IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v73y2014icp762-770.html
   My bibliography  Save this article

Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility

Author

Listed:
  • Taghavifar, Hamid
  • Mardani, Aref
  • Karim-Maslak, Haleh

Abstract

The main objective of the present study was to determine the energy use efficiency of soil-wheel interaction in a soil bin testing facility. Input parameters were velocity at three levels (i.e. 0.7, 1.4 and 2 m/s), tire inflation pressure at three levels (i.e. 100, 200 and 300 kPa) and wheel load at five levels (i.e. 1, 2, 3, 4, and 5 kN) where the output was the energy waste of soil-tire interface. The potential of nonparametric technique of DEA (data envelopment analysis) and hybrid statistical-mathematical modeling approach of RSM (response surface methodology) were assessed in the present investigation. Response surface contours were constructed to determine the optimum conditions for the objective parameter. The present investigation spearheads the practice of DEA and RSM approaches in the optimization of energy waste of off-road vehicles. The findings revealed that RSM with optimized value of 5.7175 J, which corresponds to wheel load of 1 kN, velocity of 0.7 m/s, and tire inflation pressure of 150 kPa, is achievable. Additionally, input-oriented option of DEA resulted in the mean efficiency of 0.4379. Moreover, contribution of each input factor for energy saving was assessed by DEA approach.

Suggested Citation

  • Taghavifar, Hamid & Mardani, Aref & Karim-Maslak, Haleh, 2014. "Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility," Energy, Elsevier, vol. 73(C), pages 762-770.
    • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:762-770
    DOI: 10.1016/j.energy.2014.06.081
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214007804
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2014.06.081?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Taghavifar, Hamid & Mardani, Aref, 2014. "Analyses of energy dissipation of run-off-road wheeled vehicles utilizing controlled soil bin facility environment," Energy, Elsevier, vol. 66(C), pages 973-980.
    2. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    3. Gerdessen, Johanna C. & Pascucci, Stefano, 2013. "Data Envelopment Analysis of sustainability indicators of European agricultural systems at regional level," Agricultural Systems, Elsevier, vol. 118(C), pages 78-90.
    4. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    5. Menegaki, Angeliki N., 2013. "Growth and renewable energy in Europe: Benchmarking with data envelopment analysis," Renewable Energy, Elsevier, vol. 60(C), pages 363-369.
    6. Taghavifar, Hamid & Mardani, Aref, 2014. "Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices," Energy, Elsevier, vol. 68(C), pages 651-657.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors," Energy, Elsevier, vol. 93(P2), pages 1704-1710.
    2. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    3. Shafaei, S.M. & Mousazadeh, H., 2023. "Motion energy perspective of tracked locomotion system of autonomous tractor-trailer robot," Energy, Elsevier, vol. 264(C).
    4. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    5. Janulevičius, Algirdas & Damanauskas, Vidas, 2015. "How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip," Energy, Elsevier, vol. 90(P1), pages 691-700.
    6. Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
    7. Wang, H., 2015. "A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator," Energy, Elsevier, vol. 80(C), pages 114-122.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    2. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Experimental analysis of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 91(C), pages 573-578.
    3. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors," Energy, Elsevier, vol. 93(P2), pages 1704-1710.
    4. Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
    5. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    6. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    7. Helmi Hammami & Thanh Ngo & David Tripe & Dinh-Tri Vo, 2022. "Ranking with a Euclidean common set of weights in data envelopment analysis: with application to the Eurozone banking sector," Annals of Operations Research, Springer, vol. 311(2), pages 675-694, April.
    8. Wu, Yanrui, 1995. "The productive efficiency of Chinese iron and steel firms A stochastic frontier analysis," Resources Policy, Elsevier, vol. 21(3), pages 215-222, September.
    9. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    10. Blasi, E. & Passeri, N. & Franco, S. & Galli, A., 2016. "An ecological footprint approach to environmental–economic evaluation of farm results," Agricultural Systems, Elsevier, vol. 145(C), pages 76-82.
    11. Liu, Lili, 1991. "Entry-exit, learning, and productivity change : evidence from Chile," Policy Research Working Paper Series 769, The World Bank.
    12. María Victoria Uribe‐Bohorquez & Jennifer Martínez‐Ferrero & Isabel‐María García‐Sánchez, 2019. "Women on boards and efficiency in a business‐orientated environment," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(1), pages 82-96, January.
    13. Harald Dyckhoff & Katrin Allen, 1999. "Theoretische Begründung einer Effizienzanalyse mittels Data Envelopment Analysis (DEA)," Schmalenbach Journal of Business Research, Springer, vol. 51(5), pages 411-436, May.
    14. Pahlavan, Reza & Omid, Mahmoud & Akram, Asadollah, 2011. "Energy use efficiency in greenhouse tomato production in Iran," Energy, Elsevier, vol. 36(12), pages 6714-6719.
    15. Liu, Haiyue & Zhang, Ruchuan & Zhou, Li & Li, Aijun, 2023. "Evaluating the financial performance of companies from the perspective of fund procurement and application: New strategy cross efficiency network data envelopment analysis models," Energy, Elsevier, vol. 269(C).
    16. Miguel SARMIENTOO & Andrés CEPEDA & Hernando MUTIS & Juan F. PÉREZ, 2013. "Nueva Evidencia sobre la Eficiencia de la Banca," Archivos de Economía 10705, Departamento Nacional de Planeación.
    17. Chang, Hsi-Hui, 1998. "Determinants of Hospital Efficiency: the Case of Central Government-owned Hospitals in Taiwan," Omega, Elsevier, vol. 26(2), pages 307-317, April.
    18. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    19. Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
    20. Ablam Estel APETI & Bao-We-Wal BAMBE & Jean Louis COMBES, 2022. "On the Macroeconomic Effects of Fiscal Reforms : Fiscal Rules and Public Expenditure Efficiency," LEO Working Papers / DR LEO 2985, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:73:y:2014:i:c:p:762-770. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.