Orderings of fuzzy sets based on fuzzy orderings. Part II: generalizations

In Part I of this series of papers, a general approach for ordering fuzzy sets with respect to fuzzy orderings was presented. Part I also highlighted three limitations of this approach. The present paper addresses these lim- itations and proposes solutions for overcoming them. We rst consider a fuzzi cation of the ordering relation, then ways to compare fuzzy sets with di erent heights, and nally we introduce how to re ne the ordering relation by lexicographic hybridization with a di erent ordering methodPeer Reviewe

Orderings of fuzzy sets based on fuzzy orderings. Part I: the basic approach

The aim of this paper is to present a general framework for comparing fuzzy sets with respect to a general class of fuzzy orderings. This approach includes known techniques based on generalizing the crisp linear ordering of real numbers by means of the extension principle, however, in its general form, it is applicable to any fuzzy subsets of any kind of universe for which a fuzzy ordering is known|no matter whether linear or partialPeer Reviewe

A Simple Approximation of Productivity Scores of Fuzzy Production Plans

This paper suggests a simple approximation procedure for the assessment of productivity scores with respect to fuzzy production plans. The procedure has a clear economic interpretation and all the necessary calculations can be performed in a spreadsheet making it highly operational.rationing; inequality preservation; taxation; manipulation; proportional method

Data clustering using a model granular magnet

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.Comment: 46 pages, postscript, 15 ps figures include

