Fuzzy system dynamics and optimization with application to manpower systems

Abstract

The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative. In this frame of mind, a fuzzy systems dynamics modelling approach is proposed to enable the policy maker to develop reliable dynamic policies relating recruitment, training, and available skills, from a systems perspective. It is anticipated in this study that fuzzy system dynamics and optimization approach would help organizations to design effective manpower policies and strategies.

Rehabilitation of existing building stock: A system dynamics model to support policy development

“…The methodology of SD also provides an effective simulation environment for exploring the environment of complex social systems and addressing long-term policy issues exhibiting dynamic complexities. This modeling approach has been widely applied to a number of problems and contexts such as policy research in health care (Homer and Hirsch 2006), environmental management (Rehan et al 2011); and political decision making (Saleh et al 2010;Mutingi and Mbohwa 2012). Applications of SD to urban sustainability within the extant literature have tended to focus on the following areas: (1) sustainable land use;…”

Dynamic Modeling of Workforce Planning for Infrastructure Projects

“…This process may encapsulate judgment technique, trend projection, stock-flow modeling, labor multiplier modeling and econometric modeling. It seeks to secure labor equilibrium via a process of dynamic policy interventions in a fuzzy workforce environment (Mutingi and Mbohwa 2012). Prediction accuracy is subject to global and national economic performance, technological advances, demographic changes and so forth.…”

“…Based upon this early work, Hafeez and Abdelmeguid (2003) developed an SD based model to illustrate the relationship between recruitment, training, skills and knowledge in a causal loop form. Mutingi and Mbohwa (2012) further combined fuzzy SD and optimization techniques to develop training strategies for a single organization. Alvanchi et al (2012) also applied SD modeling to study the dynamics of construction workforce skill evolution and understand how a company's human resource policy affects the project's performance and cost.…”

“…In the absence of reliable workforce planning (WP), labor market requirements are inherently uncertain and exposed to workforce perturbations that can oscillate erratically between surplus and shortage (Sing et al 2012a). To forecast and plan for labor requirements a plethora of models (e.g., regression and econometric modelling) have been developed so that government policy makers can adjust the type, number and size of training courses available to fulfill demand (Mutingi and Mbohwa 2012). Despite progress in this area, inaccuracies with WP continue to frustrate the industry's attempts to effectively manage labor requirements (Sing et al 2014).…”

Recruitment Model in Manpower Planning Under Fuzzy Environment

“…The main contribution by Zadeh & Bellman [18] to fuzzy and decision theory have led to many fuzzy models in various fields and particularly in manpower systems. To mention a few, Guerry [19] used fuzzy sets in manpower planning and Mutingi M. [20] has applied fuzzy dynamics to manpower systems. In this paper, we have extended the recruitment model, solved using stochastic process by Jeeva [21], to a fuzzy model and solved it using a fuzzy technique.…”