A Combination of Workforce Sizing Plan and Worker Selection Guide with the Holonic Control Paradigm

The Holonic Workforce Allocation Model (HWM) is a dual-level advisory model using the concepts of Holonic Manufacturing Systems (HMS). The quantitative and pre-active level is termed as Workforce Sizing Plan (WOZIP), whereby the number of workers required for a production period can be forecasted. The resultant group of workers, in a case-by-case fashion, are continually assigned to parallel series of production tasks considering the individual skill and task urgency factors, at the qualitative and reactive level called Worker Selection Guide (WOSEG). When developing such an integrated model, four holonic control attributes need to be observed, namely real-time control, event-driven control, intelligent control, and distributed control. These control attributes help ensure the effective and sustainable improvement of factory processes, for which the strengths of and the interactions between holonic elements are discussed in this paper. Keywords: Holonic control, workforce sizing, worker selectio

Simulation study of a semi-automated flexible production line

In today’s highly competitive and challenging marketplace, manufacturing process improvement is more important than ever before. Conversely, it is probably also harder to achieve than at any time in the past. This is due to several factors. High levels of capital investment combined with short product life cycles mean that maximising utilisation levels of expensive equipment is essential. Increasingly complex production facilities are difficult to analyse and improve. The possibility of worsening the situation rather than improving it means that experimentation on the line itself is often a risk not worth taking. One solution to this problem is the use of computer based manufacturing system simulation. Simulation studies are beneficial because they remove the element of risk associated with experimentation. Potential process improvement strategies can be identified, evaluated, compared and chosen in a virtual environment before eventual implementation on the factory floor. This research aimed to evaluate the use of discrete event system simulation in a real world manufacturing environment. To this end, a flexible simulation model of the main transfer line of Läpple Ireland, a large metal panel production facility, was designed and constructed using Extend simulation software. In conjunction with Läpple personnel, various ‘what if’ scenarios were identified and evaluated. These scenarios were aimed at deciding the best position for providing additional automation by investing in robots. From the results of the simulation modelling of the three main proposed modifications to the line, improvements of 9%, 18% and 33% in press line throughput were predicted. The negative effect on these improvements in the case that the proposed robots failed to achieve the desired speeds were evaluated. These negative effects were found to be not as dramatic as could be expected. The results were compared to those of similar research efforts elsewhere. Finally, future steps for the research to take were identified and suggestions for future areas of application for the model were made

A Capacity Planning Simulation Model for Reconfigurable Manufacturing Systems

Important objectives and challenges in today’s manufacturing environment include the introduction of new products and the designing and developing of reconfigurable manufacturing systems. The objective of this research is to investigate and support the reconfigurability of a manufacturing system in terms of scalability by applying a discrete-event simulation modelling technique integrated with flexible capacity control functions and communication rules for re-scaling process. Moreover, the possible extension of integrating the discrete-event simulation with an agent-based model is presented as a framework. The benefits of this framework are collaborative decision making using agents for flexible reaction to system changes and system performance improvement. AnyLogic multi-method simulation modelling platform is utilized to design and create different simulation modelling scenarios. The developed capacity planning simulation model results are demonstrated in terms of a case study using the configurable assembly Learning Factory (iFactory) in the Intelligent Manufacturing Systems (IMS) Center at the University of Windsor. The main benefit of developed capacity planning simulation in comparison to traditional discrete-event simulation is, with a single simulation run, the recommended capacity for manufacturing system will be determined instead of running several discrete-event simulation models to find the needed capacity