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.     

Quantity and Due Date Quoting Available to Promise

The available to promise (ATP) function has increasingly attracted the attention of the supply chain management research community as a tool for enhancing the responsiveness of order promising and the reliability of order fulfillment. It directly links available resources, including both material and capacity, with customer orders and, thus, affects the overall performance of a supply chain. In this paper, a mixed integer programming (MIP) model for a quantity and due date quoting ATP mechanism is presented. This model can provide individual order delivery dates for a batch of customer orders that arrive within a predefined batching interval. In addition, the model allows customized configurations and takes into account a variety of realistic supply chain constraints, such as material compatibility, substitution preferences, capacity utilization, and material reserve. We conclude this paper with sensitivity analysis of performance impacts with respect to batching interval size and material reserve policy.     

A research on problems of mixed-line production and the re-scheduling

This paper is to establish and solve the re-scheduling problems under a flow-shop mixed-line production planning. A case study of the final stage, module manufacturing, of TFT-LCD is provided for illustration of the developed mechanism. In this research, the mixed-line production system and its rescheduling problems are discussed. The buffer management and the DBR scheduling methods based on the Theory of Constraints are used to detect, identify, and level the bottleneck problems in the system. The direct contribution of the results is to increase the production flexibility and mobility of the manufacturing scheduling system and to benefit the entire members of supply chain system. The simulation software, Flexsim, is used to construct and evaluate the developed model, some phenomena of simulated system performance on the maximum delay of orders, the total cost of delay, and make span are discussed.     

基于动态MANCOVA的混合制程虚拟测量算法

在晶圆/液晶面板等批次加工过程中,产品质量的及时估计与品质管制是提高产能和降低成本的有效途径.针对"少量多样"的混合制程,利用逐步回归算法挑选该制程的关毽变量,引入产品的效益因子,建立混合制程的虚拟测量模型;为克服系统扰动对模型精度的影响,以产品效益因子为状态量建立该制程的状态方程,利用Kalman滤波器递归估计模型参数得到动态的MANCOVA模型;最后通过某湿式蚀刻制程的工程应用验证了该算法的有效性.     

Optimization of Order Fulfillment in Distribution Network Problems

This paper focuses on optimization of order due date fulfillment reliability in multi-echelon distribution network problems with uncertainties present in the production lead time, transportation lead time, and due date of orders. Reliability regarding order due date fulfillment is critical in customer service, and customer retention. However, this reliability can be seriously influenced by supply chain uncertainties, which may induce tardiness in various stages throughout the supply chain. Supply chain uncertainty is inevitable, since most input values are predicted from historical data, and unexpected events may happen. Hence, a multi-criterion genetic integrative optimization methodology is developed for solving this problem. The proposed algorithm integrates genetic algorithms with analytic hierarchy process to enable multi-criterion optimization, and probabilistic analysis to capture uncertainties. The optimization involves determination of demand allocations in the network, transportation modes between facilities, and production scheduling in manufacturing plants. A hypothetical three-echelon distribution network is studied, and the computation results demonstrated the reliability of the proposed algorithms. Received: October 2004 / Accepted: September 2005     

Autonomous negotiation for resource allocation of multiple sectors in the TFT-LCD manufacturing industry

In a global TFT-LCD manufacturing enterprise, a central planning sector promises orders to maximize profit, while the production sector is responsible for minimizing costs and meeting delivery deadline. The two sectors frequently struggle with different preferences. The contradictory objectives raise conflict between the two sectors when developing an entire resource allocation plan for the enterprise. This study presents a novel negotiation framework and develops a mutually acceptable resource allocation plan via autonomous negotiation between sectors with different preferences. A mathematical model considering the major characters of the TFT-LCD industry is formulated. Individual sectors can employ preferred negotiation tactics to achieve their objectives with an acceptable level of trade-off. This study examines various negotiation tactics and compares the proposed decentralized model with a centralized solution. Negotiation experiments demonstrate a good resource allocation plan over a short time and the conflict between sectors can be resolved efficiently. The proposed autonomous negotiation facilitates smart operations management.     

Heuristic approaches for master planning in semiconductor manufacturing

In this paper, we propose heuristic approaches for solving master planning problems that arise in semiconductor manufacturing networks. The considered problem consists of determining appropriate wafer quantities for several products, facilities, and time periods by taking demand fulfillment (i.e., confirmed orders and forecasts) and capacity constraints into account. In addition, fixed costs are used to reduce production partitioning. A mixed-integer programming (MIP) formulation is presented and the problem is shown to be NP-hard. As a consequence, two heuristic procedures are proposed: a product based decomposition scheme and a genetic algorithm. The performance of both heuristics is assessed using randomly generated test instances. It turns out that the decomposition scheme is able to produce high-quality solutions, while the genetic algorithm achieves results with reasonable quality in a short amount of time.     

Capacity planning with ant colony optimization for TFT-LCD array manufacturing

Array manufacturing in thin film transistor-liquid crystal display (TFT-LCD) production network is characterized as a capital-intensive and capacity-constrained production system with re-entrance and batch operations. Effectively using associated machines through optimal capacity planning and order scheduling decisions is a critical issue for array manufacturing. This study develops a capacity planning system (CPS) for TFT-LCD array manufacturing. CPS uses information including master production schedule, order due date, process routing, processing time, and number of machines. In addition, CPS derives the order release time, estimated machine start and finish time, machine allocation, and order completion time to maximize machine workload, improve lateness, and eliminate setup time. This research also develops ant colony optimization (ACO) to seek the optimal order release schedule to maximize a combination of the above objectives. The preliminary experiments are first applied to identify the optimal tuning parameters of the ACO algorithm. Computational experiments are then conducted to evaluate the significance and the robustness of the proposed algorithm compared with other competitive algorithms by full factorial experimental design.     

An integrated algorithm for cutting stock problems in the thin-film transistor liquid crystal display industry

The cutting stock problem (CSP) is a critical issue in the manufacturing of thin film transistor liquid crystal display (TFT-LCD) products. Two manufacturing processes are utilized in this industry: (1) various TFT-LCD plates are cut from a glass substrate based on cutting patterns, and (2) the number of glass substrates required to satisfy customer requirements is minimized. The current algorithm used to select the cutting pattern is defined as a mixed integer program (MIP). Although the current MIP method yields an optimal solution, but the computation time is unacceptable when the problem scale is large. To accelerate the computation and improve the current method, this study proposes an integrated algorithm that incorporates a genetic algorithm, a corner arrangement method, and a production plan model to solve CSPs in the TFT-LCD industry. The results of numerical experiments demonstrate that the proposed algorithm is significantly more efficient than the current method, especially when applied to large-scale problems.     

A capacity allocation and expansion model for TFT-LCD multi-site manufacturing

This paper presents a capacity allocation and expansion problem of thin film transistor liquid crystal display (TFT-LCD) manufacturing in a multi-site environment. Capacity allocation and expansion decisions have become more challenging issues because of the following: (1) complex product hierarchy caused by a wide range of product types and applications, (2) coexistence of multiple generations of manufacturing technologies in the multi-stage production network, and (3) rapidly growing and changing market demands due to the replacement of the traditional cathode ray tube. Since the Array stage is the bottleneck of this production network, our research objective is to simultaneously seek an optimal capacity allocation plan and capacity expansion policy under single-stage, multi-generation, and multi-site structures. Capacity allocation determines the profitable product mixes and allocated production quantities for each product group at each production site. Capacity expansion determines timing, types, and sizes of capacity investments, especially in the acquisition of auxiliary tools. This study proposes a mixed integer linear programming to formulate the whole model of capacity allocation and expansion, which considers the practical characteristics and constraints of TFT-LCD manufacturing. Finally, an industrial case study modified from a Taiwanese TFT-LCD manufacturer is illustrated and a sensitivity analysis of some influential parameters is also addressed.     

A First-Order Hybrid Petri Net Model for Supply Chain Management

A supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategies.     

A non-linear quality improvement model using SVR for manufacturing TFT-LCDs

Thin Film Transistor??Liquid Crystal Displays (TFT-LCDs) are widely used in TVs, monitors, and PDAs. The key process of producing a TFT-LCD is using alignment to combine a Thin Film Transistor (TFT) panel with a Color Filter (CF) panel, which is called ??celling??. The defined cell vernier, which indicates the alignment error, is an important quality index in the manufacturing process. In the CF manufacturing process, the cell vernier is difficult to control because it depends on six TPEs (Total Pitch Errors), with each TPE highly dependent on the others. This paper aims to improve the cell vernier forecasting model with the six TPE attributes to enhance the production yield in the CF manufacturing process. Using the six dependent variables, this study found that the SVR (Support Vector Machine for Regression) model is the fittest for generating quality results that meet the designed specifications.     

The order fulfillment planning problem considering multi-site order allocation and single-site shop floor scheduling

The order fulfillment planning process in the thin film transistor–liquid crystal display panel industry is analyzed in this study. A two-phase order fulfillment planning structure is proposed, including the multi-site order allocation among module factories and single-site shop floor scheduling in each factory. In the first phase, the order allocation problem is solved using a mathematical programming model considering practical characteristics, including product structures, customer preferences, alternative bill-of-material, and production constraints. In the second phase, a constraint-based simulation scheduling algorithm is developed to address the scheduling problem in each module factory for determining the ideal order release time. Since production planning and scheduling are dealt with different time scales, the major challenge for the integration lies in the large problem size of the optimization model and becomes intractable. Most of the time bucket-based planning methods in the past literature simplify their scheduling models, but in this paper the detailed shop floor operations and processing behaviors are considered, such as changeover time, processing sequence of orders, and machine characteristics. Finally, a practical case in Taiwan will be employed to testify the feasibility of the proposed order fulfillment planning process; meanwhile, through the analysis of experiments, the adaptability and comparison of different planning approaches in an environment of various market demands are discussed.     

Integrated Vehicle Configuration System—Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.     

Applying the IPA and DEMATEL models to improve the order-winner criteria: A case study of Taiwan’s network communication equipment manufacturing industry

The purpose of this study is to use the gap-analysis method to calculate the performance of quality characteristics and apply the multiple regression analysis method to establish the overall level of satisfaction and implicit importance of quality characteristics. In addition we will use the decision making trial and evaluation laboratory (DEMATEL) method to analyze the cause-effect relationship and level of influence among different quality characteristics in order to make revisions to the traditional IPA model and find the core problems that are involved with winning orders. The methodology that we propose for this research not only makes revisions to the IPA model’s method of directly using explicit information from customer responses, we also solved the influence of the cause-effect relationships of quality characteristics. For our research we analyzed the case of a company from Taiwan’s network communication equipment manufacturing industry in order to make our corrections to the IPA model and gain the benefits of using the DEMATEL model. The findings of our research show that with the corrections to the IPA and the methodology of DEMATEL we can find and correct the core problems of the company that was analyzed, improve quality control and therefore improve the company’s ability to win and be compatible for orders.     

同时具有学习和遗忘效应的TFT-LCD模块组装调度问题研究

薄膜晶体管液晶显示器（TFT-LCD）制造过程特点包含三个部分：前段阵列（Array）过程、后段面板成盒（Cell）过程、模块组装（Module）过程。针对薄膜晶体管液晶显示器模块组装生产规模性、精密性、重复性的特点,引入相关工件学习效应和遗忘效应,以极小化工件最大工件完工时间为目标,讨论TFT-LCD模块组装调度问题。采用新型的布谷鸟智能优化算法对当前模型进行求解,通过对算法进行模拟仿真实验,验证了布谷鸟算法在求解模块调度问题上的有效性和可行性。此外,针对学习效应和遗忘效应对模块组装调度的影响,提出了相应的调度建议,为薄膜晶体管液晶显示器的研究和实际生产提供了参考。     

Bilevel model for production–distribution planning solved by using ant colony optimization

