A Confined Workforce Planning Model with Plugging for Service Organizations Using Network Flow Under Finite Horizon, Varying Demand Senario

• Main Author: Varughese, T. C.
• Format: Thesis
• Language: English; English
• Published: 2015
• Subjects: HF Commerce
• Online Access: http://ur.aeu.edu.my/104/1/A%20confined%20workforce%20planning%20model%20with%20plugging%20for%20service%20organizations%20using%20network%20flow%20under%20finite%20horizon%2C%20varying%20demand%20scenario.pdf; http://ur.aeu.edu.my/104/6/A%20confined%20workforce%20planning%20model%20with%20plugging%20for%20service%20organizations%20using%20network%20flow%20under%20finite%20horizon%2C%20varying%20demand%20scenario.pdf; http://ur.aeu.edu.my/104/; https://online.fliphtml5.com/sppgg/ffil/?1596677116619

Managing the workforce of a service industry is a key factor for its success. An ideal quantitative model for workforce management should be efficient in terms of time, cost, volume and the simplicity of use. The aim of this study is to develop a user-friendly quantitative model to determine the magnitude of changes in workforce strength of a service industry to meet the varying demands, while maintaining a desired level of quality and cost effectiveness. The scope of the study is limited to finite planning horizon. Also, employee-learning through experience is not considered in this study. The study uses the Shortest Path method of the network flow models and identifies the optimum capacity required in each period of the planning horizon using Dijikstra’s Algorithm. Two non-linear, zero-one integer programming models have been developed – first one is the Confined Workforce Planning (CWP) model in which cost for changing the workforce strength is assumed as uniform and the second, a Confined Workforce Planning with Plugging (CWPP) model where workforce changing cost is non-uniform. These models are then converted into network flow models and optimization is done using shortest path method. Three extensions, pertaining to the impacts of controlled violation of the constraints and more system constraints, and the applicability of the models in other problem domains are discussed. The study finds that both models will have the same size, if no additional force is acquired at any period of the horizon, and maximum size of CWPP model occurs when acquiring is done in the first period. The study also reveals that the optimal solution can be obtained in polynomial time and the network size depends on workforce strength changing levels, planning horizon length and the ratio of the initial essential capacity to the available capacity which are computationally verified.