Tzu-Li Chen

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.

A Hierarchical Planning and Scheduling Framework for TFT-LCD Production Chain

This paper presents a hierarchical planning and scheduling framework for Thin Film Transistor Liquid Crystal Display (TFT-LCD) production chain in an assembly-to-order (ATO) environment. The TFT-LCD manufacturing process comprises three sequential steps, namely TFT-LCD production chain: Array, Cell and Module process. Many special characteristics and constraints, such as product grade constraint, site-eligibility constraint and key material available constraint, are inherited in this production chain. The globally distributed nature of production planning and scheduling activities of TFT-LCD production also leads to an urgent need of an integrated planning and scheduling framework to balance supply and demand problems. The hierarchical framework divides planning hierarchies into three planning levels, identifying planning modules in each level in attempt to balance the supply and demand of the TFT-LCD production chain. Finally, an example is illustrated to demonstrate the relationship among the planning modules in the framework

Capacity and Product Mix Planning Problem for TFT Array Multi-Plant

ABSTRACT This research focuses on the strategic capacity and product mix planning problems in Thin Film Transistor Liquid Crystal Display (TFT-LCD) Panel Industry. A capacity and product mix planning decision model for TFT Array multi-plant is proposed according to TFT-LCD panel industry environment. Capacity and product mix planning problems for TFT Array multi-plant are resource allocation problems. Product mix and production quantity for each product group produced in a specific plant are decided on capacity and product mix planning. In the planning, the objective is maximum contribution margin. And, we consider many constraints to obtain the capacity and product mix plan, such as material utilization, production capacity, production capability, production variable costs, and inventory-driven costs for each product group produced in a specific plant. Furthermore, we use capacity and product mix planning to balanced the difference between total supply capacity and total demand forecast. If total supply capacity is smaller than total demand forecast, we evaluate the feasibility to expand production capacity. On the other hand, we suggest increasing production quantity in over-supply plant. We propose a methodology to solve capacity and product mix planning problems for TFT Array multi-plant. There are three phases in the methodology, including (1) capacity configuration, (2) capacity expansion, and (3) capacity exploitation. In the first phase, we allocate production capacity in each plant to produce a suit product mix set. If demand forecast is greater than supply capacity for a specific product group, we evaluate the feasibility to expand production capacity in the second phase. In the third phase, we suggest best additional production quantity of the profitable product group to exploit the remained capacity adequately.

A genetic algorithm for the multi-stage and parallel-machine scheduling problem with job splitting – A case study for the solar cell industry

This paper studies a multi-stage and parallel-machine scheduling problem with job splitting which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The HFS has one common hypothesis, one job on one machine, among the research. Under the hypothesis, one order cannot be executed by numerous machines simultaneously. Therefore, multiprocessor task scheduling has been advocated by scholars. The machine allocation of each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage is determined. However, machine allocation and production sequence decisions are highly interactive. As a result, this study, motivated from the solar cell industry, is going to explore these issues. The multi-stage and parallel-machine scheduling problem with job splitting simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically splitting a job into several sublots to be processed on multiple machines. We formulate this problem as a mixed integer linear programming model considering practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. A preliminary computational study indicates that the developed algorithm not only provides good quality solutions but outperforms the classic branch and bound method and the current heuristic in practice.

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

Transferring cognitive apprenticeship to manufacturing process knowledge management system

Purpose – This study aims to develop a manufacturing process management system which aims to benefit the excavation, collection and search of the mentors’ experience and knowledge. The coating painting industry is a typical small and medium-sized traditional industry and usually depends on masters experience to solve the manufacturing problem. Design/methodology/approach – Based on the characteristics and manufacturing process of the architectural coating industry, this study develops a practical knowledge management system (KMS) with two stages. The first stage collects and analyzes manufacturing process data, and the second stage constructs the KMS of the manufacturing process. Findings – This manufacturing process KMS can accumulate and share the operators’ experience and knowledge systematically; this KMS not only improves the apprentices’ skill and problem-solving abilities but also enhances the enterprise’s overall product quality, undoubtedly. Research limitations/implications – This manufacturing...

A capacity planning model for TFT-LCD production networks

The paper presents a capacity planning model for TFT-LCD production network. The capacity planning decision of TFT-LCD manufacturing is to simultaneously seek an optimal capacity allocation plan and capacity expansion policy. Capacity allocation decides on profitable product mixes and allocated production quantities of each product group at each production site. Capacity expansion concerned with determining the timing, types and sizes of capacity investments, especially in the procurement of new bottleneck machines and acquisition of new auxiliary tools. This study proposes a mixed integer linear programming (MILP) to formulate the capacity planning problem which considers many practical characteristics of TFT-LCD manufacturing such as multi-generation & multi-site production network, two capacity types, economic cutting ratio, and high capacity expansion costs. Finally, a case study modified from a Taiwan TFT-LCD manufacturer is illustrated.

Genetic Algorithm Approach for Multi-Objective Optimization of Closed-Loop Supply Chain Network

This paper applies multi-objective genetic algorithm (MOGA) to solve a closed-loop supply chain network design problem with multi-objective sustainable concerns. First of all, a multi-objective mixed integer programming model capturing the tradeoffs between the total cost and the carbon dioxide (CO2) emission is developed to tackle the multi-stage closed-loop supply chain design problem from both economic and environmental perspectives. The multi-objective optimization problem raised by the model is then solved using MOGA. Finally, some experiments are made to measure the performance.

A Capacity Planning Model in the TFT-LCD Production Chain

The paper studies the capacity planning problem in the TFT-LCD production chain. Due to two significant trends, the capacity planning decision gradually becomes an important strategic issue to which TFT-LCD industry paid attention. First is the increase of product types which causes a wide range of product groups, such as mobile, monitor, notebook, TV and industrial display are produced. The second trend is the advances of new technology which cause multiple generations of technologies coexist in each manufacturing stage and production site. This paper proposes a mixed integer linear programming (MILP) to formulate the capacity planning which considers many practical characteristics and constraints in TFT-LCD production chain. A heuristic algorithm is developed to solve the MILP model.

Multi-stage Parallel Machines and Lot-Streaming Scheduling Problems – A Case Study for Solar Cell Industry

This research focuses on a parallel machines scheduling problem considering lot streaming which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be determined in advance. In addition, the size of each sublot is splited by parallel machines configuration. However, allocation of machine resources, sublot size and lot sequence are highly mutual influence. If allocation of machine resources has been determined, adjustment on production sequence is unable to reduce production makespan. Without splitting a given job into sublots, the production scheduling cannot have overlapping of successive operations in multi-stage parallel machines environment thereby contributing to the best production scheduling. Therefore, this research motivated from a solar cell industry is going to explore these issues. The multi-stage and parallel-machines scheduling problem in the solar cell industry simultaneously considers the optimal sublot size, sublot sequence, parallel machines sublot scheduling and machine configurations through dynamically allocating all sublot to parallel machines. We formulate this problem as a mixed integer linear programming (MILP) model considering the practical characteristics including parallel machines, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. A hybrid-coded particle swarm optimization (HCPSO) is developed to find a near-optimal solution. At the end of this study, the result of this research will compare with the optimization method of mixed integer linear programming and case study.