Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operatorâtask assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components

Modeling and optimization of supply chain cost of responsiveness

In today\u27s ever increasing competitive business world, a responsive Supply Chain (SC) should adapt itself quickly to customer demands resulting into maximum benefits to all its primary stakeholders. The objective of this work is to provide a managerial tool that optimizes the cost of responsiveness of supply chains where various transportation durations are present between the SC components, and to determine the weakest links in the SC that need strengthening for elevating the overall responsiveness. For these objectives a mathematical model was formulated and solved using CPLEX. Assessing supply chain\u27s responsiveness is discussed in this work as well using the cost of responsiveness, SC output rate and production slack times. The computational results show that the mathematical model is effective in planning and synchronizing production, shipping and storage in a supply chain from start to end so that the cost of responsiveness is minimized while customer demands are fulfilled under limited outsourcing

Advanced Planning and Scheduling in the United States Air Force Depot-Level Maintenance

In the post cold war environment, the rapid deployment of combat capability is critical. Deployment lift capability is limited, however, so the real-time selection of the optimal combat asset mix that balances capability provided and sustainment required has become paramount. In this model, the value of a force mix is determined by the sum of the individual weapon system suitabilities against their assigned missions. The value is constrained by the numerical limits on the items required to create and support the force mix, and the lift required to move these items. The research considered heuristic and complete enumeration methods against the problem structure to develop a decision support model that expedites the selection of the best overall force mix. War planners are provided a decision support tool that objectively compares alternative force mix packages and selects the optimal asset mix in a reasonable amount of time while explicitly considering logistics constraints. This demonstrates the feasibility of an approach that integrates intelligence, operations, and logistics issues into a single decision support and planning tool for force mix decisions

Theory and Practice of Supply Chain Synchronization

In this dissertation, we develop strategies to synchronize component procurement in assemble-to-order (ATO) production and overhaul operations. We focus on the high-tech and mass customization industries which are not only considered to be very important to create or keep U.S. manufacturing jobs, but also suffer most from component inventory burden. In the second chapter, we address the deterministic joint replenishment inventory problem with batch size constraints (JRPB). We characterize system regeneration points, derive a closed-form expression of the average product inventory, and formulate the problem of finding the optimal joint reorder interval to minimize inventory and ordering costs per unit of time. Thereafter, we discuss exact solution approaches and the case of variable reorder intervals. Computational examples demonstrate the power of our methodology. In the third chapter, we incorporate stochastic demand to the JRPB. We propose a joint part replenishment policy that balances inventory and ordering costs while providing a desired service level. A case study and guided computational experiments show the magnitudes of savings that are possible using our methodology. In the fourth chapter, we show how lack of synchronization in assembly systems with long and highly variable component supply lead times can rapidly deteriorate system performance. We develop a full synchronization strategy through time buffering of component orders, which not only guarantees meeting planned production dates but also drastically reduces inventory holding costs. A case study has been carried out to prove the practical relevance, assess potential risks, and evaluate phased implementation policies. The fifth chapter explores the use of condition information from a large number of distributed working units in the field to improve the management of the inventory of spare parts required to maintain those units. Synchronization is again paramount here since spare part inventory needs to adapt to the condition of the engine fleet. All needed parts must be available to complete the overhaul of a unit. We develop a complex simulation environment to assess the performance of different inventory policies and the value of health monitoring. The sixth chapter concludes this dissertation and outlines future research plans as well as opportunities

Plethora : a framework for the intelligent control of robotic assembly systems

Plethora : a framework for the intelligent control of robotic assembly system