Abstract

In today’s global and competitive market, manufacturing companies are working hard to improve their production system performance. Most companies develop production systems that can help in quality improvement, cost reduction and throughput time reduction. Manufacturing systems typically consist of different elements including production methods, machines, processes, control and information systems. Human issues are an important part of manufacturing systems, yet most companies do not pay sufficient attention to them. The majority of a company’s improvement comes when the right workers with the right skills, behaviors and capacities are deployed appropriately throughout a company. Developing an integrated workforce planning system that incorporates the human being is a challenging problem. To achieve this goal, a multi-objective mixed integer nonlinear programming model is developed to determine the amount of hiring, firing, training, overtime for each worker type and the amount of the break for each worker. This thesis considers a workforce planning model including human aspects such as skills, training, workers’ personalities, capacity, motivation, learning rates, and fatigue and recovery levels. This model helps to minimize the hiring, firing, training and overtime costs, minimize the number of fired workers with high performance, minimize the break time and minimize the average worker’s fatigue level. The results indicate that the worker differences should be considered in workforce scheduling to generate realistic plans with minimum costs. This thesis also investigates the effects of human fatigue and recovery rates, and human learning rates on the performance of the production systems. Moreover, a decision support system (DSS) based on the proposed model is introduced using the Excel-LINGO software interfacing feature. It is shown that considering both technical and human factors together can reduce the costs in manufacturing systems and ensure the safety of the workers.