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.
Supply chain optimization under risk and uncertainty: A case study for high-end server manufacturing
We propose an approach for supply chain optimization under risk and uncertainty.Simulation and optimization models are combined through an iterative procedure.A software application is developed based on the proposed approach.To validate the proposed approach, a case study is provided. This paper presents an approach and a software application for supply chain optimization under risk and uncertainty. The proposed approach combines simulation and optimization techniques for managing risks in supply chains. A multi-objective optimization model is developed which considers the deterministic features of the supply chain. A simulation model is used to represent the stochastic features of the supply chain. Both models communicate to achieve the best values for profit, lead time and risk reduction by selecting a combination of mitigation strategies and allocating orders and inventory. A case study from a high-end server manufacturing environment is used to demonstrate the validity of the proposed approach. The analytical results show clear trade-offs among the three objectives where changing the risk reduction goal value will affect the total profit and lead time. The proposed approach helps decision makers identify the best risk mitigation strategies and allocate inventory and customer orders effectively.
A note on bounds for direct shipping cost
The bounds on direct shipping cost developed by Gallego and Simchi-Levi are used in this note to demonstrate that it is difficult to rule out multiple stop inventory-routing strategies, even when the optimal shipment size for direct shipping is a full vehicle.