Skip to main content
SpringerLink
Log in

Impact of price-adjustments costs on integration of pricing and production planning of multiple-products

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

Joint pricing and production decisions are crucial to the competitiveness of a manufacturing company. A common assumption in coordination of pricing and production planning decisions is that the price-adjustments are costless. However, those costs are not negligible, as they may take up a significant part of the firms’ reported profit. In this paper, we consider multi-product multi-period production planning systems with costly price-adjustments. A capacitated setting is investigated and a demand-based model where the demand is a function of the price is introduced. Effective computational models will be developed for both deterministic and stochastic price dependent demand. Both fixed and variable price-adjustment costs will be considered. The aim of the paper is to utilize the existing commercial packages for optimization and compare the effectiveness of various models for addressing realistic size problems. In the case of uncertain demand function, we focus on an additive demand model for which the underlying random variable is normally distributed. By using a chance constrained programming approach, we show that the model is still solvable for realistic size problems. We also develop a robust optimization model to fit in a scenario-based demand function. Computational results on a range of test problems support the effectiveness of our models.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. AIMMS (Advanced Interactive Multidimensional Modelling System) is a software system designed for modelling and solving large-scale optimization and scheduling-type problems. It consists of an algebraic modelling language, an integrated development environment for both editing models and creating a graphical user interface around these models, and a graphical end-user environment. AIMMS is linked to multiple solvers through the AIMMS Open Solver Interface. Supported solvers include CPLEX, Gurobi, MOSEK, CBC, Conopt, MINOS, IPOPT, SNOPT and KNITRO [13].

References

  1. Elmaghraby, W., Keskinocak, P.: Dynamic pricing in the presence of inventory considerations. research overview, current practices, and future directions. Manage. Sci. 49, P1287–1309 (2003)

    Article  Google Scholar 

  2. Simchi-Levi, D., Wu, S.D., Shen, Z.J.: Modelling in the E-business Era, Handbook of Qualitative Supply Chain Analysis, pp. 335–392 (2004)

  3. Gallego, G., Van Ryzin, G.: A multiproduct dynamic pricing problem and its applications to network yield management. Oper. Res. 45(1), 24–41 (1997)

    Article  MATH  Google Scholar 

  4. Levy, D., Bergen, M., Dutta, S., Venable, R.: The magnitude of menu costs: direct evidence from large U.S. supermarket chains. Q. J. Econ. 113, 791–825 (1997)

    Article  Google Scholar 

  5. Slade, M.E.: Optimal pricing with costly adjustment: evidence from retail-grocery prices. Rev. Econ. Stud. 65, 87–107 (1998)

    Article  MATH  Google Scholar 

  6. Aguirregabiria, V.: The dynamics of mark-ups and inventories in retailing firms. Rev. Econ. Stud. 66, 275–308 (1999)

    Article  MATH  Google Scholar 

  7. Bergen, M., Dutta, S., Levy, D., Ritson, M., Zbaracki, M.: Shattering the myth of costless price-adjustments: emerging perspectives on dynamic pricing. Eur. Manage. J. 21, 663–669 (2003)

    Article  Google Scholar 

  8. Zbaracki, M.J., Ritson, M., Levy, D., Dutta, S., Bergen, M.: Managerial and customer costs of price-adjustment: direct evidence from industrial markets. Rev. Econ. Stat. 86(2), 512–533 (2004)

    Article  Google Scholar 

  9. Kano, K.: Menu costs, strategic interactions, and retail price movement, Working Paper, Queen’s University

  10. Chen, X., Hu, P.: Joint pricing and inventory management with deterministic demand and costly price-adjustment. Oper. Res. Lett. 40(5), 385–389 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chen, X., Zhou, S., Chen, F.: Integration of inventory and pricing decisions with costly price-adjustments. Oper. Res. 59(5), 1144–1158 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. Chen, X., Simchi-Levi, D.: Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: the finite horizon case. Oper. Res. 52(6), 887–896 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kallrath, J.: Modeling Languages in Mathematical Optimization. Kluwer Academic Publishing, Dordrecht (2004)

    Book  MATH  Google Scholar 

  14. Tsiddon, D.: On the stubbornness of sticky prices. Int. Econ. Rev. 32(1), 69–75 (1991)

    Article  Google Scholar 

  15. Rotemberg, J.: Monopolistic price-adjustment and aggregate output. Rev. Econ. Stud. XLIX, 517–531 (1982a)

    Article  Google Scholar 

  16. Rotemberg, J.: Sticky prices in the United States. J. Polit. Econ. 90, 1187–1211 (1982b)

    Google Scholar 

  17. Roberts, J.: Evidence on price-adjustment costs in U.S. manufacturing industry. Econ. Inquiry 30, 399–417 (1992)

    Article  Google Scholar 

  18. Li, D., Sun, X.: Nonlinear Integer Programming. Springer Science and Business Media LLC (2006)

  19. Eppen, G.D.: Effects of centralization on expected costs in a multi-location newsboy problem. Manage. Sci. 25(5), 498–501 (1979)

    Article  MATH  Google Scholar 

  20. Liao, C.J., Shyu, C.H.: An analytical determination of lead time with normal demand. Int. J. Oper. Prod. Manage. 11(9) (1991)

  21. Ben-Daya, R.M.: Inventory models involving lead time as a decision variable. J. Oper. Res. Soc. 45(5), 579–582 (1994)

    Article  MATH  Google Scholar 

  22. Rudi, N., Kapur, S., Pyke, D.F.: A two-location inventory model with transhipment and local decision making. Manage. Sci. 47(12), 1668–1680 (2001)

    Article  MATH  Google Scholar 

  23. Caccetta, L., Mardaneh, E.: Joint pricing and production planning of multi-period multi-product systems with uncertainty in demand. Pac. J. Optim. 8(1), 121–134 (2012)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Western Australian Centre of Excellence in Industrial Optimization (WACEIO), Curtin University of Australia, GPO Box U1987, Perth, WA, 6845, Australia

    Elham Mardaneh & Louis Caccetta

Authors
  1. Elham Mardaneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Louis Caccetta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Louis Caccetta.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mardaneh, E., Caccetta, L. Impact of price-adjustments costs on integration of pricing and production planning of multiple-products. Optim Lett 9, 119–142 (2015). https://doi.org/10.1007/s11590-013-0718-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-013-0718-2

Keywords

Search

Navigation