Kan Wu

An examination of variability and its basic properties for a factory

Variability is a key performance index of a factory. In order to characterize variability of a factory, definitions of bottleneck, utilization, and variability of a single machine are reexamined and clarified. The clarification leads to the introduction of a detail expression for the relationship between cycle time and work-in-progress. In order to quantify variability for factories, the author uses a single machine system to gauge the behaviors, and subsequently derives an explicit expression for the variability, of a simple factory, making use of analogy and the clarified definitions. The obtained results can be applied to many subjects in the field of manufacturing management, such as factory performance analysis, capacity planning, and cycle time reduction. With the derived results, properties of variability for a simple factory in the aspects of utilization versus throughput bottlenecks and nonthroughput bottlenecks, gap effects, and bounds on variability, are examined in detail to shed light on the insights of the stochastic behaviors of a complex factory.

Classification of queueing models for a workstation with interruptions: a review

Queueing theory is commonly used to evaluate the performance of production systems. Due to the complexity of practical production lines, choosing correct queueing models under the existence of interruptions can be intricate, but is critical to the evaluation of system performance. This paper gives a review of research in this area, proposes a systematic way to classify different types of interruptions in manufacturing systems and suggests proper queueing models for each category. The operational definition of service time is given and the decomposition properties for the run-based state-induced and time-based preemptive events are explained. The corresponding G/G/1 approximate models are proposed.

Queueing models for a single machine subject to multiple types of interruptions

Queueing models are commonly applied to quantify the performance of production systems. Prior research has usually focused on deriving queueing models for a specific type of interruptions. However, machines generally suffer multiple types of interruptions in practical manufacturing systems. To satisfy this need, an integrated model is proposed, in which multiple types of interruptions commonly seen on the shop floor are considered.

Performance evaluation for general queueing networks in manufacturing systems: Characterizing the trade-off between queue time and utilization

Performance evaluation plays a key role in manufacturing system design and productivity improvement. Characterizing performance objectively is the first step. Inspired by the underlying structure of tandem queues, we have derived an approximate model to characterize the system performance. The model decomposes system queue time and variability into bottleneck and non-bottleneck parts while capturing the dependence among workstations. Compared the new model with prior approaches, the new model not only is more accurate but also requires less information. The property of manufacturing system performance is given based on the insight from the model.

The Determination and Indetermination of Service Times in Manufacturing Systems

The notion of service times is of such fundamental importance in the analysis of queues that it has long been taken for granted. Intuitively, it is used to represent the time interval that a server is capable of completing a dispatched job. However, actual measurements of service times under simple queues in production lines have encountered practical difficulties, in spite of its seemingly deterministic nature. Previous studies have introduced concepts of effective process times to quantify service times. Besides notions of theoretical processing times, raw process times and queueing times, among others, are commonly used in various applications. Their existence causes confusion in the determination of service times and clarification of such terminologies is needed. A simple model is examined to quantify the various concepts and establish their interrelationships. This paper brings out new properties of effective process times with a dynamic dependence on utilization. Discrete event simulations are conducted to verify these properties and explain the phenomenon of indetermination of service times. Both theoretical prediction and simulation results show that unless the system is fully loaded, service time and effective process time are not equivalent and it cannot be measured directly from observations of effective process times.

Taxonomy of batch queueing models in manufacturing systems

Batching plays an important role in performance evaluation of manufacturing systems. Three types of batching are commonly seen: transfer batches, parallel batches and serial batches. To model the batching behavior correctly, a comprehensive classification of batching is proposed. Eight types of batching behavior are classified and corresponding queueing models are given. The newly proposed models are validated by simulation.

Optimal inventory control in a multi-period newsvendor problem with non-stationary demand

The optimal control of inventory in supply chains plays a key role in the competiveness of a corporation. The inventory cost can account for half of companys logistics cost. The classical inventory models, e.g., newsvendor and EOQ models, assume either a single or infinite planning periods. However, these models may not be applied to perishable products which usually have a certain shelf life. To optimize the total logistic cost for perishable products, this paper presents a multi-period newsvendor model, and the problem is formulated as a multi-stage stochastic programming model with integer recourse decisions. We extend the progressive hedging method to solve the model efficiently. A numerical example and its sensitivity analysis are demonstrated.

Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels

A novel strategic model for designing the MDSCN is proposed.Multiple objectives of the MDSCN are designed for sustainable development.A novel MOABC algorithm is introduced with unique features.Both the model and the method are validated through experimentation. The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results.

Compatibility of Queueing Theory, Manufacturing Systems and SEMI Standards

Queueing theory is a powerful method to evaluate the performance of a manufacturing system. However, when applying it to a real system, both practical and theoretical issues arise. The practical issues are how to determine the parameter values for queuing models from the data available in real manufacturing systems. We explore this issue by comparing the data needed by queueing models to SEMI standards definitions. The theoretical issues are the selection of a specific queuing model and its proper use. In order to illustrate this, we design a specific situation for the applications of an M/M/1/Unreliable Machine queue, and compare the performances of two other approaches with it. The results show that, depending on the approach chosen, the differences in approximation error for cycle time can be over 30% at lower utilization levels.

Approximating the Performance of a Batch Service Queue Using the

Batching plays an important role in semiconductor fabs, and it can lead to inefficiency if not treated with care. The performance of parallel batch processes is often approximated by the G/G/1-based approximate models. By carefully examining the existing models, the dependence between queueing time and wait-to-batch time has been identified. A new improved model for parallel batch systems is proposed to exploit this dependence. The computation of the new model is still simple and fast, but it gives better approximation by reducing the systematic error in earlier models which ignored the dependence between queueing time and wait-to-batch time.