Process Systems Engineering

Multiechelon supply chain planning with sequence-dependent changeovers and price elasticity of demand under uncertainty

  1. Songsong Liu1,
  2. Nilay Shah2,
  3. Lazaros G. Papageorgiou1,*

Article first published online: 1 FEB 2012

DOI: 10.1002/aic.13732

Issue

AIChE Journal

AIChE Journal

Volume 58, Issue 11, pages 3390–3403, November 2012

Additional Information

How to Cite

Liu, S., Shah, N. and Papageorgiou, L. G. (2012), Multiechelon supply chain planning with sequence-dependent changeovers and price elasticity of demand under uncertainty. AIChE J., 58: 3390–3403. doi: 10.1002/aic.13732

Author Information

  1. 1

    Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.

  2. 2

    Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, U.K.

*Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.

Publication History

  1. Issue published online: 5 OCT 2012
  2. Article first published online: 1 FEB 2012
  3. Accepted manuscript online: 9 JAN 2012 09:43AM EST
  4. Manuscript Revised: 10 DEC 2011
  5. Manuscript Received: 24 MAR 2011

Funded by

  • Overseas Research Students Award Scheme (ORSAS)
  • Centre for Process Systems Engineering (CPSE)

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Keywords:

  • supply chain management;
  • price elasticity of demand;
  • inventory control;
  • pricing;
  • model predictive control;
  • sequence-dependent changeovers

Abstract

An optimization framework is proposed for a multiechelon multiproduct process supply chain planning under demand uncertainty considering inventory deviation and price fluctuation. In this problem, the sequence-dependent changeovers occur at the production plants, and price elasticity of demand is considered at the markets. Based on the classic formulation of travelling salesman problem (TSP), a mixed-integer liner programming (MILP) is developed, whose objective function considers the profit, inventory deviations from the desired trajectories and price changes simultaneously. Model predictive control (MPC) approach is adopted to tackle the uncertain issues, as well as the inventory and price maintenance. The applicability of the proposed model and approach was illustrated by solving a supply chain example. Some issues, including length of the control horizon, price elasticity of demand, weights, inventory trajectories, and changeovers, are discussed based on the computational results. © 2012 American Institute of Chemical Engineers AIChE J, 2012

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