Evaluating Recruitment Strategies Using Fuzzy Set Theory in Stochastic Manpower Planning

Abstract

One aspect of Manpower Planning is the investigation of wastage and internal transitions for homogeneous groups of employees in a Manpower system. In the relevant literature, the attainability of a desired stock vector was studied under control by recruitment for time-discrete Markov models. These approaches allow choosing a proper recruitment strategy, resulting in an attainable vector most similar to a desired one. In this paper, this problem will be discussed under stochastic assumptions for attainability after one step. Based on fuzzy set theory, a procedure is introduced that allows evaluating and comparing recruitment strategies, resulting in the determination of a most preferable strategy.

Balancing attainability, desirability and promotion steadiness in manpower planning systems

“…This paper can be extended in several ways. Other objectives in manpower planning, for example, the realization degree (the attainability in the stochastic case as in De Feyter and Guerry, 2009) is another important objective. This objective is critical when the personnel wastage is beyond the management control and cannot be known beforehand.…”

“…De Feyter and Guerry (2009) proposed a fuzzy set approach for defining the desirability degree. Let β(n(t)) be the desirability degree of personnel structure n(t).…”

“…This approach corresponds to maximizing the similarity between the realized and the desired personnel structure, an objective that is frequently used in previous work (Georgiou and Tsantas, 2002;Dimitriou et al, 2013). In order to express this similarity, De Feyter and Guerry (2009) introduced the desirability degree of a personnel structure. Thus, in case the desired personnel structure is not attainable, an organization would choose a recruitment strategy that realizes an attainable personnel structure with maximum desirability degree.…”

Evaluation of staffing policies in Markov manpower systems and their extension to organizations with outsource personnel

“…The books of Bartholomew (1982) and Vajda (1978) provide a comprehensive treatment of the theory of Markov models used in manpower planning, while the paper of Ernst et al (2004) provides a comprehensive review of the methods, models and applications in staff scheduling and rostering. A few of the contemporaneous and relevant papers are on the stochastic theory of compartments with applications to manpower planning (Agrafiotis, 1991), convergence of a manpower distribution (Ledermann, 1992), cohort analysis technique for long-term manpower planning (Chu and Lin, 1994), use of DEA for planning in UK universities (Sarricol and Dyson, 2000), modelling for planning and management of bed capacities in hospitals (Harper and Shahani, 2002), modelling heterogeneity in manpower systems (De Feyter, 2006), modelling of mixed push and pull promotion flows (De Feyter, 2007), an application of Markov manpower planning models in the armed forces (Skulj et al, 2008), profile-based push models (Guerry, 2008), a recent review of Markov models in manpower planning (De Feyter and Guerry, 2009a), Fuzzy set theory in stochastic manpower planning (De Feyter and Guerry, 2009b), two-stage workforce planning under demand fluctuations and uncertainty (Zhu and Sherali, 2009), and planning and benchmarking of doctoral programmes using Markov models (Nicholls, 2009). The most recent work is that of Li et al (2010) on optimal manpower decisions with a minimal length of employment constraint, Guerry (2011) on hidden heterogeneity in Markov manpower systems, Kim and Yoo (2012) on combined manpower planning and preventive maintenance strategies, and Guerry and De Feyter (2012) on optimal recruitment strategies in multi-level manpower planning.…”

Optimizing cost-effectiveness in a stochastic Markov manpower planning system under control by recruitment

“…In the present paper, we propose a mathematical model and algorithm for optimizing cost-effectiveness in a manpower planning system under control by recruitment. In real-world applications, the uncontrollable model parameters regarding wastage and internal flows are often subject to uncertainty (Chattopadhyay & Gupta 2007;De Feyter & Guerry 2009;Papadopoulou & Tsaklidis 2007;Shapiro et al 2009;Vassiliou & Gerontidis 1985). Therefore, in contrast to similar previous work (Dimitriou et al 2013;Georgiou & Tsantas 2002), we consider the stochastic nature of the manpower planning problem.…”