An algorithm to identify the most motivated employees

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here (please insert the web address here). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.Purpose - In this paper, the aim is to present an algorithm to identify the most motivated employees according to the corporate human resources policies applied in a knowledge-based company. Design/methodology/approach - First, the author presents a complete table with motivation factors found in the literature. Then, a fuzzy tool, i.e. an adequacy index, is used in order to include subjectivity and uncertainty from personal perceptions both from employees and managers in the model. Findings - An algorithm to identify the most motivated employees according to corporate motivation policies designed by managers is found. Practical implications - Results provide information that can be used as a support for helping managers in decision making about training, promotion, leadership practices or team management. Originality/value - An exhaustive list that does not exist in the literature about motivation factors is presented as well as the proposal of measure and identification on most-motivated employees is also a new idea.Canós-Darós, L. (2013). An algorithm to identify the most motivated employees. Management Decision. 51(4):813-823. doi:10.1108/00251741311326581S813823514Baddoo, N., Hall, T., & Jagielska, D. (2006). Software developer motivation in a high maturity company: a case study. Software Process: Improvement and Practice, 11(3), 219-228. doi:10.1002/spip.265Campbell, D. J., Campbell, K. M., & Chia, H.-B. (1998). Merit pay, performance appraisal, and individual motivation: An analysis and alternative. Human Resource Management, 37(2), 131-146. doi:10.1002/(sici)1099-050x(199822)37:23.0.co;2-xChen, S.-H. (1985). Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets and Systems, 17(2), 113-129. doi:10.1016/0165-0114(85)90050-8Choobineh, F., & Li, H. (1993). An index for ordering fuzzy numbers. Fuzzy Sets and Systems, 54(3), 287-294. doi:10.1016/0165-0114(93)90374-qCory, S.N., Ward, S. and Schultz, S.A. (2007), “Managing human resources in a small firm. Motivation through performance evaluation”, The CPA Journal, October, pp. 62‐65.DeVoe, S. E., & Iyengar, S. S. (2004). Managers’ theories of subordinates: A cross-cultural examination of manager perceptions of motivation and appraisal of performance. Organizational Behavior and Human Decision Processes, 93(1), 47-61. doi:10.1016/j.obhdp.2003.09.001Dieleman, M., Toonen, J., Touré, H., & Martineau, T. (2006). The match between motivation and performance management of health sector workers in Mali. Human Resources for Health, 4(1). doi:10.1186/1478-4491-4-2Fortemps, P., & Roubens, M. (1996). Ranking and defuzzification methods based on area compensation. Fuzzy Sets and Systems, 82(3), 319-330. doi:10.1016/0165-0114(95)00273-1Garg, P., & Rastogi, R. (2006). New model of job design: motivating employees’ performance. Journal of Management Development, 25(6), 572-587. doi:10.1108/02621710610670137Gholipour, A., Pirannejad, A., Kozekanan, S. F., & Gholipour, F. (2011). Designing Motivation System to Produce Creativity and Entrepreneurship in Petrochemical Company. International Journal of Business and Management, 6(5). doi:10.5539/ijbm.v6n5p137Glen, C. (2006). Key skills retention and motivation: the war for talent still rages and retention is the high ground. Industrial and Commercial Training, 38(1), 37-45. doi:10.1108/00197850610646034James, H. S. (2005). Why did you do that? An economic examination of the effect of extrinsic compensation on intrinsic motivation and performance. Journal of Economic Psychology, 26(4), 549-566. doi:10.1016/j.joep.2004.11.002Kuvaas, B. (2006). Work performance, affective commitment, and work motivation: the roles of pay administration and pay level. Journal of Organizational Behavior, 27(3), 365-385. doi:10.1002/job.377Mathauer, I., & Imhoff, I. (2006). Health worker motivation in Africa: the role of non-financial incentives and human resource management tools. Human Resources for Health, 4(1). doi:10.1186/1478-4491-4-24Orpen, C. (1994). Interactive Effects of Work Motivation and Personal Control on Employee Job Performance and Satisfaction. The Journal of Social Psychology, 134(6), 855-856. doi:10.1080/00224545.1994.9923021Peterson, T. M. (2007). Motivation: How to Increase Project Team Performance. Project Management Journal, 38(4), 60-69. doi:10.1002/pmj.20019Reis, D., & Peña, L. (2001). Reengineering the motivation to work. Management Decision, 39(8), 666-675. doi:10.1108/eum0000000005929Story, P. A., Hart, J. W., Stasson, M. F., & Mahoney, J. M. (2009). Using a two-factor theory of achievement motivation to examine performance-based outcomes and self-regulatory processes. Personality and Individual Differences, 46(4), 391-395. doi:10.1016/j.paid.2008.10.023Tabassi, A. A., & Bakar, A. H. A. (2009). Training, motivation, and performance: The case of human resource management in construction projects in Mashhad, Iran. International Journal of Project Management, 27(5), 471-480. doi:10.1016/j.ijproman.2008.08.002Tietjen, M. A., & Myers, R. M. (1998). Motivation and job satisfaction. Management Decision, 36(4), 226-231. doi:10.1108/00251749810211027Van Knippenberg, D. (2000). Work Motivation and Performance: A Social Identity Perspective. Applied Psychology, 49(3), 357-371. doi:10.1111/1464-0597.00020Vollmeyer, R., & Rheinberg, F. (2000). Does motivation affect performance via persistence? Learning and Instruction, 10(4), 293-309. doi:10.1016/s0959-4752(99)00031-6Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets and Systems, 118(3), 375-385. doi:10.1016/s0165-0114(99)00062-7Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (II). Fuzzy Sets and Systems, 118(3), 387-405. doi:10.1016/s0165-0114(99)00063-9Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161. doi:10.1016/0020-0255(81)90017-7Yuan, Y. (1991). Criteria for evaluating fuzzy ranking methods. Fuzzy Sets and Systems, 43(2), 139-157. doi:10.1016/0165-0114(91)90073-yZadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-

Quantity and number

Quantity is the first category that Aristotle lists after substance. It has extraordinary epistemological clarity: "2+2=4" is the model of a self-evident and universally known truth. Continuous quantities such as the ratio of circumference to diameter of a circle are as clearly known as discrete ones. The theory that mathematics was "the science of quantity" was once the leading philosophy of mathematics. The article looks at puzzles in the classification and epistemology of quantity