Development of a standard framework for manufacturing simulators

Discrete event simulation is now a well established modelling and experimental technique for the analysis of manufacturing systems. Since it was first employed as a technique, much of the research and commercial developments in the field have been concerned with improving the considerable task of model specification in order to improve productivity and reduce the level of modelling and programming expertise required. The main areas of research have been the development of modelling structures to bring modularity in program development, incorporating such structures in simulation software systems which would alleviate some of the programming burden, and the use of automatic programming systems to develop interfaces that would raise the model specification to a higher level of abstraction. A more recent development in the field has been the advent of a new generation of software, often referred to as manufacturing simulators, which have incorporated extensive manufacturing system domain knowledge in the model specification interface. Many manufacturing simulators are now commercially available, but their development has not been based on any common standard. This is evident in the differences that exist between their interfaces, internal data representation methods and modelling capabilities. The lack of a standard makes it impossible to reuse any part of a model when a user finds it necessary to move from one simulator to another. In such cases, not only a new modelling language has to be learnt but also the complete model has to be developed again requiring considerable time and effort. The motivation for the research was the need for the development of a standard that is necessary to improve reusability of models and is the first step towards interchangability of such models. A standard framework for manufacturing simulators has been developed. It consists of a data model that is independent of any simulator, and a translation module for converting model specification data into the internal data representation of manufacturing simulators; the translators are application specific, but the methodology is common and illustrated for three popular simulators. The data model provides for a minimum common model data specification which is based on an extensive analysis of existing simulators. It uses dialogues for interface and the frame knowledge representation method for modular storage of data. The translation methodology uses production rules for data mapping

Introduction to IntelliSIM 1.0

IntelliSIM is a prototype for a new generation of knowledge-based simulation tool that has been developed by the Systems Simulation Laboratory at Arizona State University. This tool is a computer environment that allows non-simulation trained modelers to predict the performance of a manufacturing system for which the necessary data is available. The system provides predictive data on such items as throughput time, queue levels, equipment utilization, reactions to machine failures, etc. With IntelliSIM, the benefits of discrete-event simulation can be exploited without requiring the high level of expertise necessary to successfully conduct a sound simulation study. The approach offered with IntelliSIM is one which will offer substantial savings over currently available simulation tools. This document is Version 1 (1992) of the user manual for the IntelliSIM software

Introduction to IntelliSIM 1.0

IntelliSIM is a prototype for a new generation of knowledge-based simulation tool that has been developed by the Systems Simulation Laboratory at Arizona State University. This tool is a computer environment that allows non-simulation trained modelers to predict the performance of a manufacturing system for which the necessary data is available. The system provides predictive data on such items as throughput time, queue levels, equipment utilization, reactions to machine failures, etc. With IntelliSIM, the benefits of discrete-event simulation can be exploited without requiring the high level of expertise necessary to successfully conduct a sound simulation study. The approach offered with IntelliSIM is one which will offer substantial savings over currently available simulation tools. This document is Version 1 (1992) of the user manual for the IntelliSIM software

The co-incident flow of work pieces and cutting tools in a restricted category of flexible machining cells

The work reported in this thesis describes research carried out into the detailed design and operation of Flexible Machining Cells (FMC) incorporating automated work and tool flow, dual flow. Three modes of cell management are considered for dual flow cells, where the author examines both their operational and economic performance. A framework is defined for investigating these dual flow cells, and a structured approach providing a novel and detailed modelling capability is described. The question of how this approach compares to single flow modelling and the additional or alternative requirements for dual flow modelling is examined via the following key areas; the specification of material handling requirements, tool transportation and issue and finally, the control required to examine the interaction between the two flows operating concurrently. The framework is tested for its industrial applicability via an industrial case study. A major aim of this study is to examine the view that a hybrid cell management strategy, competitive management, could outperform the other strategies examined. The aim of this methodology is to provide a solution for the control of FMCs. Emphasis is placed on the ease of control and how the loading and control rules selection can maximise economic enhancement of a cells performance

