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy Sets and Systems 157(16), 2273–2285 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  26. Potter, M.: The design and analysis of a computational model of cooperative coevolution. Ph.D. Thesis, George Mason University (1997)

    Google Scholar 

  27. Pyke, D.F., Cohen, M.A.: Performance characteristics of stochastic integrated production-distribution systems. Europ. J. Oper. Res. 68(1), 23–48 (1993)

    Article  MATH  Google Scholar 

  28. Pyke, D.F., Cohen, M.A.: Multiproduct integrated production–distribution systems. Europ. J. Oper. Res. 74(1), 18–49 (1994)

    Article  MATH  Google Scholar 

  29. Stadtler, H., Kilger, C.: Supply Chain Management and Advanced Planning. Springer (2008)

    Google Scholar 

  30. Thomas, D.J., Griffin, P.M.: Coordinated supply chain management. Europ. J. Oper. Res. 94(1), 1–15 (1996)

    Article  MATH  Google Scholar 

  31. Toth, P., Vigo, D.: The Vehicle routing problem. Society for Industrial and Applied Mathematics (2001)

    Google Scholar 

  32. Van Laarhoven, P.J.M.: Job shop scheduling by simulated annealing. Oper. Res. 40, 113 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  33. Vergara, F.E., Khouja, M., Michalewicz, Z.: An evolutionary algorithm for optimizing material flow in supply chains. Comput. Ind. Eng. 43(3), 407–421 (2002)

    Article  Google Scholar 

  34. Vidal, C.J., Goetschalckx, M.: Strategic production-distribution models: A critical review with emphasis on global supply chain models. Europ. J. Oper. Res. 98(1), 1–18 (1997)

    Article  MATH  Google Scholar 

  35. Wong, H., Kranenburg, B., van Houtum, G., Cattrysse, D.: Efficient heuristics for two-echelon spare parts inventory systems with an aggregate mean waiting time constraint per local warehouse. OR Spectrum 29(4), 699–722 (2007)

    Article  MATH  Google Scholar 

  36. Zhou, G., Min, H., Gen, M.: A genetic algorithm approach to the bi-criteria allocation of customers to warehouses. Int. J. Prod. Econ. 86(1), 35–45 (2003)

    Article  Google Scholar 

  37. Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.D.: Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proc. 2006 IEEE Congr. Evol. Comput., pp. 1857–1864 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maksud Ibrahimov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ibrahimov, M., Mohais, A., Ozols, M., Schellenberg, S., Michalewicz, Z. (2013). Advanced Planning in Vertically Integrated Wine Supply Chains. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38416-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38415-8

  • Online ISBN: 978-3-642-38416-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics