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How to cite this paper
APA: Sotoudeh-Anvari, A., Sadjadi, S., Molana, S & Sadi-Nezhad, S. (2018). A new MCDM-based approach using BWM and SAW for optimal search model. Decision Science Letters, 7(4), 395-404.
Chicago/Turabian: Sotoudeh-Anvari, A., Sadjadi, S., Molana, S & Sadi-Nezhad, S. 2018. "A new MCDM-based approach using BWM and SAW for optimal search model." Decision Science Letters 7, no. 4 (2018): 395-404.
AMA: Sotoudeh-Anvari, A., Sadjadi, S., Molana, S & Sadi-Nezhad, S. A new MCDM-based approach using BWM and SAW for optimal search model. Decision Science Letters. 2018;7(4):395-404.
References
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Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, 153, 242-252.
Askarifar, K., Motaffef, Z., & Aazaami, S. (2018). An investment development framework in Iran's seashores using TOPSIS and best-worst multi-criteria decision making methods. Decision Science Letters, 7(1), 55-64.
Benkoski, S. J., Monticino, M. G., & Weisinger, J. R. (1991). A survey of the search theory literature. Naval Research Logistics (NRL), 38(4), 469-494.
Bernroider, E., & Stix, V. (2007). A method using weight restrictions in data envelopment analysis for ranking and validity issues in decision making. Computers & Operations Research, 34(9), 2637-2647.
Black, W. L. (1965). Discrete sequential search. Information and control, 8(2), 159-162.
Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data methods. Materials & Design, 32(2), 851-860.
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016). Multi objective performance analysis: A novel multi-criteria decision making approach for a supply chain. Computers & Industrial Engineering, 94, 105-124.
Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety science, 35(1-3), 41-57.
Garg, R., & Jain, D. (2017). Fuzzy multi-attribute decision making evaluation of e-learning websites using FAHP, COPRAS, VIKOR, WDBA. Decision Science Letters, 6(4), 351-364.
Ghaffari, S., Arab, A., Nafari, J., & Manteghi, M. (2017). Investigation and evaluation of key success factors in technological innovation development based on BWM. Decision Science Letters, 6(3), 295-306.
Ginevičius, R., & Zubrecovas, V. (2009). Selection of the optimal real estate investment project basing on multiple criteria evaluation using stochastic dimensions. Journal of business economics and management, 10(3), 261-270.
Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258.
Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351-366.
Hobbs, B. F., & Horn, G. T. (1997). Building public confidence in energy planning: a multimethod MCDM approach to demand-side planning at BC gas. Energy policy, 25(3), 357-375.
Jing, Y. Y., Bai, H., & Wang, J. J. (2012). A fuzzy multi-criteria decision-making model for CCHP systems driven by different energy sources. Energy Policy, 42, 286-296.
Kadane, J. B. (2015). Optimal discrete search with technological choice. Mathematical Methods of Operations Research, 81(3), 317-336.
Kriheli, B., Levner, E., & Spivak, A. (2016). Optimal search for hidden targets by unmanned aerial vehicles under imperfect inspections. American Journal of Operations Research, 6(02), 153.
Levner, E. (1994). Infinite‐horizon Scheduling Algorithms for Optimal Search for Hidden Objects. International transactions in operational research, 1(2), 241-250
Løken, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595.
Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253.
Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production.
Mou, Q., Xu, Z., & Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224-239.
Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146-156.
Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review, 49(1), 217-249.
Ren, J., Liang, H., & Chan, F. T. (2017). Urban sewage sludge, sustainability, and transition for Eco-City: Multi-criteria sustainability assessment of technologies based on best-worst method. Technological Forecasting and Social Change, 116, 29-39.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
Ross, S. M. (1983). Introduction to stochastic dynamic programming. Academic press.
Roy, B. (1990). The outranking approach and the foundations of ELECTRE methods. In Readings in multiple criteria decision aid (pp. 155-183). Springer, Berlin, Heidelberg.
Salehi, A., & Izadikhah, M. (2014). A novel method to extend SAW for decision-making problems with interval data. Decision Science Letters, 3(2), 225-236.
Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and program planning, 66, 147-155.
Serrai, W., Abdelli, A., Mokdad, L., & Hammal, Y. (2017). Towards an efficient and a more accurate web service selection using MCDM methods. Journal of Computational Science, 22, 253-267.
Sotoudeh-Anvari, A., Sadjadi, S.J., Molana, S.M.H., & Sadi-Nezhad, S. (2018). A stochastic multi-objective model based on the classical optimal search model for searching for the people who are lost in response stage of earthquake. Scientia Iranica (in press)
Stone, L. D. (1976). Theory of optimal search (Vol. 118). Elsevier.
Stone, L. D. (2013). Search theory. Encyclopedia of Operations Research and Management Science, 1366-1378.
Terrados, J., Almonacid, G., & PeRez-Higueras, P. (2009). Proposal for a combined methodology for renewable energy planning. Application to a Spanish region. Renewable and Sustainable Energy Reviews, 13(8), 2022-2030.
Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study (pp. 5-21). Springer, Boston, MA.
Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), 870-880.
Wang, Y. M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51(1-2), 1-12.
Wang, P., Zhu, Z., & Wang, Y. (2016). A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design. Information Sciences, 345, 27-45.
Zavadskas, E. K., & Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology & Decision Making, 15(02), 267-283.
Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, 153, 242-252.
Askarifar, K., Motaffef, Z., & Aazaami, S. (2018). An investment development framework in Iran's seashores using TOPSIS and best-worst multi-criteria decision making methods. Decision Science Letters, 7(1), 55-64.
Benkoski, S. J., Monticino, M. G., & Weisinger, J. R. (1991). A survey of the search theory literature. Naval Research Logistics (NRL), 38(4), 469-494.
Bernroider, E., & Stix, V. (2007). A method using weight restrictions in data envelopment analysis for ranking and validity issues in decision making. Computers & Operations Research, 34(9), 2637-2647.
Black, W. L. (1965). Discrete sequential search. Information and control, 8(2), 159-162.
Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data methods. Materials & Design, 32(2), 851-860.
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016). Multi objective performance analysis: A novel multi-criteria decision making approach for a supply chain. Computers & Industrial Engineering, 94, 105-124.
Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety science, 35(1-3), 41-57.
Garg, R., & Jain, D. (2017). Fuzzy multi-attribute decision making evaluation of e-learning websites using FAHP, COPRAS, VIKOR, WDBA. Decision Science Letters, 6(4), 351-364.
Ghaffari, S., Arab, A., Nafari, J., & Manteghi, M. (2017). Investigation and evaluation of key success factors in technological innovation development based on BWM. Decision Science Letters, 6(3), 295-306.
Ginevičius, R., & Zubrecovas, V. (2009). Selection of the optimal real estate investment project basing on multiple criteria evaluation using stochastic dimensions. Journal of business economics and management, 10(3), 261-270.
Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258.
Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351-366.
Hobbs, B. F., & Horn, G. T. (1997). Building public confidence in energy planning: a multimethod MCDM approach to demand-side planning at BC gas. Energy policy, 25(3), 357-375.
Jing, Y. Y., Bai, H., & Wang, J. J. (2012). A fuzzy multi-criteria decision-making model for CCHP systems driven by different energy sources. Energy Policy, 42, 286-296.
Kadane, J. B. (2015). Optimal discrete search with technological choice. Mathematical Methods of Operations Research, 81(3), 317-336.
Kriheli, B., Levner, E., & Spivak, A. (2016). Optimal search for hidden targets by unmanned aerial vehicles under imperfect inspections. American Journal of Operations Research, 6(02), 153.
Levner, E. (1994). Infinite‐horizon Scheduling Algorithms for Optimal Search for Hidden Objects. International transactions in operational research, 1(2), 241-250
Løken, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595.
Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253.
Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production.
Mou, Q., Xu, Z., & Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224-239.
Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146-156.
Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review, 49(1), 217-249.
Ren, J., Liang, H., & Chan, F. T. (2017). Urban sewage sludge, sustainability, and transition for Eco-City: Multi-criteria sustainability assessment of technologies based on best-worst method. Technological Forecasting and Social Change, 116, 29-39.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
Ross, S. M. (1983). Introduction to stochastic dynamic programming. Academic press.
Roy, B. (1990). The outranking approach and the foundations of ELECTRE methods. In Readings in multiple criteria decision aid (pp. 155-183). Springer, Berlin, Heidelberg.
Salehi, A., & Izadikhah, M. (2014). A novel method to extend SAW for decision-making problems with interval data. Decision Science Letters, 3(2), 225-236.
Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and program planning, 66, 147-155.
Serrai, W., Abdelli, A., Mokdad, L., & Hammal, Y. (2017). Towards an efficient and a more accurate web service selection using MCDM methods. Journal of Computational Science, 22, 253-267.
Sotoudeh-Anvari, A., Sadjadi, S.J., Molana, S.M.H., & Sadi-Nezhad, S. (2018). A stochastic multi-objective model based on the classical optimal search model for searching for the people who are lost in response stage of earthquake. Scientia Iranica (in press)
Stone, L. D. (1976). Theory of optimal search (Vol. 118). Elsevier.
Stone, L. D. (2013). Search theory. Encyclopedia of Operations Research and Management Science, 1366-1378.
Terrados, J., Almonacid, G., & PeRez-Higueras, P. (2009). Proposal for a combined methodology for renewable energy planning. Application to a Spanish region. Renewable and Sustainable Energy Reviews, 13(8), 2022-2030.
Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study (pp. 5-21). Springer, Boston, MA.
Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), 870-880.
Wang, Y. M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51(1-2), 1-12.
Wang, P., Zhu, Z., & Wang, Y. (2016). A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design. Information Sciences, 345, 27-45.
Zavadskas, E. K., & Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology & Decision Making, 15(02), 267-283.