Application Environment for the demonstrators and test case

Size: px
Start display at page:

Download "Application Environment for the demonstrators and test case"

Transcription

1 Application Environment for the demonstrators and test case Tapio HEIKKILÄ, Jean-Pierre COURTOIS, Hartwig BAUMGAERTEL, Jo WYNS VTT Automation, Kaitoväylä 1, PO Box FIN Oulu, Finland, A.I. SYSTEMS, J. Wybran Avenue 40, B-1070 Brussels, Belgium, Daimler-Chrysler AG, Research and Technology, Alt-Moabit 96A, D Berlin, Germany, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium, 1. Introduction The MASCADA framework are tested within three different manufacturing domains, i.e. car manufacturing (the test of MASCADA), in electronics manufacturing, and in steel production. The two latter ones are clearly demonstrators, and this comes from the fact, that comprehensive testing and evaluation will be done only within the car manufacturing test bed, and only limited tests showing the feasibility of the MASCADA approach will be done within demonstrators. The test bed is described in details in the WP5 report, and only some general outline is given here. There are certain characteristics for demonstrators, which is necessary in WP6 to evaluate the generality of the approach. For example in car and electronics manufacturing customer orders can not be mapped onto the units of semi-finished products, which is the case in steel production. On the other hand, in steel production, there are strong time dependencies of the product properties. In addition, the test bed and demonstrators are all also providing necessary information and experience for the elaboration of the framework and related design guidelines in WP6. 2. Electronics Demonstrator Description 2.1. Case description There is a variety of changes and disturbances for which the manufacturing control system should contribute. Failures and errors originating prom the process (NC/insertion machines, robots, peripherals (feeders etc.), conveyors, AGVs,...) may cause re-direction of the material flows and re-transportation of the parts to new resources. The control system should maintain and have access to the latest available information about the process resources, and should be able allocate and reallocate orders to the available set of available resources avoiding such solutions which result into dead-locks or otherwise drastic reduction of through-put. For example, availability of transportation resources (transportation system) and needs for parts and material transportation (production lines and cells) should be matched together. This should take place within a limited time window ahead, finally depending on the applied production strategies and dominating criteria. There are certain restrictions for the order of process steps depending whether there are both through-hole components and SMD components in the product and whether the components are inserted by automatic insertion machines. Through-holes are inserted before the SMD s if only one soldering phase (wave solder) is used because of the mechanical shocks the through hole component insertion causes onto the board. If two soldering phases are used, typically re-flow is applied first for the other side of the board and then wave soldering to the other side of the board. If the insertion is done manually, then the order is not so critical. In some cases also the mechanical assembly is done in the same factory and production line. If the production lot size is high, this can be automated by hard automation, i.e. by customised assembly machines, or by assembly robots. The typical process steps for these two soldering principles are illustrated in [Lassila98].

2 goals from user orders, controls JAVA-agent controllers STATION11 CONTROLLER driver messages 111 STATION11 QUEST models LINE1 CONTROLLER AGV1 CONTROLLER AGV STATION12 CONTROLLER STATION Figure 1. The principal structure of the electronics demonstrator SW system Demonstrator description The basic approach for the demonstrations is to drive the simulation production model directly by the external agent based controls. Here the agent based control is composed of several agents running on one or more Windows NT processes connected in a local area network with TCP/IP protocol. The agent models are implemented in Java language using Symantec s Visual Cafe (2.5) rapid application tools. Those agent controllers which are managing physical machines and equipment (machines, AGVs etc.) are plugged in to the simulator by a specific driver interface (four digit driver codes consistent in both sides). The application software in the simulator implements the simple driver operations (digital mock-up approach). As a production system simulator the QUEST simulator software is used. The drivers in QUEST are implemented as different logics, i.e. process logic, routing logic, AGV logic and so. The organization of the software is illustrated in Figure Specificity There is a clear lot size for each order. This is clearly decomposed or mapped to the atomic resources. In other domains (car, steel) the orders are treated more in order to order basis. Also the dependency to the transportation system and the capacity it can provide (the inter-line and storageto/from-line transportations) is critical in electronics manufacturing. A special characteristic is to rely on capacity estimates given by the transportation system Purpose of the demonstrator From the emergent behaviour of the agent system, and from the ability on focusing on local aspects (production lines, transportation, stations within lines) the following benefits are expected: lower down times of the lines and higher through-put avoiding dead-locks and other situations with low efficiency better utilization of the resources, also within unexpected situations (caused by failures or human interventions) easier configurability and reconfigurability of the technology (hardware, software) The demonstrator is used to facilitate running ordinary production lots but with different products, introducing rush orders, and handling disturbances. The rush orders are introduced in arbitrary times by human operators, and disturbances are introduced by disabling one or more resources by disabling their simulator counterparts. In each case the thru-put of the whole system is observed.

