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Stochastic models of manufacturing systems Thursday, June 24, 2010
Dieter Armbruster Some issues for aggregate models of production systems We have shown in recent years that describing production systems via a continuum flow leads to models on large scales that can be simulated fast. In this talk we will discuss three issues or extensions of these models: Control at the aggregate level, aggregate models for strongly time-varying inputs and aggregate production lines with finite buffers. Remco Bierbooms Performance Analysis of Production Lines with Fluid Flows and Finite Buffers In this talk we consider production lines consisting of a number of
machines or machines in series and a finite buffer between each of the
machines. The flow of products behaves like a fluid. Each machine suffers
from breakdowns, which are for example caused by failures, changeovers,
cleaning etcetera. We are typically interested in the throughput of such
production lines. Our research is inspired by a production line at Heineken
Den Bosch, where retour bottles are being filled and processed by 11
machines in series having different machine speeds. A conveyor belt between
each pair of machines is being used for transportation and for buffering in
case of machine breakdowns. The number of bottles handled per hour by the
machines is very large, which makes it natural to treat the material flows
as fluid rather than as discrete items. In the first part of the talk, we
explain how to build a simulation model for the Heineken production line
using factory floor data. Çagdas Büyükkaramikli Delivery Lead Time and Flexible Capacity Setting for Repair Shops with Homogenous Customers In this paper, we study a system consisting of a repair shop and its homogenous customers. The customers operate machines that are prone to failure. Upon a failure of a machine, a substitute machine is hired at the market at a certain cost and the failed machine is sent to the shop for repair. We assume that the substitute machine is immediately available and is hired for a specific amount of time, which is decided by the customer. If the time until repair exceeds the hiring duration, the customer looses revenue due to the machine unavailability. The shop’s capacity affects the time until repair and the unit repair costs. Under centralized decision making, system wide optimal shop capacity and the hiring duration can be found. However, in practice, these decisions are taken separately and most of the time the shop capacity decision is unbeknownst to the customers. Therefore, first we design the coordination mechanism under fixed shop capacity and find the iterative decision making process that achieves the system wide minimum cost per repair cycle. Then, in the second part we extend the system by allowing the repair shop to adapt its capacity and to admit failed machines only at equidistant points in time. Time between consecutive points is called a period and it delays the delivery of the failed machine to the shop and creates burstiness in the arrival stream to the shop. We model the unit capacity cost and failed machine transportation costs as well as the delay in machine delivery as a function of period length and with the coordination mechanism developed in the first part; investigate the improvements in total costs in this periodic admission system. The more flexible use of shop’s capacity and the efficiency in the transportation of the failed machines make the periodic admission policy an interesting alternative. John Buzacott, York University, Canada What’s the Use of Stochastic Models of Manufacturing Systems? This talk addresses the question of why stochastic models are useful and relevant in designing and operating manufacturing systems. Investing in advanced manufacturing systems requires a major commitment by the firm. While simulation is a well established tool for design, stochastic models provide valuable guidance in the rapid comparison of key features of different alternatives. They can help designers avoid major mistakes because they expand the designer’s intuitive understanding of what determines system performance. The talk will outline the range of manufacturing systems and issues for which stochastic models exist and illustrate their use for understanding these issues. The talk will also show how stochastic models can provide insight on important issues relating to the overall manufacturing strategy of the firm. Stéphane Dauzère-Pérès, Ecole des Mines de Saint-Etienne, France Modeling and optimizing operations in semiconductor manufacturing This talk aims at covering various research work conducted at different decision levels with semiconductor manufacturers in the last six years. A first global overview of the main characteristics of semiconductor manufacturing will be presented, together with the three problem types discussed in the sequel. The first problem is related to capacity planning, and in particular qualification management. We will show how we model flexibility, and its impact on manufacturing performances. The second problem is related to detailed scheduling. The presentation will focus on a specific workshop, and summarizes how scheduling decisions were modeled and optimized. The last problem is related to the detailed simulation model of a semiconductor manufacturing facility, including automated transportation, production and storage. The features of the model will be presented, together with the current work on optimizing key parameters. The talk will end with more recent research avenues, and provide some perspectives on the related challenges. Nico van Dijk, University of Amsterdam, The Netherlands Joint work with Nikky Kortbeek, University of Twente Lessons from an OT-ICU system for manufacturing Motivated by the interaction between an Operating Theatre (OT) and an
