Advertisement

General Two-Stage Systems

  • Chiang Kao
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 240)

Abstract

The basic two-stage system discussed in the preceding chapter describes a production type where all intermediate products produced by the first division are consumed by the second division for production. Specifically, no intermediate products flow out of the system, and the second division does not consume other inputs supplied from outside, except for the intermediate products. However, it should be noted that real world cases are usually more complicated than this basic two-stage system. For example, some intermediate products may flow out of the system to be sold as spare parts, and the second division may need workers to work on the intermediate products to become the final products. Taking these situations into account, we then have a general two-stage system, which allows the first division to have final outputs and the second division to have exogenous inputs. Several models have been proposed for measuring the efficiency of this type of system, and many applications have been reported in the literature (Kao 2014a).

References

  1. Abad C, Thore SA, Laffarga J (2004) Fundamental analysis of stock by two-stage DEA. Manag Decis Econ 25:231–241CrossRefGoogle Scholar
  2. Amirteimoori A (2013) A DEA two-stage decision processes with shared resources. CEJOR 21:141–151CrossRefGoogle Scholar
  3. Avkiran NK, McCrystal A (2012) Sensitivity analysis of network DEA: NSBM versus NRAM. Appl Math Comput 218:11226–11239Google Scholar
  4. Bichou K (2011) A two-stage supply chain DEA model for measuring container-terminal efficiency. IJSTL 3:6–26CrossRefGoogle Scholar
  5. Charnes A, Cooper WW (1962) Programming with linear fractionals. Nav Res Logist Q 9:181–186CrossRefGoogle Scholar
  6. Charnes A, Cooper WW, Golany B, Halek R, Klopp G, Schmitz E, Thomas D (1986) Two-phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: tradeoffs between joint services and army advertising. Research Report CCS #532. Center forCybernetic Studies, University of Texas-Austin, Austin, TXGoogle Scholar
  7. Chen PC, Chang CC, Yu MM, Hsu SH (2012) Performance measurement for incineration plants using multi-activity network data envelopment analysis: the case of Taiwan. J Environ Manag 93:95–103CrossRefGoogle Scholar
  8. Chen Y, Cook WD, Li N, Zhu J (2009) Additive efficiency decomposition in two-stage DEA. Eur J Oper Res 196:1170–1176CrossRefGoogle Scholar
  9. Chen Y, Du J, Sherman HD, Zhu J (2010) DEA model with shared resources and efficiency decomposition. Eur J Oper Res 207:339–349CrossRefGoogle Scholar
  10. Chen Y, Liang L, Yang F, Zhu J (2006) Evaluation of information technology investment: a data envelopment analysis approach. Comput Oper Res 33:1368–1379CrossRefGoogle Scholar
  11. Chen Y, Zhu J (2004) Measuring information technology’s indirect impact on firm performance. Inf Technol Manag 5:9–22CrossRefGoogle Scholar
  12. Chiu YH, Huang CW, Chen YC (2012) The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pac J Manag 29:989–1006CrossRefGoogle Scholar
  13. Chiu YH, Huang CW, Ma CM (2011a) Assessment of China transit and economic efficiencies in a modified value-chains DEA model. Eur J Oper Res 209:95–103CrossRefGoogle Scholar
  14. Chiu YH, Huang CW, Ting CT (2011b) Measuring the repair performance for stricken cultivated land and agricultural efficiency in China with a modified two-stage DEA model. Asia Pac J Oper Res 28:633–649CrossRefGoogle Scholar
  15. Fang L, Zhang CQ (2008) Resource allocation based on the DEA model. J Oper Res Soc 59:1136–1141CrossRefGoogle Scholar
  16. Färe R, Grabowski R, Grosskopf S, Kraft S (1997) Efficiency of a fixed but allocatable input: a non-parametric approach. Econ Lett 56:187–193CrossRefGoogle Scholar
  17. Färe R, Grosskopf S (1996) Productivity and intermediate products: a frontier approach. Econ Lett 50:65–70CrossRefGoogle Scholar
  18. Färe R, Whittaker G (1995) An intermediate input model of dairy production using complex survey data. J Agric Econ 46:201–213CrossRefGoogle Scholar
  19. Golany B, Hackman ST, Passy U (2006) An efficiency measurement framework for multi-stage production systems. Ann Oper Res 145:51–68CrossRefGoogle Scholar
  20. Guan J, Chen K (2012) Modeling the relative efficiency of national innovation systems. Res Policy 41:102–115CrossRefGoogle Scholar
  21. Guan J, Zuo K (2014) A cross-country comparison of innovation efficiency. Scientometrics 100:541–575CrossRefGoogle Scholar
  22. Hampf B (2014) Separating environmental efficiency into production and abatement efficiency: a nonparametric model with application to US power plants. J Prod Anal 41:457–473CrossRefGoogle Scholar
  23. Kao C (2009) Efficiency decomposition in network data envelopment analysis: a relational model. Eur J Oper Res 192:949–962CrossRefGoogle Scholar
  24. Kao C (2014a) Network data envelopment analysis: a review. Eur J Oper Res 239:1–16CrossRefGoogle Scholar
