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.     

Towards product customization: An integrated order fulfillment system

To stay competitive, manufacturing companies nowadays pursue product customization; and configure-to-order (CTO) has been recognized as an ideal business model to implement product customization. Due to the complexities resulting from the large number of customized products, an increased attention has been paid to the order fulfillment process in CTO. However, most of the solutions delivered either focus on own interested areas without considering the impacts from/on the others or approach order fulfillment processes from a strategic level. In view of the limitations of existing solutions and the significance of order fulfillment activity automation and integration, this study considers the entire spectrum of order fulfillment process at an operational level and, proposes an integrated order fulfillment system (IOFS). The IOFS is designed to (1) automatically execute order processing, which is conventionally undertaken in a face-to-face manner; (2) configure products and process plans based on product and process family models; and (3) allow real-time data extraction and sharing of the latest information. Therefore, it assists companies to quickly respond to diverse customer requirements and deliver the expected products at low costs. An industrial example of turn unit assemblies is presented to demonstrate the feasibility and potential of the IOFS for product customization.     

An advanced order acceptance model for hybrid production strategy

Competitors stand out through commitment to providing excellent customer service. Many organizations strive to promote their order promising system to generate more reliable quotes with quantity and due date constraints. This paper develops a Capable-to-Promise (CTP) based Order Acceptance Model (OAM) for a Hybrid Production Strategy (HPS) based on the positioning of the Customer Order Decoupling Points. The proposed model allocates the uncommitted availability and planned production receipts to current and anticipation of future needs. A Mixed-Integer Linear Programming (MILP) is developed to determine the optimal order quantities based on the resource availability. The proposed model efficiently mitigates the risk of not being reliable in the commitments due to discrepancies between the real and unused quantities. The CTP based OAM is compatible with a HPS with both make-to-order (MTO) and make-to-stock (MTS). The presented model encompasses four steps. First, demands for MTO products are collected in batches on daily basis and a forecasting model is applied to predict orders for MTS products. Secondly, the quantity-based Revenue Management approach is used to prioritize orders. Afterwards, the optimization model assesses the availability of resources in order to produce collected orders. The final step is to accept valuable orders based on the resource availability. In order to illustrate the applications of the modeling approach, two case studies are provided.     

Data-driven order correlation pattern and storage location assignment in robotic mobile fulfillment and process automation system

With the rapid development and implementation of ICT, academics and industrial practitioners are widely applying robotic process automation (RPA) to enhance their business processes and operational efficiencies. This paper intends to address the value creation of utilizing RPA under the cloud-based Cyber-Physical Systems (CPS) in Robotic Mobile Fulfillment System (RMFS). By providing a TO-BE analysis of RPA and cloud-based CPS framework, a data-driven approach is proposed for zone clustering and storage location assignment classification in RMFS. The purpose of the paper is to gain better operational efficiency in RMFS. A modified A* algorithm is adopted for calculating the total traveling cost of each moveable rack in the case company layout. Nine common clustering algorithms are applied for the RMFS’s zone clustering. The results from the zone clustering are considered as nine scenarios for data-driven order classification to solve the storage location assignment problem. Six common classification algorithms are applied for a detailed comparison which has been conducted with thousands of orders. The results reveal that K-means, Gaussian Mixture Models, and Bayesian Gaussian Mixture Model are worked well with six supervised classification algorithms which yield an average of 95% accuracy rate and a higher customers’ expectation can be achieved under the customer-driven e-commerce economy.     

The value of postponing online fulfillment decisions in multi-channel retail/e-tail organizations

Many retail/e-tail organizations assign responsibilities for online sales immediately and to the closest fulfillment location that has available stock. Unfortunately there is little research on the value of using such policies in retail/e-tail companies. To fill this gap, this paper examines two aspects of the online fulfillment assignment decision that differ from current practice. We propose that online sales should be accumulated before they are assigned to a fulfillment site and that more inventory position information should be leveraged into the fulfillment decision. Specifically, we develop and evaluate a “quasi-dynamic” allocation policy that assigns accumulated online sales to fulfillment locations based on expected inventory, shipping, and customer wait costs. Computational results show that our policy can reduce costs (i.e., holding, backorder, transportation cost) at the fulfillment locations by as much as 23% on average over a commonly used transportation cost policy. In addition, postponing the allocation decision and allowing sales to accumulate can reduce inventory costs at the fulfillment sites by 14% over common practice of instantaneously assigning online sales responsibilities. The magnitude of the benefit depends critically on the number of allocations made each period and the fraction of total sales coming from the online channel. Although postponement delays receipt of online sales, our findings suggest that explicitly incorporating customer service in the allocation decision can improve product availability at little or no additional cost.     

Lot release times and dispatching rule for a TFT-LCD cell process

In this paper, the simulation analysis of the cell process in a thin film transistor-liquid crystal display (TFT-LCD) manufacturing was analyzed, and the effects of the lot release times and dispatching rule were considered. Discrete-event simulation models were developed in an e-M Plant to study this system. The lot release times and dispatching rule based on minimum setup times was used in this system. In order to improve the system performance, a heuristic algorithm for lot release times and a queue time maximum un-matches (QTMU) dispatching rule for rubbing machines are proposed. The simulation results revealed a substantial improvement of the cell process performance and reduced the setup times for the rubbing machine and the cycle time of TFT-array substrate and color filter (CF) substrate processing.     

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.     

基于数据的间歇过程时变神经模糊模型研究

间歇过程的优化控制往往依赖于过程精确的数学模型,快速反应的市场要求使得数据驱动的建模方法被应用到了间歇过程的建模中.但常规的数据驱动建模方法在模型结构中没有考虑间歇过程具有重复性的特性,只是简单地将间歇过程作为一般的非线性结构进行处理.针对该问题,本文提出一种新颖的间歇过程时变神经模糊模型,将时间轴和批次轴的信息统一在...     

Iterative learning model predictive control for multi-phase batch processes

Multi-phase batch process is common in industry, such as injection molding process, fermentation and sequencing batch reactor; however, it is still an open problem to control and analyze this kind of processes. Motivated by injection molding processes, the multi-phase batch process in each cycle is formulated as a switched system with internally forced switching instant. Controlling multi-phase batch processes can be decomposed into two subtasks: detecting the dynamics-switching-time; designing the control law for each phase with considering switching effect. In this paper, it is assumed that the dynamics-switching-time can be obtained in real-time and only the second subtask is studied. To exploit the repetitive nature of batch processes, iterative learning control scheme is used in batch direction. To deal with constraints, updating law is designed by using model predictive control scheme. An online iterative learning model predictive control (ILMPC) law is first proposed with a quadratic programming problem to be solved online. To reduce computation burden, an offline ILMPC is also proposed and compared. Applications on injection molding processes show that the proposed algorithms can control multi-phase batch processes well.     

A performance evaluation model for order picking warehouse design

This paper is concerned with a performance evaluation model for the order picking facility for warehouse design in a supply center (SC) by reducing the travel distance of transporters. This study includes important detail aspects of warehouse design and operational parameters such as warehouse size, rack size, number of transporters, and the system performance. In this study, we develop both mathematical and simulation modes considering probabilistic demand and picking frequency, and using simulation S/W, AutoMod. The results are compared and validated via simulation methods using AutoMod simulator. Finally, we developed a systematic and practical computer program and it was known that the proposed method is potentially efficient and useful in performance analysis for order picking warehouse problems.     

间歇化工过程的能量综合优化策略

综述了三类间歇过程能量综合优化方法,分析了间歇过程的生产和用能特点.从间歇过程能量利用环节和能量回收环节相互制约的关系出发,提出了适用于间歇过程能量综合优化改进的一般方法,为精细化工过程的节能降耗提供理论指导.方法用于指导某聚酯生产过程的能量综合优化,取得了节能10%以上的效果,表明所提出的能量综合优化方法能有效指导间歇过程的全局用能优化改进.     

A best-matching protocol for order fulfillment in re-configurable supply networks

Order fulfillment is a process which encompasses all the activities from the inquiry of goods by the customer to the final delivery of goods to the customer. The most important activity of the order fulfillment process is the selection of the order fulfilling agent in the supply network. The selection of the agent involves multiple criteria based on quantitative and qualitative metrics and requires several self-interested agents and organizations to dynamically form and configure supply chain. This article describes a methodology for selection of an order fulfillment agent in a collaborative, geographically distributed network by developing a Best Matching Protocol (BMP). The BMP developed, enables better matching of fulfillment agents with customers in a given supply network, by determining which agent best satisfies the pre-defined quality and cost requirements of the customer. The protocol enables collaboration between the agents of the Supply Network (SN) and provides a scalable solution for the increasing size of the SN.     

A neuro-fuzzy warning system for combating cybersickness in the elderly caused by the virtual environment on a TFT-LCD

Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE.     

Algorithms for on-line order batching in an order picking warehouse

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items required by (internal or external) customers. Order batching consists of combining these – indivisible – customer orders into picking orders. With respect to order batching, two problem types can be distinguished: in off-line (static) batching, all customer orders are known in advance; in on-line (dynamic) batching, customer orders become available dynamically over time. This paper considers an on-line order batching problem in which the maximum completion time of the customer orders arriving within a certain time period has to be minimized. The author shows how heuristic approaches for off-line order batching can be modified in order to deal with the on-line situation. In a competitive analysis, lower and upper bounds for the competitive ratios of the proposed algorithms are presented. The proposed algorithms are evaluated in a series of extensive numerical experiments. It is demonstrated that the choice of an appropriate batching method can lead to a substantial reduction of the maximum completion time.     

A mathematical model for order splitting in a multiple supplier single-item inventory system

In this paper we develop a mathematical model which considers multiple-supplier single-item inventory systems. The lead times of the suppliers and demand arrival rate are random variables. All shortages are backordered. Continuous review (s, Q) policy has been assumed. When the inventory level hits the reorder level, the total order is split among n suppliers. The problem is to determine the reorder level and order quantity for each supplier so that the expected total cost per time unit, including ordering cost, procurement cost, inventory holding cost and shortage cost is minimized. We also conduct extensive numerical experiments to show the advantages of our model compared to the relevant models in the literature. In addition, some managerial insights are observed.     

Tuning algorithms for fractional order internal model controllers for time delay processes

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.     

A survey on multistage/multiphase statistical modeling methods for batch processes

In industrial manufacturing, most batch processes are inherently multistage/multiphase in nature. To ensure both quality consistency of the manufactured products and safe operation of this kind of batch process, different multivariate statistical process control (MSPC) methods have been proposed in recent years. This paper gives an overview of multistage/multiphase statistical process control methods used for process analysis, monitoring, quality prediction and online quality improvement. Different types of phase divisions and modeling strategies are introduced and the method properties are discussed. For comparisons, a selection guide to these methods for different application purposes is provided. Finally, some promising research directions are suggested based on existing works.     

Industrial internet of things-driven storage location assignment and order picking in a resource synchronization and sharing-based robotic mobile fulfillment system

In this paper, we intend to address the value creation of utilizing the Industrial Internet of Things (IIoT)-driven resource synchronization and sharing-based robotic mobile fulfillment system (RMFS) to enhance the overall operational effectiveness and efficiencies during information transfer and synchronization of resources. With the advent of IIoT, a graph theory-based heuristic under the multi-deep RMFS is used for computing the shortest path. A-star, Dijkstra, and genetic heuristic algorithms are applied for comparison. A simulation with a consideration of the different types of collisions is conducted for different algorithms. By providing a new three-tier IIoT architecture which includes the suppliers, RMFS, and the disposal center, a model is developed with different storage location assignment rules and strategies under the particular parties to minimize the operation costs. IIoT enables resource synchronization and information sharing, and the path will be generated under different order scenarios with different algorithms. The results show that different storage assignment rules and strategies may lead to 30% cost differences compared to the company’s current practice with random storage.     

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.