A computer-aided simulation tool based on Petri nets for the design and analysis of FMSs

Discrete-event simulation has been recognized as an invaluable tool in analyzing and designing FMSs. In this dissertation, a computer-aided simulation tool based on Petri nets is presented to facilitate simulation projects in the manufacturing area. For modeling and simulation of FMSs, Conserved nets which are a subclass of Petri nets are proposed and implemented. The structural characteristics and liveness conditions of Conserved nets are investigated. While hardware components are modeled by hierarchically-classified Petri net objects, high-level, real-time control systems in FMSs are separately modeled and integrated with a Petri net model to resolve conflicts occurring in Petri net execution. The structure of the Petri net-based simulation tool is presented. Also, the use of the simulation tool is illustrated with several case studies including performance comparison of push- and pull-based AGV dispatching rules in an FMS. Finally, strengths and weaknesses of the developed simulation tool are discussed including areas for future study

Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

Essays On Perioperative Services Problems In Healthcare

One of the critical challenges in healthcare operations management is to efficiently utilize the expensive resources needed while maintaining the quality of care provided. Simulation and optimization methods can be effectively used to provide better healthcare services. This can be achieved by developing models to minimize patient waiting times, minimize healthcare supply chain and logistics costs, and maximize access. In this proposal, we study some of the important problems in healthcare operations management. More specifically, we focus on perioperative services and study scheduling of operating rooms (ORs) and management of necessary resources such as staff, equipment, and surgical instruments. We develop optimization and simulation methods to coordinate material handling decisions, inventory management, and OR scheduling. In Chapter 1 of this dissertation, we investigate material handling services to improve the flow of surgical materials in hospitals. The ORs require timely supply of surgical materials such as surgical instruments, linen, and other additional equipment required to perform the surgeries. The availability of surgical instruments at the right location is crucial to both patient safety and cost reduction in hospitals. Similarly, soiled material must also be disposed of appropriately and quickly. Hospitals use automated material handling systems to perform these daily tasks, minimize workforce requirements, reduce risk of contamination, and reduce workplace injuries. Most of the literature related to AGV systems focuses on improving their performance in manufacturing settings. In the last 20 years, several articles have addressed issues relevant to healthcare systems. This literature mainly focuses on improving the design and management of AGV systems to handle the specific challenges faced in hospitals, such as interactions with patients, staff, and elevators; adhering to safety standards and hygiene, etc. In Chapter 1, we focus on optimizing the delivery of surgical instrument case carts from material departments to ORs through automated guided vehicles (AGV). We propose a framework that integrates data analysis with system simulation and optimization. We test the performance of the proposed framework through a case study developed using data from a partnering hospital, Greenville Memorial Hospital (GMH) in South Carolina. Through an extensive set of simulation experiments, we investigate whether performance measures, such as travel time and task completion time, improve after a redesign of AGV pathways. We also study the impact of fleet size on these performance measures and use simulation-optimization to evaluate the performance of the system for different fleet sizes. A pilot study was conducted at GMH to validate the results of our analysis. We further evaluated different policies for scheduling the material handling activities to assess their impact on delays and the level of inventory required. Reducing the inventory level of an instrument may negatively impact the flexibility in scheduling surgeries, cause delays, and therefore, reduce the service level provided. On the other hand, increasing inventory levels may not necessarily eliminate the delays since some delays occur because of inefficiencies in the material handling processes. Hospitals tend to maintain large inventories to ensure that the required instruments are available for scheduled surgery. Typically, the inventory level of surgical instruments is determined by the total number of surgeries scheduled in a day, the daily schedule of surgeries that use the same instrument, the processing capacity of the central sterile storage division (CSSD), and the schedule of material handling activities. Using simulation-optimization tools, we demonstrate that integrating decisions of material handling activities with inventory management has the potential to reduce the cost of the