Parallelization of Goal-Driven, Production Systems on Hypercube Machines in a C Environment.

Production systems are widely used in artificial intelligence to capture the notion of expertise in modeling expert systems. Production systems are computationally intensive programs spending most of the execution time in their MATCH or recognise phase. Efforts have been made by the research in this dissertation to minimize the production system\u27s execution time by optimizing the MATCH phase. Goal oriented deterministic production systems are commonly used for robotics applications and formed the main class of production systems that were studied in this dissertation. The main motivation for the research was to provide a better MATCH algorithm and use the multiprocessing capabilities of existing parallel computer hardware. The dissertation realizes these goals by transforming a traditional production system\u27s scalar equivalence operations into C arithmetic hashing function to generate an indexing variable for the switch-case construct of the C language. Partitioning of the working memory into homogeneous blocks and distributing production memory over the multiprocessors enhanced the MIMD operation of the production system. A scheme is formulated and implemented to identify a few key condition elements that may be used as an indexing variable and reduce the number of condition elements used in the MATCH phase. The complete translation from OPS5 code to C and the implementation scheme is presented in this dissertation. Various issues regarding the distribution of the inference engine over the multiprocessor environment and other related synchronization topics for distributed systems are covered in the dissertation. A detailed description of the parallel computer\u27s simulator is also provided in the dissertation. The dissertation identifies other research topics and problems related to parallelization of production systems, the most significant being the ability to incorporate LEARNING in production systems by using one or all of the idle processors that are waiting for the active processor to complete it\u27s activities

Transient Performance Analysis of Serial Production Lines

TRANSIENT PERFORMANCE ANALYSIS OF SERIAL PRODUCTION LINES by Yang Sun The University of Wisconsin-Milwaukee, 2015 Under the Supervision of Professor Liang Zhang Production lines with unreliable machines and finite buffers are characterized by both steady-state performance and transient behavior. The steady-state performance has been analyzed extensively for over 50 years. Transient behavior, however, is rarely studied and remains less explored. In practice, a lot of the real production systems are running partially or entirely in transient periods. Therefore, transient analysis is of significant practical importance. Most of the past research on production systems focuses on discrete materials flow which utilities Markov chain analysis. This dissertation is devoted to investigate the effects of system parameters on performance measures for transient serial production line with other machine reliability models. The reliability models investigated in this dissertation are exponential and no-exponential (Weibull, Gamma, Log-normal). In a real production line system, machine reliability models are much more difficult to identify. Strictly speaking, it requires the identifications of the histograms of up- and downtime, which requires a very large number of measurements during a long period of time. The result may be that the machines\u27 real reliability model on the factory floor are, practically, never known. Therefore, it is of significant practical importance to investigate the general effects of system parameters on performance measures for transient serial production line with different reliability models. The system parameters include machine efficiency e, ratio of N and Tdown (K), machines\u27 average downtime Tdown, and coefficient of variation CV. The performance measures include settling time of production rate (t_sPR), settling time of work-in-process (t_sWIP), total production (TP), production loss (PL). The relationship between the performance measures and system parameters reveals the fundamental principles that characterize the behavior of such systems and can be used as a guideline for product lines\u27 management and improvement. Most previous research studies are limited to two or three machine system due to the technical complexity. Furthermore, presently there are no analytical tools to address the problems with multiple machines and buffers during transient periods. This dissertation addresses this problem by using simulations with C++ programming to evaluate the multiple machines (up to 10) and buffers and demonstrate the transient performance at different conditions. The simulation method does not only provide quantified transient performance results for a given production line, but also provides a valuable tool to investigate the system parameter effects and how to manage and improve the existing production line

