Skip to main content

Integrated Production Planning and Pricing Decisions in Congestion-Prone Capacitated Production Systems

  • Chapter
  • First Online:
Essays in Production, Project Planning and Scheduling

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 200))

  • 2011 Accesses

Abstract

In many industries both the pricing of a product and its delivery lead time to the customer are significant factors affecting demand. However, it is well known from queuing models that the lead time increases nonlinearly with the utilization of capacitated resources. Hence when customer demand is sensitive to delivery lead times, firms must take a broader view: A large reduction in price may increase demand to the point that it cannot be met in a timely manner with available capacity, which can adversely affect customer satisfaction and reduce future sales.

In this chapter we develop an integrated model for dynamic pricing and production planning for a single product under workload-dependent lead times. This allows us to capture interactions between pricing, sales and lead times that have generally not been considered in the past. We present numerical examples to demonstrate these interactions, as well as analytical results showing that the proposed model behaves quite differently from conventional models that ignore congestion.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

References

  • Adida, E., & Perakis, G. (2006). A robust optimization approach to dynamic pricing and inventory control with no backorders. Mathematical Programming Series B, 107, 97–129.

    Google Scholar 

  • Adida, E., & Perakis, G. (2010). Dynamic pricing and inventory control: robust vs. stochastic uncertainty models: A computational study. Annals of Operations Research, 181, 125–157.

    Google Scholar 

  • Agnew, C. (1976). Dynamic modeling and control of some congestion prone systems. Operations Research, 24(3), 400–419.

    Google Scholar 

  • Ahn, H., Gumus, M., & Kaminsky, P. (2007). Pricing and manufacturing decisions when demand is a function of prices in multiple periods. Operations Research, 55(6), 1039–1057.

    Google Scholar 

  • Akcali, E., Nemoto, K., & Uzsoy, R. (2000). Quantifying the benefits of cycle-time reduction in semiconductor wafer fabrication. IEEE Transactions on Electronics Packaging Manufacturing, 23, 39–47.

    Google Scholar 

  • Allison, R. A. H., Yu, J., Tsai, L. H., Liu, C., Drummond, M., Kayton, D., Sustae, T., & Witte, J. (1997). Macro model development as a bridge between factory level simulation and LP enterprise systems. IEEE/CPMT International Electronics Manufacturing Technology Symposium: 408–416.

    Google Scholar 

  • Asmundsson, J. M., Rardin, R. L., Turkseven, C. H., & Uzsoy, R. (2009). Production planning models with resources subject to congestion. Naval Research Logistics, 56, 142–157.

    Google Scholar 

  • Asmundsson, J. M., Rardin, R. L., & Uzsoy, R. (2006). Tractable nonlinear production planning models for semiconductor wafer fabrication facilities. IEEE Transactions on Semiconductor Manufacturing, 19, 95–111.

    Google Scholar 

  • Boyaci, T., & Ray, S. (2003). Product differentiation and capacity cost interaction in time and price sensitive markets. Manufacturing and Service Operations Management, 5(1), 18–36.

    Google Scholar 

  • Buzacott, J. A., & Shanthikumar, J. G. (1993). Stochastic models of manufacturing systems. Englewood Cliffs, NJ, Prentice-Hall.

    Google Scholar 

  • Charnsirisakskul, K., Griffin, P., & Keskinocak, P. (2004). Order selection and scheduling with leadtime flexibility. IIE Transactions, 36, 697–707.

    Google Scholar 

  • Charnsirisakskul, K., Griffin, P., & Keskinocak, P. (2006). Pricing and scheduling decisions with leadtime flexibility. European Journal of Operational Research, 171, 153–169.

    Google Scholar 

  • Chatterjee, S., Slotnick, S. A., & Sobel, M. J. (2002). Delivery guarantees and the interdependence of marketing and operations. Production and Operations Management, 11(3), 393–410.

    Google Scholar 

  • Chen, Z. L., & Hall, N. G. (2010). The coordination of pricing and scheduling decisions. Manufacturing and Service Operations Management, 12(1), 77–92.

    Google Scholar 

  • de Kok, A. G., & Fransoo, J. C. (2003). Planning supply chain operations: definition and comparison of planning concepts. OR Handbook on supply chain management. A. G. de Kok & S. C. Graves (597–675). Amsterdam: Elsevier.