Intensive Care Unit Nicky van Foreest, University of Groningen, The Netherlands Order Acceptance Policies for Produce-To-Order Environments with Family-Dependent Due-Dates In this talk we consider a produce-to-order production environment in which a single bottleneck machine produces one product family at a time and is subject to significant switch-over times when the product family changes. Orders are to be delivered on-date to customers and the lead times of orders are family dependent. An acceptance and scheduling policy determines whether an arriving order is to be accepted into a production schedule, or rejected upon arrival. The aim of the production system is to find a policy that maximizes the long run expected reward per arriving order. We use a new Markov Decision Process (MDP) based approach to find a threshold type of policy that is near optimal and easy to implement. First, by means of an MDP approach we find an optimal policy which is shown to be difficult to implement. Second, a new approach is used to analyze the structure of the optimal policy and to find a threshold type of policy that is easy to implement and nearly optimal under a wide range of parameter settings, including product family asymmetries in arrival rate, job size, job reward, and due-date. John Fowler, Arizona State University , USA Healthcare Systems Engineering Modern industrial engineering, systems engineering, operations management, and operations research methods hold significant promise for health care systems and quality of care research. Among the most promising methods are queuing theory, optimization and process simulation. This presentation will utilize cases to demonstrate application of engineering and operations management principles and tools to improve health care systems. Michiel Jansen, TU/e IE&IS The effect of workload constraints in periodic order release models for manufacturing systems We study the relation between workload constraints and planned lead times in periodic order release models for manufacturing systems. We consider manufacturing systems where periodic throughput is limited and subject to uncertainties. Workload constraints are necessary to ensure that the manufacturing system can meet the planned lead time. Due to the periodic interaction with the manufacturing system, workload constraints may also lead to additional idling of resources which reduces the effective capacity of the manufacturing system. We analyze a stylized model of a manufacturing system with a single server and two queues in series: an admission queue and a work-in-progress (WIP) queue. Periodically, jobs from the admission queue are released to the WIP queue such that the number of jobs in WIP and in service does not exceed the workload constraint. We present a simple formula for the maximum utilization rate of such a system (one that permits stable queue-lengths) and show that it is generally less than one. We characterize the queue-length distribution by its generating function and give its first two moments. We also propose a numerical procedure for obtaining the steady state probabilities of the underlying DTMC. We compare these results to a model of a manufacturing system that does not include a workload constraint and observe that a workload constraint particularly leads to a significant increase in expected queue-length if it is close to or less than the expected potential throughput in a period (which is the setting that is oftentimes chosen in mathematical programming models for production planning). Furthermore, using these results, we illustrate the relation between workload constraints and planned lead times for various cost functions involving inventory and tardiness costs and conclude that a planned lead time of two periods is often the best choice.We study the relation between workload constraints and planned lead times in periodic order release models for manufacturing systems. We consider manufacturing systems where periodic throughput is limited and subject to uncertainties. Workload constraints are necessary to ensure that the manufacturing system can meet the planned lead time. Due to the periodic interaction with the manufacturing system, workload constraints may also lead to additional idling of resources which reduces the effective capacity of the manufacturing system. We analyze a stylized model of a manufacturing system with a single server and two queues in series: an admission queue and a work-in-progress (WIP) queue. Periodically, jobs from the admission queue are released to the WIP queue such that the number of jobs in WIP and in service does not exceed the workload constraint. We present a simple formula for the maximum utilization rate of such a system (one that permits stable queue-lengths) and show that it is generally less than one. We characterize the queue-length distribution by its generating function and give its first two moments. We also propose a numerical procedure for obtaining the steady state probabilities of the underlying DTMC. We compare these results to a model of a manufacturing system that does not include a workload constraint and observe that a workload constraint particularly leads to a significant increase in expected queue-length if it is close to or less than the expected potential throughput in a period (which is the setting that is oftentimes chosen in mathematical programming models for production planning). Furthermore, using these results, we illustrate the relation between workload constraints and planned lead times for various cost functions involving inventory and tardiness costs and conclude that a planned lead time of two periods is often the best choice. Ger Koole, VU University Amsterdam, The Netherlands Process optimization in hospitals In this talk we give an overview of health care logistics, with a focus on the differences with manufacturing. We present a number of cases, concerning nursing wards, shared resources such as scanners, and sojourn time reduction in an emergency department. Erwin van der Laan, Erasmus University, The Netherlands Managing uncertainty in hybrid manufacturing/remanufacturing inventory systems Product recovery has gained considerable interest as of late as it can
