Fuzzy System Dynamics of Manpower Systems

Introduction

Over the last three decades, several researchers and practitioners have increasingly focused their attention on developing robust manpower recruitment and training policies, especially for organizations (Mehlmann, 1980; McClean, 1991; Rao, 1991; Anthony & Wilson, 1990; Nilakantan & Raghavendra, 2005; Skulj et al. 2008; Mutingi et al., 2012). In the absence of numerical precision on the likely future market demand, it is often difficult to develop effective manpower recruitment and training strategies, especially in the case of large organizations. As such, future demand for human resources is often fuzzy or uncertain (Guerry, 1999; Mutingi et al., 2012). When future manpower demand is imprecise, uncertain, or vague, managers in large organizations frequently face challenges in developing manpower recruitment policies to maintain human resources at the right level over time. In the same token, future supply and demand of goods and services in the marketplace is precise. As a result, researchers and practitioners resort to the use of fuzzy set theoretic methods, dynamic approaches, and related methodologies in order to address problems found in complex ill-structured systems, such as manpower systems, supply chain systems, inventory management systems, production planning systems, manufacturing systems, transportation systems, and other industrial systems (Madronero, Peidro, & Vasant, 2010). In general, soft computing approaches have become the most appropriate and practical solution methods for solving real life problems, as postulated by numerous authors in the literature (Vasant et al., 2007; Vasant & Barsoum, 2009; Elamvazuthi et al., 2009; Peidro & Vasant, 2010; Elamvazuthi, Vasant, & Ganesan, 2010; Peidro & Vasant, 2011; Cebi et al., 2012; Dostal, 2013; Guerry, 1999). However, in practice, several complexities are realized in manpower systems modeling.

In the real world, we are often unable or unwilling to describe a manpower system structure and behavior using humanistic or linguistic expressions, such as high demand, somewhat high demand, or increasing demand, among others. In large corporations, vagueness of future demand for human resources is. In most cases, caused by dynamic market growth which. In turn, directly influences the need to develop robust recruitment policies in accordance with the ensuing manpower demand behavior. As an illustration. In times of growth or decline of business, manpower policy makers must recruit human resources cautiously, paying attention to the lack of numerical precision in the variables that affect the behavior of manpower systems (Guerry, 1999). In fact, this is critical in the sense that robust policies can avoid huge costs associated with understaffing due to under-investment in human resource recruitment and training, and overstaffing due to over-investment in human resources. Therefore, a judicious trade-off between loss of business due to under-investment in human resources in times of business growth and overstaffing costs due to over-investment in human resources in times of business decline. Robust computing approaches are essential in complex and uncertain environments in order to design, evaluate and implement appropriate human resource planning policies.