An integrated simulation–optimization study for consolidating production lines in a configure-to-order production environment
This study investigates production lines consolidation in a configure-to-order production environment using simulation and optimization strategies. The current production environment has three separate production lines that will be consolidated into two production lines. Two production line configurations are analyzed: product and process layouts. Product layout has a continuous smooth flow, small work-in-process (WIP), short total production time per unit of product, and reduced material handling requirements. On the contrary, process layout offers more flexibility, allows for higher utilization of machines, and requires higher grades of operators’ skills. A simulation-based comparison of the current and future systems is conducted considering lead time, throughput, utilization, and WIP. A mixed integer programming model that optimizes the transportation cost and waiting time is then formulated based on the selected process layout. Simulation results provide recommendations to improve the system level performance measures and support management decisions. The current transportation schedule is ineffective, and hence a real-time dashboard based on the optimization model is suggested. The dashboard will improve communication and transportation between the two production lines.
A framework for selecting and evaluating process improvement projects using simulation and optimization techniques
Selection of process improvement initiatives can be a challenging task. Process improvement projects usually fall into the following categories: Lean, Six Sigma, Lean Six Sigma, Change Management, and Business Process Reengineering. The selection process of these projects is a multi-criteria decision making process which involves multiple conflicting objectives. In this study, we develop an optimization model to select process improvement projects taking into consideration resource availability, required skills, and budget constraints. In addition, discrete event simulation (DES) models are developed to evaluate some of the selected projects. The DES models account for the uncertainty in the system and allow for performing scenario analysis on the selected projects. To validate the proposed approach, we provide a case study from a high-end server manufacturing environment. Results can be used to enhance the decisions on selecting process improvement projects.
Comparison of bottleneck detection methods for AGV systems
The performance of a manufacturing or logistic system is determined by its constraints. Therefore, in order to improve the performance, it is necessary to improve the constraints, also known as the bottlenecks. Finding the bottlenecks, however, is not easy. We compare the two most common bottleneck detection methods, based on the utilization and the waiting time, with the shifting bottleneck detection method developed by us, for AGV systems. We find that the two conventional methods have many shortcomings compared to the shifting bottleneck detection method. In the example presented here, conventional methods are either unable to detect the bottleneck at all or detect the bottleneck incorrectly. The shifting bottleneck detection method not only finds the bottlenecks but also determines the magnitude of the primary and secondary bottlenecks.