contribute to both economical and ecological performance. It is well-known
that product recovery comes with considerable uncertainties with respect to
quantity, timing and quality of recoverable products. Hubert Missbauer, The University of Innsbruck, Austria Order release planning based on stochastic models of manufacturing systems
Planning and control systems for manufacturing systems
and supply chains are often structured hierarchically. The top (upper) level
performs medium-term planning of the material flow that coordinates the
production units (e.g., component manufacturing, assembly), and the base
(lower) level performs detailed scheduling of the work orders within the
production units. The essential interface between these levels is order
release. Therefore production planning models that support the coordination
task of the top level usually are order release planning models. Yoni Nazarathy, EURANDOM, The Netherlands The Variance of Production Counts over a Long Time Horizon Consider a production process. Once the throughput is known, knowledge of
the variability can allow managers to allocate storage and transportation
resources more effectively. This has led some researchers over the past
years to develop computational models for assessing the variability of
complex production systems. Since the variance of the number of items
produced during the time interval [0,t] typically grows linearly for large
t, a natural quantity of interest is the asymptotic variance rate - this is
the asymptotic slope of the variance of the number of items produced during
[0,t]. Oliver Rose, Technische Universität Dresden, Germany An approach to model production systems with SysML In this talk, we present an approach for developing
a simulation-tool-independent description of production systems and how to
convert such a general model into simulation-tool-specific models. Our aim
is to develop production models by means of SysML (Systems Modeling Language)
and to build converters from SysML models to a large variety of simulation
tools. J. George Shanthikumar, Purdue University West Lafayette Queueing Systems Modeling with Operational Statistics Reha Uzsoy, North Carolina State University Production planning with resources subject to congestion While the use of optimization models for production planning dates back to at least the 1950s, the ability to represent the nonlinear relationships between work releases and critical performance measures such as lead times has remained a longstanding problem. We review alternative approaches that have been used in the literature, and then suggest a new approach using nonlinear clearing functions that represent the relationship between the expected throughput of a production resource in a planning period, and the expected work in process inventory level at the resource over that period. Extensive computational experiments show that the proposed approach produces production plans which are more realistic in terms of the production system's ability to execute them. We compare the performance of clearing function models to those from iterative LP-simulation methods, and discuss a number of extensions including incorporation of stochastic demand. Casper Veeger, Tu/e, The Netherlands Aggregate modeling in semiconductor manufacturing using effective process times In semiconductor manufacturing, model-based performance analysis is becoming increasingly important due to growing competition and high capital investment costs. A detailed simulation model of a manufacturing system may be helpful in performance improvement activities, but requires considerable development and maintenance effort. Alternatively, an analytical model can be used. Analytical models are fast to evaluate though incorporation of all relevant factoryfloor aspects is difficult. To reduce the number of factory-floor aspects that have to be modeled explicitly, we investigate the use of an aggregate model to represent the manufacturing system. The aggregate model is a simple analytical or discrete-event simulation model with only a few parameters, such as the mean and the coefficient of variation of an aggregated process time distribution. The aggregate process time lumps together all the relevant aspects of the considered system. We refer to this aggregate process time as the Effective Process Time (EPT). In the presentation, the concept of EPT-based aggregate modeling will be explained first. Next, we present an EPT-based aggregate model that we have recently developed. The model is a single-server representation of a workstation. The EPT distribution of the single server is estimated from arrival and departure events measured at the workstation being modeled. In addition, the order in which lots travel through the system is incorporated in the aggregate model. Finally, it is discussed whether the single-server aggregate model can be used to model entire networks. Fabian Wirth, University of Würzburg, Germany Structure preserving model reduction for logistic systems In modern logistics applications large scale
networks are increasingly important, for instance due to the emergence of
transnational supply chains. Due to their size analysis or simulation of
such networks becomes expensive to such an extent that is is desirable to
derive approximate models with lower complexity, that retain important
structural properties of the complex network. Last updated
12-11-2010, |
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