    Google Scholar 

  • Dellaert, N. P. (1991). Due date setting and production control. International Journal of Production Economics, 23, 59–67.

    Google Scholar 

  • Deng, S., & Yano, C. A. (2006). Joint production and pricing decisions with setup costs and capacity constraints. Management Science, 52, 741–756.

    Google Scholar 

  • Donohue, K. L. (1994). The economics of capacity and marketing measures in a simple manufacturing environment. Production and Operations Management, 3(2), 78–99.

    Google Scholar 

  • Duenyas, I. (1995). Single facility due date setting with multiple customer classes. Management Science, 41(4), 608–619.

    Google Scholar 

  • Duenyas, I., & Hopp, W. J. (1995). Quoting customer lead times. Management Science, 41, 608–619.

    Google Scholar 

  • Easton, F. F., & Moodie, D. R. (1999). Pricing and lead time decisions for make-to-order firms with contingent orders. European Journal of Operational Research, 116, 305–318.

    Google Scholar 

  • Elhafsi, M. (2000). An operational decision model for lead-time and price quotation in congested manufacturing systems. European Journal of Operational Research, 126, 355–370.

    Google Scholar 

  • Elhafsi, M., & Rolland, E. (1999). Negotiating price/delivery date in a stochastic manufacturimg environment. IIE Transactions, 31, 255–270.

    Google Scholar 

  • Eliashberg, J., & Steinberg, R. (1991). Marketing-production joint decision-making. Management science in marketing, handbooks in operations research and management science. J. Eliashberg and J. D. Lilien, North Holland: 827–880.

    Google Scholar 

  • Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices and future directions. Management Science, 49(10), 1287–1309.

    Google Scholar 

  • Graves, S. C. (1986). A tactical planning model for a job shop. Operations Research, 34, 552–533.

    Google Scholar 

  • Hackman, S. T., & Leachman, R. C. (1989). A general framework for modeling production. Management Science, 35, 478–495.

    Google Scholar 

  • Hopp, W. J., & Spearman, M. L. (2001). Factory physics: Foundations of manufacturing management. Boston, Irwin/McGraw-Hill.

    Google Scholar 

  • Johnson, L. A., & Montgomery, D. C. (1974). Operations research in production planning, scheduling and inventory control. New York: John Wiley.

    Google Scholar 

  • Kacar, N. B., & Uzsoy, R. (2010). Estimating clearing functions from simulation data. Winter Simulation Conference. B. Johansson, Jain, S., Montoya-Torres, J., Hugan, J., Yucesan, E. Baltimore, MD.

    Google Scholar 

  • Karmarkar, U. S. (1989). Capacity loading and release planning with work-in-progress (WIP) and lead-times. Journal of Manufacturing and Operations Management, 2(105-123).

    Google Scholar 

  • Kefeli, A., Uzsoy, R., Fathi, Y., & Kay, M. (2011). Using a mathematical programming model to examine the marginal price of capacitated resources. International Journal of Production Economics, 131(1), 383–391.

    Google Scholar 

  • Keskinocak, P., & Tayur, S. (2004). Due-date management policies. In D. Simchi-Levi, S. D. Wu, & Z. M. Shen (Eds.), Supply chain analysis in the e-business era: Handbook of quantitative supply chain analysis. Kluwer Academic Publishers.

    Google Scholar 

  • Leachman, R. C., & Ding, S. (2007). Integration of speed economics into decision-making for manufacturing management. International Journal of Production Economics, 107, 39–55.

    Google Scholar 

  • Liu, L. M., Parlar, M., & Zhu, S. X. (2007). Pricing and lead time decisions in decentralized supply chains. Management Science, 53(5), 713–725.

    Google Scholar 

  • Low, D. W. (1974). Optimal dynamic pricing policies for an M/M/s queue. Operations Research, 22, 545–561.

    Google Scholar 

  • Medhi, J. (1991). Stochastic models in queuing theory. Academic Press.

    Google Scholar 

  • Missbauer, H. (2009). Models of the transient behaviour of production units to optimize the aggregate material flow. International Journal of Production Economics, 118(2), 387–397.

    Google Scholar 

  • Missbauer, H., & Uzsoy, R. (2010). Optimization models for production planning. Planning production and inventories in the extended enterprise: A state of the art handbook. K. G. Kempf, P. Keskinocak and R. Uzsoy (437–508). New York: Springer.

