Matching Items (3)
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- All Subjects: Scheduling
- Creators: Fowler, John W
Description
A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions.
ContributorsLi, Qing (Author) / Fowler, John W (Thesis advisor) / Mohan, Srimathy (Thesis advisor) / Gopalakrishnan, Mohan (Committee member) / Askin, Ronald G. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2010
Description
Ramping up a semiconductor wafer fabrication facility is a challenging endeavor. One of the key components of this process is to schedule a large number of activities in installing and qualifying (Install/Qual) the capital intensive and sophisticated manufacturing equipment. Activities in the Install/Qual process share multiple types of expensive and scare resources and each activity might potentially have multiple processing options. In this dissertation, the semiconductor capital equipment Install/Qual scheduling problem is modeled as a multi-mode resource-constrained project scheduling problem (MRCPSP) with multiple special extensions. Three phases of research are carried out: the first phase studies the special problem characteristics of the Install/Qual process, including multiple activity processing options, time-varying resource availability levels, resource vacations, and activity splitting that does not allow preemption. A modified precedence tree-based branch-and-bound algorithm is proposed to solve small size academic problem instances to optimality. Heuristic-based methodologies are the main focus of phase 2. Modified priority rule-based simple heuristics and a modified random key-based genetic algorithm (RKGA) are proposed to search for Install/Qual schedules with short makespans but subject to resource constraints. Methodologies are tested on both small and large random academic problem instances and instances that are similar to the actual Install/Qual process of a major semiconductor manufacturer. In phase 3, a decision making framework is proposed to strategically plan the Install/Qual capacity ramp. Product market demand, product market price, resource consumption cost, as well as the payment of capital equipment, are considered. A modified simulated annealing (SA) algorithm-based optimization module is integrated with a Monte Carlo simulation-based simulation module to search for good capacity ramping strategies under uncertain market information. The decision making framework can be used during the Install/Qual schedule planning phase as well as the Install/Qual schedule execution phase when there is a portion of equipment that has already been installed or qualified. Computational experiments demonstrate the effectiveness of the decision making framework.
ContributorsCheng, Junzilan (Author) / Fowler, John W (Thesis advisor) / Kempf, Karl (Thesis advisor) / Mason, Scott J. (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2015