This paper addresses a hierarchical production–distribution planning problem. There are two different decision makers controlling the production and the distribution processes, respectively, that do not cooperate because of different optimization strategies. The distribution company, which is the leader of the hierarchical process, controls the allocation of retailers to each depot and the routes which serve them. In order to supply items to retailers, the distribution company orders from the manufacturing company the items which have to be available at the depots. The manufacturing company, which is the follower of the hierarchical process, reacts to these orders deciding which manufacturing plants will produce them. A bilevel program is proposed to model the problem and an ant colony optimization based approach is developed to solve the bilevel model. In order to construct a feasible solution, the procedure uses ants to compute the routes of a feasible solution of the associated multi-depot vehicle route problem. Then, under the given data on depot needs, the corresponding production problem of the manufacturing company is solved. Global pheromone trail updating is based on the leader objective function, which involves costs of sending items from depots to retailers and costs of acquiring items from manufacturing plants and unloading them into depots. A computational experiment is carried out to analyze the performance of the algorithm.     

VCS: value chains simulator, a tool for value analysis of manufacturing enterprise processes (a value-based decision support tool)

Manufacturing enterprises are facing a competitive challenge. This paper proposes the use of a value chain based approach to support the modelling and simulation of manufacturing enterprise processes. The aim is to help experts to make relevant decisions on product design and/or product manufacturing process planning. This decision tool is based on the value chain modelling, by considering the product requirements. In order to evaluate several performance indicators, a simulation of various potential value chains adapted to market demand was conducted through a Value Chains Simulator (VCS). A discrete event simulator is used to perform the simulation of these scenarios and to evaluate the value as a global performance criterion (balancing cost, quality, delivery time, services, etc.). An Analytical Hierarchy Process module supports the analysis process. The value chain model is based on activities and uses the concepts of resource consumption, while integrating the benefiting entities view point. A case study in the microelectronic field is carried out to corroborate the validity of the proposed VCS.     

Real-time capacity requirement planning for make-to-order manufacturing with variable time-window orders

In a make-to-stock (MTS) manufacturing environment using material requirement planning (MRP), checking the capacity feasibility of a master production schedule (MPS) requires capacity requirement planning (CRP) that can be easily calculated. The time window of an order is the time interval from its ready date to its due date. In a make-to-order (MTO) manufacturing environment, the CRP method checks whether a set of orders with different time windows can be scheduled for timely completion. This corporate-level CRP problem has long perplexed MTO contract manufacturers, such as those in the fashion industry. This study therefore develops an efficient and effective CRP approach that considers orders with variable time windows. Real-time capacity feasibility can be checked on both the corporate planning and detailed operational scheduling levels by applying the preemptive earliest due date (PEDD) rule to a single machine problem. This simple and efficient dispatching rule can assess the impact on capacity consumption each time an inquiry order is received or select a set of pre-prioritized orders that can be feasibly scheduled. The efficiency of a supply chain network is affected by its overall lead time, which includes time spent on order processing, manufacturing, and transportation. The proposed approach significantly reduces the order processing time and enhances supply chain efficiency.     

Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes

Polarizers are one of the key parts of Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD), and their production requires high material costs. How to reduce manufacturing costs is thus a key task in this highly competitive global market. The precise yield forecast model considering learning effects that is proposed in this work is believed to be an effective approach to reduce both the raw material input-cost and inventory cost of overproduction. Support vector regression (SVR) model is one of the commonly used approaches to forecast the yield trend. However, in the early manufacturing stages for a new product, an SVR model is usually sensitive and unstable because of the use of insufficient data. Faced with this problem, this research aims at enhancing the SVR model by using past manufacturing experience and virtual samples to estimate the yield trend model for pilot products. This paper proposes a novel Quadratic-Curve Diffusion (QCD) method, wherein we derive a quadratic yield function (QYF) of the new manufacturing process for each key manufacturing variable by utilizing past manufacturing experience; and then use the QYF to generate virtual samples to assist building the overall yield forecast model of the new manufacturing process. The results show that the proposed method is superior to the performance of other forecast and virtual sample generation models.