A technique of the salient success and survival aspiration levels for multiple objective/criteria decision-making problems

References

• Al-Nowaihi, A., Bradley, I., & Dhami, S. (2008). A note on the utility function under prospect theory. Economics Letters, 99(2), 337–339.10.1016/j.econlet.2007.08.004
   Web of Science ®Google Scholar
• Boyle, E., & Shapira, Z. (2012). The liability of leading: Battling aspiration and survival goals in the Jeopardy! tournament of champions. Organization Science, 23(4), 1100–1113.10.1287/orsc.1110.0690
   Web of Science ®Google Scholar
• Chang, C. T. (2007). Multi-choice goal programming. Omega, 35(4), 389–396.10.1016/j.omega.2005.07.009
   Web of Science ®Google Scholar
• Chang, C. T. (2010). An approximation approach for representing S-shaped membership functions. IEEE Transactions on Fuzzy Systems, 18(2), 412–424.
   Web of Science ®Google Scholar
• Chang, C. T. (2011). Multiple choice goal programming with utility functions. European Journal of Operational Research, 215(2), 439–445.10.1016/j.ejor.2011.06.041
   Web of Science ®Google Scholar
• Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. New York, NY: Wiley.
   Google Scholar
• Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138–151.10.1287/mnsc.1.2.138
   Web of Science ®Google Scholar
• Clark, B. H., & Montgomery, D. B. (1999). Managerial identification of competitors. Journal of Marketing, 63(3), 67–83.10.2307/1251776
   Web of Science ®Google Scholar
• Gavetti, G., Levinthal, D., & Ocasio, W. (2007). Perspective – Neo-carnegie: The Carnegie school’s past, present, and reconstructing for the future. Organization Science, 18(3), 523–536.10.1287/orsc.1070.0277
   Web of Science ®Google Scholar
• Greve, H. R. (1998). Performance, aspirations, and risky organizational change. Administrative Science Quarterly, 43(1), 58–86.10.2307/2393591
   Web of Science ®Google Scholar
• Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making methods and applications. New York, NY: Springer.10.1007/978-3-642-48318-9
   Google Scholar
• Ignizio, J. P. (1985). Introduction to linear goal programming. Beverly Hills, CA: Sage.10.4135/9781412984669
   Google Scholar
• Iyer, D. N., & Miller, K. D. (2008). Performance feedback, slack, and the timing of acquisitions. The Academy of Management Journal, 51(4), 808–822.
   Web of Science ®Google Scholar
• Jones, D. F., & Tamiz, M. (1995). Expanding the flexibility of goal programming via preference modeling technique. Omega, 23(1), 41–48.10.1016/0305-0483(94)00056-G
   Web of Science ®Google Scholar
• Lai, Y. J., & Hwang, C. L. (1994). Fuzzy multiple objective decision making: Methods and applications. New York, NY: Spinger.10.1007/978-3-642-57949-3
   Google Scholar
• Larbani, M., & Aouni, B. (2011). A new approach for generating efficient solutions within the goal programming model. Journal of the Operational Research Society, 62(1), 175–182.10.1057/jors.2009.185
   Web of Science ®Google Scholar
• Lee, S. M. (1972). Goal programming for decision analysis. Philadelphia, PA: Auerbach.
   Google Scholar
• Lotfi, V., Yoon, Y. S., & Zionts, S. (1997). Aspiration-based search algorithm (ABSALG) for multiple objective linear programming problems: Theory and comparative tests. Management Science, 43(8), 1047–1059.10.1287/mnsc.43.8.1047
   Web of Science ®Google Scholar
• March, J. G., & Shapira, Z. (1992). Variable risk preferences and the focus of attention. Psychological Review, 99(1), 172–183.10.1037/0033-295X.99.1.172
   Web of Science ®Google Scholar
• Martin, B. A., & Manning, D. J. (1995). Combined effects of normative information and task difficulty on the goal commitment-performance relationship. Journal of Management, 21(1), 65–80.10.1177/014920639502100104
   Web of Science ®Google Scholar
• Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345, 9–26.10.1016/j.ins.2016.01.042
   Web of Science ®Google Scholar
• Mezias, S. J., Chen, Y. R., & Murphy, P. R. (2002). Aspiration-level adaptation in an american financial services organization: A field study. Management Science, 48(10), 1285–1300.10.1287/mnsc.48.10.1285.277
   Web of Science ®Google Scholar
• Michalowski, W., & Szapiro, T. (1992). A bi-reference procedure for interactive multiple criteria programming. Operations Research, 40(2), 247–258.10.1287/opre.40.2.247
   Web of Science ®Google Scholar
• Miller, K. D., & Chen, W. R. (2004). Variable organizational risk preferences: Tests of the March-Shapira model. Academic Management Journal, 47(1), 105–115.10.2307/20159563
   Web of Science ®Google Scholar
• Peng, W., & Mayorga, R. V. (2013). An interval-coefficient fuzzy binary linear programming, the solution, and its application under uncertainties. Journal of the Operational Research Society, 64(10), 1557–1569.10.1057/jors.2012.111
   Web of Science ®Google Scholar
• Podinorski, V. V. (2010). Set choice problems with incomplete information about the preferences of the decision maker. European Journal of Operational Research, 207(1), 371–379.10.1016/j.ejor.2010.04.010
   Web of Science ®Google Scholar
• Romero, C. (2001). Extended lexicographic goal programming: A unifying approach. Omega, 29(1), 63–71.10.1016/S0305-0483(00)00026-8
   Web of Science ®Google Scholar
• Schrage, L. (2002). LINGO release 8.0. Chicago: LINDO System.
   Google Scholar
• Sinha, S. B., & Sinha, S. (2004). A linear programming approach for linear multi-level programming problems. Journal of the Operational Research Society, 55(3), 312–316.10.1057/palgrave.jors.2601701
   Web of Science ®Google Scholar
• Tamiz, M., Jones, D., & Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research, 111(3), 569–581.10.1016/S0377-2217(97)00317-2
   Web of Science ®Google Scholar
• Zhang, Z. Y., & Shang, J. S. (2001). Goal programs with -ni, -pi and - ni + pi) objective functions. European Journal of Operational Research, 134(1), 157–164.10.1016/S0377-2217(00)00245-9
   Google Scholar