Approximations for fork/join systems with inputs from multi-server stations.

Fork/join stations are commonly used to model synchronization constraints in queuing network models of computer and manufacturing systems. This paper presents an exact analysis of a fork/join station in a closed queuing network with inputs from multi-server stations with two-phase Coxian service distributions. The underlying queue length process is analyzed exactly to determine performance measures such as through put, and distributions of the queue length at the fork/join station. By choosing suitable parameters for the two-phase Coxian distributions, the effect of variability in inputs on system performance is studied. The study reveals that for several system configurations, analysis of the simpler system with exponential inputs provides efficient approximations for performance measures. Both, the exact analysis and the simple approximations of fork/join systems constitute useful building blocks for developing efficient methods for analyzing large queuing networks with fork/join stations.queueing; fork/join; synchronization; assembly systems; closed queuing networks;

Determining Kanban Size Using Mathematical Programming and Discrete Event Simulation for a Manufacturing System with Large Production Variability

In order to become more competitive and aggressive in the market place it is imperative for manufacturers to reduce cycle time, limit work-in-process, and improve productivity, responsiveness, capacities, and quality. One manner in which supply chains can be improved is via the use of kanbans in a pull production system. Kanbans refer to a card or signal for productions scheduling within just-in-time (JIT) production systems to signal where and what to produce, when to produce it, and how much. A Kanban based JIT production system has been shown to be beneficial to supply chains for they reduce work-in-process, provide real time status of the system, and enhance communication both up and down stream. While many studies exist in regards to determining optimal number of kanbans, types of kanban systems, and other factors related to kanban system performance, no comprehensive model has been developed to determine kanban size in a manufacturing system with variable workforce production rate and variable demand pattern. This study used Stewart-Marchman-Act, a Daytona Beach rehabilitation center for those with mental disabilities or recovering from addiction that has several manufacturing processes, as a test bed sing mathematical programming and discrete event simulation models to determine 2 the Kanban size empirically. Results from the validated simulation model indicated that there would be a significant reduction in cycle time with a kanban system; on average, there would be a decrease in cycle time of nine days (almost two weeks). Results were discussed and limitations of the study were presented in the end

A multi-echelon supply chain model for strategic inventory assessment through the deployment of kanbans

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 100-102).As global competition in the manufacturing space grows, so do corporations' needs for sophisticated and optimized management systems to enable continuous flows of information and materials across the many tiers within their supply chains. With the complexities introduced by the variability in the demand for finished goods as well as by the variability in lead-time of transportation, procurement, production and administrative activities, corporations have turned to quantitative modeling of their supply chains to address these issues. Based on the data of a heavy machinery manufacturer headquartered in the US, this research introduces a robust model for the deployment of strategic inventory buffers across a multi-echelon manufacturing system. Specifically, this study establishes a replenishment policy for inventory using a multiple bin, or Kanban, system for each part number in the assembly of products from our sponsors tractor line. We employ a numerical simulation to evaluate and optimize the various inventory deployment scenarios. Utilizing several thousand runs of the simulation, we derive a generalized treatment for each part number based on an econometric function of the parameters associated with lead-time, order frequency, inventory value and order costing. The pilot for the simulation focuses on the parts data for three earthmoving products across eight echelons, but scales to n products across m echelons. Our results show that this approach predicted the optimal quantities of Kanbans for 95% of parts to a level of accuracy +/- 3 bins.by Philip J. Hodge and Joshua D. Lemaitre.M.Eng.in Logistic

Production and inventory control in complex production systems using approximate dynamic programming.

Production systems focus not only on providing enough product to supply the market, but also on delivering the right product at the right price, while lowering the cost during the production process. The dynamics and uncertainties of modern production systems and the requirements of fast response often make its design and operation very complex. Thus, analytical models, such as those involving the use of dynamic programming, may fail to generate an optimal control policy for modern production systems. Modern production systems are often in possession of the features that allow them to produce various types of product through multiple working stations interacting with each other. The production process is usually divided into several stages, thus a number of intermediate components (WIP) are made to stock and wait to be handled by the next production stage. In particular, development of an efficient production and inventory control policy for such production systems is difficult, since the uncertain demand, system dynamics and large changeover times at the work stations cause significant problems. Also, due to the large state and action space, the controlling problems of modern production systems often suffer from the curse of dimensionality