system. In Chapter 2 we focus on coordinating OR scheduling decisions with efficient management of surgical instruments. Hospitals pay more attention to OR scheduling. This is because a large portion of hospitals\u27 income is due to surgical procedures. Inventory management of decisions follows the OR schedules. Previous work points to the cost savings and benefits of optimizing the OR scheduling process. However, based on our review of the literature, only a few articles discuss the inclusion of instrument inventory-related decisions in OR schedules. Surgical instruments are classified as (1) owned by the hospital and (2) borrowed from other hospitals or vendors. Borrowed instruments incur rental costs that can be up to 12-25\% of the listed price of the surgical instrument. A daily schedule of ORs determines how many rental instruments would be required to perform all surgeries in a timely manner. A simple strategy used in most hospitals is to first schedule the ORs, followed by determining the instrument assignments. However, such a strategy may result in low utilization of surgical instruments owned by hospitals. Furthermore, creating an OR schedule that efficiently uses available surgical instruments is a challenging problem. The problem becomes even more challenging in the presence of material handling delays, stochastic demand, and uncertain surgery duration. In this study, we propose an alternative scheduling strategy in which the OR scheduling and inventory management decisions are coordinated. More specifically, we propose a mixed-integer programming model that integrates instrument assignment decisions with OR scheduling to minimize costs. This model determines how many ORs to open, determines the schedule of ORs, and also identifies the instrument assignments for each surgery. If the level of instrument inventory cannot meet the surgical requirements, our model allows instruments to be rented at a higher cost. We introduce and evaluate the solution methods for this problem. We propose a Lagrangean decomposition-based heuristic, which is an iterative procedure. This heuristic separates the scheduling problem from the inventory assignment problem. These subproblems are computationally easier to solve and provide a lower bound on the optimal cost of the integrated OR scheduling problem. The solution of the scheduling subproblem is used to generate feasible solutions in every iteration. We propose two alternatives to find feasible solutions to our problem. These alternatives provide an upper bound on the cost of the integrated scheduling problem. We conducted a thorough sensitivity analysis to evaluate the impact of different parameters, such as the length of the scheduling horizon, the number of ORs that can be used in parallel, the number of surgeries, and various cost parameters on the running time and quality of the solution. Using a case study developed at GMH, we demonstrate that integrating OR scheduling decisions with inventory management has the potential to reduce the cost of the system. The objective of Chapter 3 is to develop quick and efficient algorithms to solve the integrated OR scheduling and inventory management problem, and generate optimal/near-optimal solutions that increase the efficiency of GMH operations. In Chapter 2, we introduced the integrated OR scheduling problem which is a combinatorial optimization problem. As such, the problem is challenging to solve. We faced these challenges when trying to solve the problem directly using the Gurobi solver. The solutions obtained via construction heuristics were much farther from optimality while the Lagrangean decomposition-based heuristics take several hours to find good solutions for large-sized problems. In addition, those methods are iterative procedures and computationally expensive. These challenges have motivated the development of metaheuristics to solve OR scheduling problems, which have been shown to be very effective in solving other combinatorial problems in general and scheduling problems in particular. In Chapter 3, we adopt a metaheuristic, Tabu search, which is a versatile heuristic that is used to solve many different types of scheduling problems. We propose an improved construction heuristic to generate an initial solution. This heuristic identifies the number if ORs to be used and then the assignment of surgeries to ORs. In the second step, this heuristic identifies instrument-surgery assignments based on a first-come, first-serve basis. The proposed Tabu search method improves upon this initial solution. To explore different areas of the feasible region, we propose three neighborhoods that are searched one after the other. For each neighborhood, we create a preferred attribute candidate list which contains solutions that have attributes of good solutions. The solutions on this list are evaluated first before examining other solutions in the neighborhood. The solutions obtained with Tabu search are compared with the lower and upper bounds obtained in Chapter \ref{Ch2}. Using a case study developed at GMH, we demonstrate that high-quality solutions can be obtained by using very little computational time