Biologically inspired learning system

Learning Systems used on robots require either a-priori knowledge in the form of models, rules of thumb or databases or require that robot to physically execute multitudes of trial solutions. The first requirement limits the robot’s ability to operate in unstructured changing environments, and the second limits the robot’s service life and resources. In this research a generalized approach to learning was developed through a series of algorithms that can be used for construction of behaviors that are able to cope with unstructured environments through adaptation of both internal parameters and system structure as a result of a goal based supervisory mechanism. Four main learning algorithms have been developed, along with a goal directed random exploration routine. These algorithms all use the concept of learning from a recent memory in order to save the robot/agent from having to exhaustively execute all trial solutions. The first algorithm is a reactive online learning algorithm that uses a supervised learning to find the sensor/action combinations that promote realization of a preprogrammed goal. It produces a feed forward neural network controller that is used to control the robot. The second algorithm is similar to first in that it uses a supervised learning strategy, but it produces a neural network that considers past values, thus providing a non-reactive solution. The third algorithm is a departure from the first two in that uses a non-supervised learning technique to learn the best actions for each situation the robot encounters. The last algorithm builds a graph of the situations encountered by agent/robot in order to learn to associate the best actions with sensor inputs. It uses an unsupervised learning approach based on shortest paths to a goal situation in the graph in order to generate a non-reactive feed forward neural network. Test results were good, the first and third algorithms were tested in a formation maneuvering task in both simulation and onboard mobile robots, while the second and fourth were tested simulation

Utilizing a Network Program Representation to Support Production System Program Development and Execution.

This dissertation discusses modifications of the internal architecture of production systems that could significantly increase the execution performance of production system programs. This increased efficiency is achieved in part by modifications to the matching step of the execution cycle. Rather than checking all possible instantiations, we propose to consider only a small subset of the potential instantiations which are the best candidates for firing (according to the conflict resolution scheme). The increased execution efficiency provided by this matching strategy is compounded by modifying the OPS5 conflict resolution strategies. We propose a goal-directed (look-ahead) conflict resolution strategy which will still retain the responsiveness emphasized by OPS5. Execution efficiency may be further enhanced by dividing a program knowledge base into procedures, such that each procedure represents a logical unit of processing. As a procedure executes, only the productions forming the procedure are matched against the program database. This strategy reduces the matching overhead for the program. Modularization also enables the programmer to avoid unwanted rule interactions and permits data abstraction and information hiding in the procedures. Program development is supported by our algorithms to diagnose errors in both the program knowledge base and database. Many of these algorithms are based on a network representation of a program\u27s potential rule and procedure interactions. These tests may be administered at compile time and during program execution. The network program representation also forms the basis of techniques for program testing: the network forms the infrastructure for a graphical program trace, provides a means of measuring the comprehensiveness of program testing, and is utilized in determining the possible input and output data of a program\u27s potential execution paths. While we present our methods in the context of OPS5, a popular forward-chaining production system, these techniques may be applied to many other productions systems

Modelling, optimisation and control of series supply chains and production processes

In recent years supply chains have gained the attention of both academia and industry. In this thesis, a novel state-space model of a multi-node supply chain is presented, controlled via local proportional inventory-replenishment policies. The model is driven by a stochastic sequence representing customer demand. The model is analysed under stationarity conditions, guaranteed to arise if the control parameters lie in a certain range which is identified and a simple recursive scheme is further developed for updating the covariance matrix of the system in closed form, i. e., as an explicit function of the control parameters. This allows us to analyse the effect of inventory policies on the "bullwhip effect" (demand amplification) for chains with an arbitrary number of nodes. The three-node model is subsequently analysed in detail under information sharing and the optimal policy is derived, which minimises inventory fluctuations (and inventory mean) under a probabilistic constraint related to downstream demand. It is shown that this policy can never lead to demand amplification in the chain, as long as the gain parameter of the downstream node lies in the stability region. Finally, issues related to estimation schemes based on local historical data are discussed. The main results and conclusions are illustrated via numerous examples and simulations. An alternative model of the supply chain is also developed using timed Hierarchical Coloured Petri Nets (HCPN). This approach considers supply chains as event-driven systems and studies decentralised control structures by analysing the impact of various continuous inventory policies and known forecasting methods followed by supply chain participants. CPN-Tools [fCPN] are used for the design of decision-making policies and simulation results are presented to highlight the main issues arising in real systems and to provide insights for future modelling and simulation work. Finally, a detailed case study is undertaken, for the production line of the "Bridngorth Aluminium Ltd" company which produces high quality rolled aluminium lithographic strips. An efficient representation for such production processes is provided and subsequently used for an extensive analysis and performance evaluation through appropriate metrics. In particular, the work addresses the implementation of an overall model in a simulation environment, capable of integrating the various aspects of the specific production management processes