Modelling and analysis of pull production systems

Ankara : Industrial Engineering and the Institute of Engineering and Science of Bilkent Univ., 1995.Thesis (Ph.D.) -- Bilkent University, 1995.Includes bibliographical references.A variety of production systems appearing in the literature are reviewed in order to develop a classification scheme for production systems. A number of pull production systems appearing in the classification are found to be equivalent to a tandem queue so that accurate tandem queue decomposition methods can be used to find the performance of such systems. The primary concern of this dissertation is to model and analyze non-tandem queue equivalent periodic pull production systems. In this research, an exact performance evaluation model is developed for a singleitem periodic pull production system. The processing and demand interarrival times are assumed to be Markovian. For large systems, which are difficult to evaluate exactly because of large state spaces involved, an approximate decomposition method is proposed. A typical approximate decomposition procedure takes individual stages or pairs of stages in isolation to analyze the system and then it aggregates the results to obtain an approximate performance for the whole system. An experiment is designed in order to investigate the general behavior of the decomposition. The results are worth attention. A second aspect of this study is to investigate an allocation methodology to achieve the maximum throughput rate with providing two sets of allocation parameters regarding the number of kanbans and the workload at each stage of the system. Together with some structural properties, the experimental results provide some insight into the behavior of pull production systems and also provide a basis for the proposed allocation methodology. Finally, we conclude our findings together with some directions for future research.Kırkavak, NureddinPh.D

Gestion conjointe de production et qualité appliquée aux lignes de production non fiables

RÉSUMÉ Cette recherche s'intéresse aux lignes de production non fiables formées de plusieurs machines satisfaisant une demande fixe de produits finis de type unique et comprenant des stocks d'encours à capacité fixe. Deux types de machines sont considérés ici: un type de machine dont une partie de la production est non conforme aux normes de la qualité et un autre type de machine dont la production est 100 % conforme. La thèse est organisée selon trois contributions principales. L'objectif visé dans la première partie est de développer des modèles d'analyse de performance et des techniques d'optimisation efficaces pour le réglage des paramètres de conception suivant une approche de contrôle de type CONWIP (Constant Work-In-Process). Notre recherche s'inscrit dans le courant des approches de décomposition des ateliers de fabrication. L'analyse de la performance de ces systèmes aléatoires discrets/continus repose essentiellement sur les équations de Kolmogorov et le principe de la demande moyenne. De plus, nous introduisons des blocs de construction formés de paires de stock local-machine globale. La machine globale commune à toutes ces paires permet alors d'introduire une mesure de corrélation importante entre tous les blocs de construction quelle que soit la distance des stocks qui entrent dans leur composition. Ceci permet de créer des liens entre blocs de construction de la décomposition qui se situent au-delà de leurs voisinages respectifs, comme c'est le cas dans d'autres méthodes de décomposition. Cet aspect de corrélation des machines est caractéristique de la stratégie de production CONWIP. De plus, dans notre modélisation globale, la dynamique totale du stock dans la boucle CONWIP est considérée comme étant essentiellement affectée par les statistiques de fiabilité de la machine M1, et la probabilité de disponibilité des pièces dans le stock (n-1), reflétant ainsi l'opinion que le CONWIP est une forme de Kanban imposée à une collection de machines.----------ABSTRACT This research is concerned with unreliable production lines. Two types of machines are considered here: a machine for which part of the production is part substandard in quality and a machine whose production is 100% in conformity. The thesis is organized according to three principal contributions. In the first part of our research and for a given choice of the maximum allowable total storage parameter, the performance of constant work-in-process (CONWIP) disciplines in unreliable transfer lines subjected to a constant rate of demand for parts is characterized via a tractable approximate mathematical model. For a (n-1) machines CONWIP loop, the model consists of n multi-state machine single buffer building blocks, separately solvable once a total of (n-1)2 unknown constants shared by the building blocks are initialized. The multi-state machine is common to all building blocks, and its n discrete states approximate the joint operating state of the machines within the CONWIP loop; each of the first (n-1) blocks maps into a single internal buffer dynamics, while the nth building block characterizes total work-in-process (wip) dynamics. The blocks correspond to linear n component state equations with boundary conditions. The unknown (shared) constants in the block dynamics are initialized and calculated by means of successive iterations. The performance estimates of interest, mean total wip, and probability of parts availability at the end buffer in the loop are obtained from the model and validated against the results of Monte-Carlo simulations. In the second part of our research, we address the optimal production control problems for an unreliable manufacturing system that produces items that can be regarded as conforming or non conforming. A new stochastic hybrid state Markovian model with three discrete states, also called modes is introduced. The first two, operational sound and operational defective are not directly observable, while the third mode, failure, is observable. Production of defective parts is respectively initiated and stopped at the random entrance times to and departure times from the defective operational mode. The intricate piecewise-deterministic dynamics of the model are studied, and the associated Kolmogorov equations are developed under the suboptimal class of hedging policies