An analysis of FMS scheduling problem: a beam search based algorithm and comparison of scheduling schemes

Ankara : Department of Industrial Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1994.Thesis (Master's) -- Bilkent University, 1994.Includes bibliographical references leaves 77-80FMS scheduling procedures in the literature can be classified into on-line and off-line schemes according to the number of scheduling decisions made at a point in time. Online scheduling attempts to schedule operations one at a time when it is needed and off-line scheduling refers to scheduling operations of available jobs for the entire scheduling period. In the literature there is no unified argument for or against either of these scheduling schemes. This research has two main objectives: development of a new scheduling scheme called quasi on-line that makes a trade-off between on-line and off-line schemes and comparison of the proposed scheme with others under various experimental conditions. A new algorithm is proposed on which the quasi online scheme is based. The proposed algorithm is a heuristic and utilizes a beam search technique. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and AGV schedules for a given scheduling period. A simulation model is also developed to implement and test scheduling schemes.Karabük, SüleymanM.S

Industry 4.0 challenges to IE paradigms: A pilot study in materials handling

Industrial engineering practices are expected to be affected by, and most likely adapt to, the new paradigms of Industry 4.0. Early indications in practice, as well as extrapolations from the current technology trends, point toward a few fundamental features. Among these are further integration, leaner and hence more agile practices, and the use of real-time data. The final objective is to reduce complexity while striving for real-time supply- and production-chain optimization. We argue that the optimization of highly integrated production systems cannot be sought by simply aggregating the known operations management tools of industrial engineering. Specifically, we present evidence, gleaned from a recent industrial project, that indicates how as the systems become more integrated, the concept of operations optimization needs to be revisited. Our work has two distinct contributions to the literature. We develop and present a state-of-the-art optimization model for a joint materials handling, inventory, and scheduling model. The model incorporates aspects of the knapsack, bin packing, vehicle routing, and inventory control formulations. Further, we show that simply collecting existing industrial engineering models into larger aggregations, albeit in line with the current best practices of our profession, will not necessarily suffice to completely fulfill the ambitions of Industry 4.0

Reference architecture for configuration, planning and control of 21st century manufacturing systems.

Today's dynamic marketplace requires flexible manufacturing systems capable of cost-effective high variety - low volume production in frequently changing product demand and mix. Several new paradigms, e.g. holonic, fractal, biological and responsive manufacturing, have recently been proposed and studied in the academic literature. These 'next generation of manufacturing systems' have been especially designed to meet the requirements of an unstable and unpredictable marketplace. However, very little in-depth research of the configuration, planning and control methodologies of these new concepts has been conducted. This research aims to improve the comprehension and implementation of these 21st century manufacturing systems by developing an integrated reference architecture from the combination of their distinctive features that would enable manufacturing enterprises to handle successfully the configuration/reconfiguration, planning and control activities under the conditions of uncertainty and continuous change.In the course of the research, a detailed investigation into the fractal, biological and responsive manufacturing systems is conducted in order to identify the strengths and weaknesses of each concept. The common and distinctive features of the paradigms are then used to merge them to create an integrated reference architecture. The fractal configuration, biological scheduling and 'resource element' representation of resource capabilities and product processing requirements are selected as the major elements of the new system. A detailed study of fractal layout design resulted in seven distinctive methods for structuring and managing fractal cellular systems. A design methodology that supports three types of dynamic scheduling is developed for biological manufacturing systems. Resource elements are used with fractal layouts and biological scheduling to enhance performance and to enable an integration of the concepts. The proposed reference architecture is modelled and evaluated using object-oriented programming, computer simulation and heuristic algorithms. The research results indicate that the performance of systems that employ biological scheduling and fractal layouts can be improved by using the concept of resource elements to utilise any hidden capabilities of resources and to achieve an optimal distribution of resources on the shop floor