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Product Line Design and Scheduling at Intel

Author

Listed:
  • Evan Rash

    (Decision Engineering Group, Intel Corporation, Santa Clara, California 95054)

  • Karl Kempf

    (Decision Engineering Group, Intel Corporation, Chandler, Arizona 85226)

Abstract

We develop a holistic and coherent model for the product line design and scheduling problem. Our model incorporates market requirements and financials, design-engineering capabilities, manufacturing costs, and multiple-time dynamics. The solution integrates techniques and concepts from optimal set-covering, resource-constrained job scheduling, dynamic programming, and portfolio optimization to maximize overall profit. The key concept is the decomposition of the problem into two layers. The outer genetic algorithm layer handles resource constraints, scheduling, and financial optimization. The inner mathematical programming layer optimizes product composition as classical set covering. The resulting algorithm efficiently solves problems of larger size and higher complexity than previously possible. Over 250 personnel representing most major Intel groups and many distinct job functions are using the decision support suite (DSS) surrounding the algorithm 21 months after its initial deployment. This DSS serves to integrate a set of previously separate noncommunicating business processes.

Suggested Citation

  • Evan Rash & Karl Kempf, 2012. "Product Line Design and Scheduling at Intel," Interfaces, INFORMS, vol. 42(5), pages 425-436, October.
    • Handle: RePEc:inm:orinte:v:42:y:2012:i:5:p:425-436
    DOI: 10.1287/inte.1120.0641
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    References listed on IDEAS

    as
    1. Alexandre Belloni & Robert Freund & Matthew Selove & Duncan Simester, 2008. "Optimizing Product Line Designs: Efficient Methods and Comparisons," Management Science, INFORMS, vol. 54(9), pages 1544-1552, September.
    2. Albritton, M. David & McMullen, Patrick R., 2007. "Optimal product design using a colony of virtual ants," European Journal of Operational Research, Elsevier, vol. 176(1), pages 498-520, January.
    3. Rajeev Kohli & R. Sukumar, 1990. "Heuristics for Product-Line Design Using Conjoint Analysis," Management Science, INFORMS, vol. 36(12), pages 1464-1478, December.
    4. Kohli, Rajeev & Krishnamurti, Ramesh, 1989. "Optimal product design using conjoint analysis: Computational complexity and algorithms," European Journal of Operational Research, Elsevier, vol. 40(2), pages 186-195, May.
    5. Paul E. Green & Abba M. Krieger, 1985. "Models and Heuristics for Product Line Selection," Marketing Science, INFORMS, vol. 4(1), pages 1-19.
    6. Richard D. McBride & Fred S. Zufryden, 1988. "An Integer Programming Approach to the Optimal Product Line Selection Problem," Marketing Science, INFORMS, vol. 7(2), pages 126-140.
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    Cited by:

    1. Rahman Khorramfar & Osman Y. Özaltın & Karl G. Kempf & Reha Uzsoy, 2022. "Managing Product Transitions: A Bilevel Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2828-2844, September.
    2. Rahman Khorramfar & Osman Ozaltin & Reha Uzsoy & Karl Kempf, 2024. "Coordinating Resource Allocation during Product Transitions Using a Multifollower Bilevel Programming Model," Papers 2401.17402, arXiv.org.
    3. John Heiney & Ryan Lovrien & Nicholas Mason & Irfan Ovacik & Evan Rash & Nandini Sarkar & Harry Travis & Zhenying Zhao & Kalani Ching & Shamin Shirodkar & Karl Kempf, 2021. "Intel Realizes $25 Billion by Applying Advanced Analytics from Product Architecture Design Through Supply Chain Planning," Interfaces, INFORMS, vol. 51(1), pages 9-25, February.

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