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Multi-Product Inventory Logistics Modeling in the Process Industries

  • Danielle Zyngier
  • Jeffrey D. Kelly
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 30)

Summary

In this chapter the mathematical modeling of several types of inventories are detailed. The inventory types are classified as batch processes, pools, pipe lines, pile lines and parcels. The key construct for all inventory models is the “fill-hold/haul-draw” fractal found in all discontinuous inventory or holdup modeling. The equipment, vessel or unit must first be “filled” unless enough product is already held in the unit. Product can then be “held” or “hauled” for a definite (fixed) or indefinite (variable) amount of time and then “drawn” out of the unit when required. Mixed-integer linear programming (MILP) modeling formulations are presented for five different types of logistics inventory models which are computationally efficient and can be readily applied to industrial decision-making problems.

Keywords

Inventory Model Batch Process Logic Variable Outlet Port Inlet Port 
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.

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Refereneces

  1. 1.
    Hopp W, Spearman M (2001) Factory physics. Mc-Graw Hill, New YorkGoogle Scholar
  2. 2.
    Kelly JD (2005) The unit-operation-stock superstructure (UOSS) and the quantity-logic-quality paradigm (QLQP) for production scheduling in the Process Industries. In MISTA 2005 Conference Proceedings 327–333Google Scholar
  3. 3.
    Kondili E, Pantelides CC, Sargent RWH (1993) A general algorithm for short-term scheduling of batch operations - I MILP formulation. Comp Chem Eng 17:211–227CrossRefGoogle Scholar
  4. 4.
    Kelly JD, Zyngier D (2007) A projectional model of data for complexity management of enterprise-wide optimization models. Submitted to Comp Chem EngGoogle Scholar
  5. 5.
    Realff MJ, Shah N, Pantelides CC (1996) Simultaneous design, layout and scheduling of pipeless batch plants. Comp Chem Eng 20:869–883CrossRefGoogle Scholar
  6. 6.
    Van den Akker JM, Hurkens CAJ, Savelsbergh MWP (2000) Time-indexed formulations for single-machine scheduling problems: Column generation. INFORMS J Comput 12:111–124MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Lee K-H, Park HI, Lee I-B (2001) A novel nonuniform discrete time formulation for short-term scheduling of batch and continuous processes. Ind Eng Chem Res 40:4902–4911CrossRefGoogle Scholar
  8. 8.
    Wolsey LA (1998) Integer programming. John Wiley and Sons, Ltd., New YorkGoogle Scholar
  9. 9.
    Prasad P, Maravelias CT, Kelly JD (2006) Optimization of aluminum smelter casthouse operations. Ind Eng Chem Res 45:7603–7617CrossRefGoogle Scholar
  10. 10.
    Jain V, Grossmann IE (1998) Cyclic scheduling of continuous parallel-process units with decaying performance. AIChE J 44:1623–1636CrossRefGoogle Scholar
  11. 11.
    Georgiadis MC, Papageorgiou LG, Macchietto S (2000) Optimal cleaning policies in heat exchanger networks under rapid fouling. Ind Eng Chem Res 39:441–454CrossRefGoogle Scholar
  12. 12.
    Savelsbergh MWP (1994) Preprocessing and probing techniques for mixed integer programming problems. ORSA J Comput 6:445–454MATHMathSciNetGoogle Scholar
  13. 13.
    Relvas S, Matos HA, Barbosa-Povoa APFD, Fialho J, Pinheiro AS (2006) Pipeline scheduling and inventory management of a multiproduct distribution oil system. Ind Eng Chem Res 45:7841–7855CrossRefGoogle Scholar
  14. 14.
    Rejowski R, Pinto JM (2003) Scheduling of a multiproduct pipeline system. Comp Chem Eng 27:1229–1246CrossRefGoogle Scholar
  15. 15.
    Rejowski R, Pinto JM (2004) Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Comp Chem Eng 28:1511–1528Google Scholar
  16. 16.
    Cafaro DC, Cerda J (2004) Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation. Comp Chem Eng 28:2053–2068CrossRefGoogle Scholar
  17. 17.
    Hane CA, Ratliff HD (1995) Sequencing inputs to multi-commodity pipelines. Ann Oper Res 57:73–101MATHCrossRefGoogle Scholar
  18. 18.
    Kelly JD, Zyngier D (2007) An improved MILP modeling of sequence-dependent switchovers for discrete-time scheduling problems. Ind Eng Chem Res 46:4964–4973CrossRefGoogle Scholar
  19. 19.
    Sahinidis NV, Grossmann IE (1991) MINLP model for cyclic multiproduct scheduling on continuous parallel lines. Comp Chem Eng 15:85–103CrossRefGoogle Scholar
  20. 20.
    Wolsey LA (1997) MIP modelling of changeovers in production planning and scheduling problems. Eur J Oper Res 99:154–165MATHCrossRefGoogle Scholar
  21. 21.
    Williams HP (1999) Model building in mathematical programming. John Wiley and Sons, Ltd., 4th ed, West Sussex, EnglandGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Honeywell Process SolutionsMarkhamCanada

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