Cheng-Hung Wu

A model for batch available-to-promise in order fulfillment processes for TFT-LCD production chains

The main purpose of this study is to explicitly highlight several special production characteristics in a thin-film transistor liquid crystal display (TFT-LCD) manufacturing industry and to present an available-to-promise (ATP) model that supports decision-making in order fulfillment processes for TFT-LCD manufacturing. A TFT-LCD production chain differs from others in its special production characteristics such as alternative bill-of-materials (BOMs), grade transition, etc., which are significant factors driving a success in an ATP implementation. Customers may specify a quality level and the materials to be used in a finished product in inquiry orders. The quality of the working-in-process can be altered using different assembled components. The ATP model enhances the responsiveness of order fulfillment processes. The ATP model directly links available material resources and capacity with inquiries or existing customer orders to improve the overall performance of the production chain. A case study using the model demonstrates the effectiveness and efficiency of the proposed ATP model in a TFT-LCD production chain and investigates the sensitivity of TFT-LCD plant performance to changes in order batching intervals.

A stochastic programming model for strategic capacity planning in thin film transistor-liquid crystal display (TFT-LCD) industry

This paper studies strategic capacity planning problems under demand uncertainties in thin film transistor-liquid crystal display (TFT-LCD) industry. Due to the following trends, capacity planning has become a critical strategic issue in TFT-LCD industry: (1) complex product hierarchy and product types caused by a wide range of product applications; (2) coexistence of multiple generation of manufacturing technologies in a multi-site production system; and (3) rapid growing and changing market demand derived by the needs for replacing traditional cathode ray tube (CRT) display. Furthermore, demand forecasts are usually inaccurate and vary rapidly over time. Our research objective is to seek a capacity allocation and expansion policy that is robust to demand uncertainties. We consider special characteristics of TFT-LCD manufacturing systems such as demand uncertainties, limited configuration flexibility, and cutting ratios. This paper proposes a scenario-based two-stage stochastic programming model for strategic capacity planning under demand uncertainties. Comparing to the deterministic approach, our stochastic model significantly improve system robustness under demand uncertainties.

Dynamic production control in a serial line with process queue time constraint

This research examines the production control problem in two-station tandem queueing systems under time constraints. In these two-station tandem queueing systems, jobs must first be processed at the upstream station and then the downstream station. For each job, the sum of the waiting and processing time in the downstream queue is limited by an upper bound. This time constraint is called the process queue time constraint. When the process queue time constraint is violated, a significant scrap cost will be accrued. In this research, we develop a Markov decision model to study the production control problem under process queue time constraints. The objective is to minimise the sum of the expected long-run average inventory holding costs and scrap costs. According to the Markov decision model, an interesting exhaustive structure of the optimal production control policy is found. Based on this exhaustive structure, an efficient algorithm is developed to solve the production control problem numerically. The performance of the proposed algorithm is verified by a simulation study. Compared with other heuristics in the literature, the proposed algorithm can significantly reduce production costs while improving system throughput and utilisation.

Dynamic vehicle allocation control for automated material handling system in semiconductor manufacturing

Abstract The current study examines the dynamic vehicle allocation problems of the automated material handling system (AMHS) in semiconductor manufacturing. With the uncertainty involved in wafer lot movement, dynamically allocating vehicles to each intrabay is very difficult. The cycle time and overall tool productivity of the wafer lots are affected when a vehicle takes too long to arrive. In the current study, a Markov decision model is developed to study the vehicle allocation control problem in the AMHS. The objective is to minimize the sum of the expected long-run average transport job waiting cost. An interesting exhaustive structure in the optimal vehicle allocation control is found in accordance with the Markov decision model. Based on this exhaustive structure, an efficient algorithm is then developed to solve the vehicle allocation control problem numerically. The performance of the proposed method is verified by a simulation study. Compared with other methods, the proposed method can significantly reduce the waiting cost of wafer lots for AMHS vehicle transportation.

Multiple product admission control in semiconductor manufacturing systems with process queue time (PQT) constraints

This research develops production control methods for multi-product systems under process queue time (PQT) constraints.A single flexible upstream workstation and multiple dedicated downstream workstations are considered.Under the PQT constraint, jobs are scrapped if the waiting time before a queue is longer than a pre-defined upper limit.An innovative multiple product admission control heuristic is developed using Markov decision processes.The proposed MPAC method reduces production costs and scrap counts by 33.8% and 59.5% in our numerical study. In a multi-product manufacture system, the complexity of production control increases. Because multiple products are competing for limited resource, managers need to dynamically adjust production priority based on customer demand and distribution of work-in-processes (WIP). The production control in multi-product system becomes even more complicated when process queue time (PQT) constraints exist. Under PQT constraints, administrators setup upper limits for the waiting time between specific manufacturing steps to ensure quality of products. This upper limit of waiting time makes dynamic production control very challenging.The objective of this paper is to develop production control methods for multi-product systems subject to PQT constraints. However, it is computationally infeasible to solve multiple product problems in a single dynamic optimization model, and we therefore first formulate a single product admission control problems using Markov decision processes (MDP). Based on observations from the single product MDP model, an innovative multiple product admission control (MPAC) heuristic is developed. In simulation study, we compare the performance of MPAC with other popular dispatching methods in literature. Compare to other control methods in literature, the proposed (MPAC) method can reduce production costs by at least 33.8% and reduce scrap count by at least 59.5% in average.

Robust production and transportation planning in thin film transistor-liquid crystal display (TFT-LCD) industry under demand and price uncertainties

This paper studies the production and transportation control (PTC) problems under demand and price uncertainties in a multi-product, two-echelon, and hybrid thin film transistor-liquid crystal display (TFT-LCD) production chain. The unique characteristics of the TFT-LCD industry, including quality grades and alternative bill-of-materials (BOMs), are considered in this study. The objective is to determine a production and transportation policy that is robust to price and demand uncertainties. This two-echelon PTC problem is formulated as several two-stage robust optimisation models. An iterative procedure is developed to apply and solve these two-stage models in a TFT-LCD production chain. Our simulation study shows that the proposed method can significantly increase profits while reducing transportation and inventory costs. In addition to improving mean and variance of profits, we use several risk measures to verify the robustness of the iterative optimisation procedure.

Optimization and economic analysis for tool portfolio planning in semiconductor manufacturing

The tool portfolio of a plant refers to the makeup, in quantity and type, of processing machines in the plant. Portfolio planning is a multi-criteria decision-making task involving trade-offs between investment cost, throughput, cycle time and risk. In this paper, an economic decision model is first presented for optimal configuration of portfolio and to determine optimal factory loading. If plants are closely located or have a twin-fab design, portfolio planning at multiple plants can be integrated to enhance the overall effectiveness of portfolios. A novel methodology for arbitrating capacity backup between multiple plants is described in the second part. Finally, robust configuration of portfolio in a dynamic demand environment is addressed. Industry data have been utilized to run through the developed methodologies.