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Efficient Resource Allocation via Efficiency Bootstraps: An Application to R&D Project Budgeting

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  • Chien-Ming Chen

    (Nanyang Business School, Nanyang Technological University, Singapore 639798)

  • Joe Zhu

    (Department of Management, School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts 01609)

Abstract

Resource allocation decisions are crucial for the success of an organization. This paper proposes an integrated approach to resource allocation problems, in which decision makers have one observation of the multiple input-output criteria of candidates. We offer important improvements over existing approaches based on the widely used data envelopment analysis (DEA), which has two major limitations in its application to resource allocation. First, traditional DEA models compute efficiency scores by optimizing firm-specific shadow prices of inputs and outputs. This could be problematic, because in practice stakeholders would usually require unanimously agreed-upon trade-offs among evaluation criteria. Second, previous allocation approaches based on DEA do not allow for controlling the risk exposure of allocation portfolios. To tackle these problems, we propose an efficiency measure based on equilibrium shadow prices of different criteria, and we use the bootstrap efficiency distributions to gather information regarding efficiency variations and correlations. Through our methodology, decision makers can obtain the risk-minimizing allocation portfolio. We illustrate the proposed approach through an empirical R&D project budgeting problem in which we allocate funding according to the projects efficiency distributions.

Suggested Citation

  • Chien-Ming Chen & Joe Zhu, 2011. "Efficient Resource Allocation via Efficiency Bootstraps: An Application to R&D Project Budgeting," Operations Research, INFORMS, vol. 59(3), pages 729-741, June.
  • Handle: RePEc:inm:oropre:v:59:y:2011:i:3:p:729-741
    DOI: 10.1287/opre.1110.0920
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    Cited by:

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    3. Kairui Zuo & Jiancheng Guan, 2017. "Measuring the R&D efficiency of regions by a parallel DEA game model," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 175-194, July.
    4. Afsharian, Mohsen & Ahn, Heinz & Thanassoulis, Emmanuel, 2017. "A DEA-based incentives system for centrally managed multi-unit organisations," European Journal of Operational Research, Elsevier, vol. 259(2), pages 587-598.
    5. Zhou, P. & Sun, Z.R. & Zhou, D.Q., 2014. "Optimal path for controlling CO2 emissions in China: A perspective of efficiency analysis," Energy Economics, Elsevier, vol. 45(C), pages 99-110.
    6. Yuichi Takano & Nobuaki Ishii & Masaaki Muraki, 2017. "Multi-period resource allocation for estimating project costs in competitive bidding," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 303-323, June.
    7. Lim, Sungmook & Oh, Kwang Wuk & Zhu, Joe, 2014. "Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market," European Journal of Operational Research, Elsevier, vol. 236(1), pages 361-368.
    8. M. Khodabakhshi & K. Aryavash, 2014. "The fair allocation of common fixed cost or revenue using DEA concept," Annals of Operations Research, Springer, vol. 214(1), pages 187-194, March.
    9. Ding, Jingjing & Dong, Wei & Liang, Liang & Zhu, Joe, 2017. "Goal congruence analysis in multi-Division Organizations with shared resources based on data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 263(3), pages 961-973.
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    11. Liesiö, Juuso & Salo, Ahti & Keisler, Jeffrey M. & Morton, Alec, 2021. "Portfolio decision analysis: Recent developments and future prospects," European Journal of Operational Research, Elsevier, vol. 293(3), pages 811-825.

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