Value Efficiency in Centralized Resource Allocation with Missing Data: A Case Study of Construction Projects
Abstract
This study proposes a mathematical model for evaluating the performance of construction projects based on the Value Efficiency (EV) approach under conditions of missing data. The proposed framework integrates Data Envelopment Analysis (DEA) with missing data estimation techniques to assess the relative efficiency of Decision-Making Units (DMUs) in real-world environments characterized by incomplete information. To address missing values in undesirable outputs, two replacement strategies are employed: mean substitution and an efficiency-based scenario approach. The empirical application includes 33 construction projects affiliated with Sadra New Town Development Company in Shiraz (before 2024). The results demonstrate that the proposed model effectively distinguishes between efficient and inefficient projects and provides a scientific basis for improving Centralized Resource Allocation (CRA) decisions.
Keywords:
Value efficiency, Data envelopment analysis, Missing data, Centralized resource allocation, Construction projectsReferences
- [1] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
- [2] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
- [3] Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)–Thirty years on. European journal of operational research, 192(1), 1-17. https://doi.org/10.1016/j.ejor.2008.01.032
- [4] Mergoni, A., Emrouznejad, A., & De Witte, K. (2025). Fifty years of data envelopment analysis. European journal of operational research, 326(3), 389-412. https://doi.org/10.1016/j.ejor.2024.12.049
- [5] Banihashemi, S. A., & Khalilzadeh, M. (2021). Time-cost-quality-environmental impact trade-off resource-constrained project scheduling problem with DEA approach. Engineering, construction and architectural management, 28(7), 1979-2004. https://doi.org/10.1108/ECAM-05-2020-0350
- [6] Camanho, A. S., Silva, M. C., Piran, F. S., & Lacerda, D. P. (2024). A literature review of economic efficiency assessments using data envelopment analysis (DEA). European journal of operational research, 315(1), 1-18. https://doi.org/10.1016/j.ejor.2023.07.027
- [7] Lozano, S., & Villa, G. (2004). Centralized resource allocation using data envelopment analysis. Journal of productivity analysis, 22(1), 143-161. https://doi.org/10.1023/B:PROD.0000034748.22820.33
- [8] Asmild, M., Paradi, J. C., & Pastor, J. T. (2009). Centralized resource allocation BCC models. Omega, 37(1), 40-49. https://doi.org/10.1016/j.omega.2006.07.006
- [9] Mehdiloozad, M., Sahoo, B. K., & Roshdi, I. (2014). A generalized multiplicative directional distance function for efficiency measurement in DEA. European journal of operational research, 232(3), 679-688. https://doi.org/10.1016/j.ejor.2013.07.042
- [10] Fang, H. H., Lee, H. S., Hwang, S. N., & Chung, C. C. (2013). A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach. Omega, 41(4), 731-734. https://doi.org/10.1016/j.omega.2012.10.004
- [11] Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20. https://doi.org/10.1016/S0377-2217(01)00293-4
- [12] Färe, R., Grosskopf, S., Lovell, C. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The review of economics and statistics, 71(1),90-98. https://doi.org/10.2307/1928055
- [13] Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of economic theory, 70(2), 407-419. https://doi.org/10.1006/jeth.1996.0096
- [14] Despotis, D. K., & Smirlis, Y. G. (2002). Data envelopment analysis with imprecise data. European journal of operational research, 140(1), 24-36. https://doi.org/10.1016/S0377-2217(01)00200-4
- [15] Chen, C., Ren, J., Tang, L., & Liu, H. (2020). Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach. PloS one, 15(6), e0234247. https://doi.org/10.1371/journal.pone.0234247
- [16] Smirlis, Y. G., Maragos, E. K., & Despotis, D. K. (2006). Data envelopment analysis with missing values: An interval DEA approach. Applied mathematics and computation, 177(1), 1-10. https://doi.org/10.1016/j.amc.2005.10.028
- [17] Halme, M., Joro, T., Korhonen, P., Salo, S., & Wallenius, J. (1999). A value efficiency approach to incorporating preference information in data envelopment analysis. Management science, 45(1), 103-115. https://doi.org/10.1287/mnsc.45.1.103