  25. Kao C (2014b) Efficiency decomposition in network data envelopment analysis with slacks-based measure. Omega 45:1–6CrossRefGoogle Scholar
  26. Kao C, Hwang SN (2008) Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur J Oper Res 185:418–429CrossRefGoogle Scholar
  27. Kao C, Hwang SN (2010) Efficiency measurement for network systems: IT impact on firm performance. Decis Support Syst 48:437–446CrossRefGoogle Scholar
  28. Khalili-Damghani K, Shahmir Z (2015) Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Comput Ind Eng 88:131–150CrossRefGoogle Scholar
  29. Li Y, Chen Y, Liang L, Xie JH (2012) DEA models for extended two-stage network structures. Omega 40:611–618CrossRefGoogle Scholar
  30. Liang L, Li ZQ, Cook WD (2011) Data envelopment analysis efficiency in two-stage networks with feedback. IIE Trans 43:309–322CrossRefGoogle Scholar
  31. Liang L, Yang F, Cook WD, Zhu J (2006) DEA models for supply chain efficiency evaluation. Ann Oper Res 145:35–49CrossRefGoogle Scholar
  32. Liu JS, Lu WM, Ho MHC (2015) National characteristics: innovation systems from the process efficiency perspective. R&D Manag 45:317–338CrossRefGoogle Scholar
  33. Löthgren M, Tambour M (1999) Productivity and customer satisfaction in Swedish pharmacies: a DEA network model. Eur J Oper Res 115:449–458CrossRefGoogle Scholar
  34. Lozano S, Gutierrez E (2014) A slacks-based network DEA efficiency analysis of European airlines. Transp Plan Technol 37:623–637CrossRefGoogle Scholar
  35. Lozano S, Gutierrez E, Moreno P (2013) Network DEA approach to airports performance assessment considering undesirable outputs. Appl Math Model 37:1665–1676CrossRefGoogle Scholar
  36. Lu WM, Liu JS, Kweh QL, Wang CW (2016) Exploring the benchmarks of the Taiwanese investment trust corporations: management and investment efficiency perspectives. Eur J Oper Res 248:607–618CrossRefGoogle Scholar
  37. Maghbouli M, Amirteimoori A, Kordrostami S (2014) Two-stage network structures with undesirable outputs: a DEA based approach. Measurement 48:109–118CrossRefGoogle Scholar
  38. Premachandra IM, Zhu J, Watson J, Galagedera DUA (2012) Best-performing US mutual fund families from 1993 to 2008: evidence from a novel two-stage DEA model for efficiency decomposition. J Bank Financ 36:3302–3317CrossRefGoogle Scholar
  39. Simon J, Simon C, Arias A (2011) Changes in productivity of Spanish university libraries. Omega 39:578–588CrossRefGoogle Scholar
  40. Song M, Zhang J, Wang S (2015) Review of the network environmental efficiencies of listed petroleum enterprises in China. Renew Sustain Energy Rev 43:65–71CrossRefGoogle Scholar
  41. Soteriou A, Zenios SA (1999) Operations, quality, and profitability in the provision of banking services. Manag Sci 45:1221–1238CrossRefGoogle Scholar
  42. Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509CrossRefGoogle Scholar
  43. Tone K, Tsutsui M (2009) Network DEA: a slacks-based approach. Eur J Oper Res 197:243–252CrossRefGoogle Scholar
  44. Tone K, Tsutsui M (2010) Dynamic DEA: a slacks-based measure approach. Omega 38:3–4CrossRefGoogle Scholar
  45. Wu DD, Birge JR (2012) Serial chain merger evaluation model and application to mortgage banking. Decis Sci 43:5–35CrossRefGoogle Scholar
  46. Wu J, Lin LV, Sun J, Ji X (2015) A comprehensive analysis of China’s regional energy saving and emission reduction efficiency: from production and treatment perspectives. Energ Pol 84:166–176CrossRefGoogle Scholar
  47. Xie BC, Fan Y, Qu QQ (2012) Does generation form influence environmental efficiency performance? An analysis of China’s power system. Appl Energy 96:261–271CrossRefGoogle Scholar
  48. Yang CC (2009) Productive efficiency, environmental efficiency and their determinants in farrow-to-finish pig farming in Taiwan. Livest Sci 126:195–205CrossRefGoogle Scholar
  49. Yang CC, Hsiao CK, Yu MM (2008) Technical efficiency and impact of environmental regulations in farrow-to-finish swine production in Taiwan. Agric Econ 39:51–61CrossRefGoogle Scholar
  50. Yu MM (2008) Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world’s railways through NDEA analysis. Transport Res Pol Pract 42:1283–1294CrossRefGoogle Scholar
  51. Yu MM, Chen PC (2011) Measuring air routes performance using a fractional network data envelopment analysis model. CEJOR 19:81–98CrossRefGoogle Scholar
  52. Yu MM, Lee BCY (2009) Efficiency and effectiveness of service business: evidence from international tourist hotels in Taiwan. Tour Manag 30:571–580CrossRefGoogle Scholar
  53. Yuan XC, Tang BJ, Wei YM (2015) China’s regional drought risk under climate change: a two-stage process assessment approach. Nat Hazards 76:667–684CrossRefGoogle Scholar
  54. Zha Y, Liang L (2010) Two-stage cooperation model with input freely distributed among the stages. Eur J Oper Res 205:332–338CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Chiang Kao
    • 1
  1. 1.Department of Industrial and Information ManagementNational Cheng Kung UniversityTainanTaiwan

Personalised recommendations