    Google Scholar 

  • Orcun, S., Uzsoy, R., & Kempf, K. G. (2006). Using system dynamics simulations to compare capacity models for production planning. Winter Simulation Conference. Monterey, CA.

    Google Scholar 

  • Pahl, J., Voss, S., & Woodruff, D. L. (2005). Production planning with load dependent lead times. 4OR: A Quarterly Journal of Operations Research, 3, 257–302.

    Google Scholar 

  • Pahl, J., Voss, S., & Woodruff, D. L. (2007). Production planning with load dependent lead times: An update of research. Annals of Operations Research, 153, 297–345.

    Google Scholar 

  • Palaka, K., Erlebacher, S., & Kropp, D. H. (1998). Lead-time setting capacity utilization, and pricing decisions under lead-time dependent demand. IIE Transactions, 30, 151–163.

    Google Scholar 

  • Pekgun, P., Griffin, P. M., & Keskinocak, P. (2008). Coordination of marketing and production for price and leadtime decisions. IIE Transactions, 40(1), 12–30.

    Google Scholar 

  • Plambeck, E. L. (2004). Optimal leadtime differentiation via diffusion approximation. Operations Research, 52(2), 213–228.

    Google Scholar 

  • Ray, S., & Jewkes, E. M. (2004). Customer lead time management when both demand and price are lead time sensitive. European Journal of Operational Research, 153, 769–781.

    Google Scholar 

  • Selçuk, B., Fransoo, J. C., & de Kok, A. G. (2007). Work in process clearing in supply chain operations planning. IIE Transactions, 40, 206–220.

    Google Scholar 

  • So, K. C., & Song, J.-S. (1998). Price, delivery time guarantees and capacity selection. European Journal of Operational Research, 111, 28–49.

    Google Scholar 

  • Spearman, M. L. (1991). An analytic congestion model for closed production systems with ifr processing times. Management Science, 37(8), 1015–1029.

    Google Scholar 

  • Spitter, J. M., A. G. de Kok and N. P. Dellaert (2005a). Timing production in LP models in a rolling schedule. International Journal of Production Economics, 93–94, 319–329.

    Google Scholar 

  • Spitter, J. M., Hurkens, C. A. J., de Kok, A. G., Lenstra, J. K., & Negenman, E. G. (2005b). Linear programming models with planned lead times for supply chain operations planning. European Journal of Operational Research, 163, 706–720.

    Google Scholar 

  • Srinivasan, A., Carey, M., & Morton, T. E. (1988). Resource pricing and aggregate scheduling in manufacturing systems. Graduate School of Industrial Administration, Carnegie-Mellon University. Pittsburgh, PA

    Google Scholar 

  • Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. New York: McGraw-Hill.

    Google Scholar 

  • Swann, J. L. (2001). Dynamic pricing models to improve supply chain performance. Department of Industrial Engineering and Management Sciences. Evanston, IL 60601, Northwestern University.

    Google Scholar 

  • Tardif, V., & Spearman, M. L. (1997). Diagnostic scheduling in finite-capacity production environments. Computers and Industrial Engineering, 32, 867–878.

    Google Scholar 

  • Upasani, A., & Uzsoy, R. (2008). Incorporating manufacturing lead times in joint production-marketing models: A review and further directions. Annals of Operations Research, 161, 171–188.

    Google Scholar 

  • Webster, S. (2002). Dynamic pricing and lead time policies for make to order systems. Decision Sciences, 33(4), 579–599.

    Google Scholar 

  • Yano, C. A., & Gilbert, S. M. (2003). Coordinated pricing and production/procurement decisions: A review. Managing business interfaces: Marketing, engineering and manufacturing perspectives. A. Charkarvarty and J. Eliashberg, Kluwer Academic Publishers: 65–103.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reha Uzsoy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Upasani, A., Uzsoy, R. (2014). Integrated Production Planning and Pricing Decisions in Congestion-Prone Capacitated Production Systems. In: Pulat, P., Sarin, S., Uzsoy, R. (eds) Essays in Production, Project Planning and Scheduling. International Series in Operations Research & Management Science, vol 200. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-9056-2_3

Download citation

Publish with us

Policies and ethics