A Stochastic model of multi-stage pull production systems

The pull multi-stage production scheduling and inventory control system is a way of implementing the just-in-time doctrine. In the pull system the production schedule of the final stage is transmitted back to all the upstream stages. This is achieved by keeping a certain amount of parts at each stage, with succeeding stages withdrawing parts from preceding ones only to the extent that they are needed;A stochastic model for the pull multi-stage production system is developed for two situations. The first situation is that a single machine (server) with non-zero inventory. The second situation is that of several machines with zero inventory;The model is studied and analyzed in the light of several measures of system performance and effectiveness. Allocation of parts among stages, and the effect of processing rates are investigated;A stochastic model for the push multi-stage production system is also developed and compared to the pull model. A new method, which reduced the number of Markovian states, is developed to model the blocking phenomenon in push system. A duality phenomenon between the pull and a specially defined push model is presented and discussed;The confluent system, with made parts processed in house into subassemblies, and then into final assemblies, is also modeled under the pull system. WIP allocation is studied as for the series system, and the possibility is explored of analyzing a process through its sub-process ;Although processing and demand are assumed exponential, the limitation imposed by stage capacity will cause the output process not to be poisson. For this reason, closed form solution for equilibrium probabilities of the system are not available and approximation and numerical methods were investigated, and compared;The proposed model can be applied to flexible manufacturing systems, assembly lines, and flow shops

A methodology for the design of quality assurance functional model and information system

In spite of all advances in computer, technologies, information processing, automation technologies, manufacturing processes, and the push for integration across all functional areas toward a totally integrated and automated manufacturing system, the suggestion is that quality assurance which covers all quality-based functions in the product-life cycle is often overlooked. In spite of the important role of quality information systems in achieving high quality processes little published research in this area is found in the literature. Study of the available relevant literature and the collection of data from manufacturing industries confirm that different manufacturing situations require different quality assurance systems, and this is evident from the proliferation of differing QA systems found in industry. There are however some common features both universal/or within different classes of industries. Accordingly an 'ISO-9000 based generic structural model incorporating these common quality based functions and their associated information requirements has been developed. This research further investigates and verifies those factors which may affect the design of a QAIS as a guide for designing Quality Assurance Information Systems for manufacturing business organisations. Realising that knowledge-based systems can provide a support environment for designing QAIS, this research also considers and, develops a KB Decision System for Designing Quality Information Systems (DSDQAIS). The DSDQAIS recommends the structure of a QAIS, in the form of an IDEFO model, appropriate to specific company profiles input by the user. Since the available software' applications and development tools which support the sub-systems run on a personal computer, the prototype of this system has also been developed and tested on PC. Recommendations for the further development of the system are given

Integrated Analytical Performance Evaluation Models of Warehouses

Warehouse design process is a complex process with numerous alternatives at all design stages, with focus on throughput capacity, inventory size and material handling equipment requirements. Enumerating all feasible solutions that satisfy the throughput and storage capacity requirements is not practical. Analytical models play a key role in the preliminary design stages in identifying several good initial warehouse configurations. This research effort pertains to the development of integrated analytical models that address capacity/congestion and inventory issues simultaneously in warehouse systems.The first part of the dissertation focuses on the development of a queueing network model of the "shared-server system," which is an inventory store with a server performing both storage and retrieval operations. First, we modeled the shared-server system using Continuous Time Markov Chains (CTMC) under exponential assumptions. We then developed an approximate queueing network model for general arrivals and general service time distribution, and designed a solution procedure based on the parametric-decomposition method. Later, we extended these models to include multi-server cases.The second part of the dissertation focuses on the development of a queueing-inventory (QI) model of an order-picking system. The configuration of the unit-load that is stored (pallets) is different from that which is retrieved (cases). We developed a single stage QI model with batch processing to represent the material movement in and out of the forward inventory store. We then extended these models to include multi-server cases.The last part of the dissertation focuses on the development of an integrated model that demonstrates the applicability of these key building blocks (the shared-server system and the order-picking system) in developing an end-to-end model of the warehouse system. Extensive numerical experiments indicate that the proposed analytical models can be solved in a computationally efficient manner and are accurate for a wide range of parameter values when compared with simulation estimates.Industrial Engineering & Managemen