3 STORAGE STORAGE AGV AGV Conv11 Conv21 Conv SMD1 SMD2 SMD3 SMD1 SMD2 SMD3 Conv12 Conv22 Conv AGV2 MAI11 MAI21 MAI31 Conv13 Conv23 Conv MAI11 MAI12 MAI21 MAI22 MAI31 Conv14 MAI12 Conv24 MAI22 Conv AGV2 AI11 AI21 AI31 Conv15 15 Conv25 Conv35 AI12 AI11 AI12 AI21 AI31 Conv16 Conv26 Conv AT11 AT21 AT31 AGV2 Conv17 Conv27 Conv AT12 AT22 AT32 AT11 AT12 AT21 AT31 AT32 Conv18 18 Conv28 28 Conv38 38 AGV AGV1 STORAGE STORAGE Figure 2 Conventional and very flexible process flows in electronics manufacturing 2.5. Principles of the approach There is a variety of changes and disturbances for which the manufacturing control system should contribute. Failures and errors originating prom the process (NC/insertion machines, robots, peripherals (feeders etc.), conveyors, AGVs,...) may cause re-direction of the material flows and re-transportation of the parts to new resources. The control system should maintain and have access to the latest available information about the process resources, and should be able allocate and reallocate orders to the available set of available resources avoiding such solutions which result into dead-locks or otherwise drastic reduction of through-put. For example, availability of transportation resources (transportation system) and needs for parts and material transportation (production lines and cells) should be matched together. This should take place within a limited time window ahead, finally depending on the applied production strategies and dominating criteria In the demonstrator the system is agentified by introducing agents to production lines and stations inside the lines, storage room controllers and opertors inside the sorages, and AGV transportation syste and indivisual AGVs. The goal for using agents is to create more flexible material flows (semi-products, parts&materials) by moving the decisions of transportations down in the hierarchy. Currently the transportation requests (for manual transportation) are created globally (review of the order queue by storage/transportation/line personal or by shits foremen). In agent like solution the decision is done by line controllers (decide which machines do what and when, which leads to the varying need of materials within different machines); the machines are responsible of acquiring parts & material so that they can maintain required procution Two types of algorithms are demonstrated: conventional dispatching rules, and emergent control. In the case of conventional ones rules like earliest-due-date and first-come-first-serv e are

4 applied throughout the system (line controllers, storage room controllers, AGV system controllers). In the case of emergent control the centralized decision making is reformulated to follow the distributed PROSA architecture. Order agents are introduced to take responsibility to find suitable resources for the production orders. All resources are connected based on the functional topology of the manufacturing system, defined by the process plans of the products, and the eligibilities of the resources to carry out certain process steps (in the process plans) for certain products. Instead of global search the basic idea is to maintain on-line information about the possible capacities of the system. This is done by propagating thru-put related data (lead times and capacities) upstream, and especially considering the real situation of the resource regarding confirmed orders for the resource (= reservations of the resource to an order within time). When only best (or two to three) routes per product type or product group are considered, the number of maintained information becomes substantially smaller (optimality). The role of the order agent is to use this on-line information, and make some inquiries downstream, starting from the available first resources within the routes, or virtual lines, and see how the possible thru-puts of (or capacities available for) other orders (all product types) behave, if the resources within the best route were allocated to that order. Such routes can be selected, which do not cause substantial reduction of thruputs for other possible orders (collaboration capability). In addition, the order agent will also test situation when each of the resource within the considerd route is broken down; such routes with second best routes should be selected, which do not cause substatial overloading of resources, i.e., the best thruputs of other orders would be available only using certain resource or few resources (proactiveness: avoid creating global bottlenecks within disturbances). In the case of disturbances, the order agent re-batches the order s original lot within the remaining part of the original batch, and routes this based on the available information of the optional routes downstream before the disturbed reosurce (reactivity). Because all the communication, both in maintaining the best available routings on-line, and trials to see what selections of certains routes will affect, is done only peer-to-peer (implicitly, based on the rpoduct type dependent connection information of the resources), there is no need maintain any global descriptions or calculate global features. This comes implicitly because the global properties are accumulated step by step in maintaining the thruput data, and also propagating the consequences of reserving the resources within the route for an order. The collaboration and proactive capabilities are evaluated by determining their goodness characteristic values, which are then combined. The order agent then selects that route which gives best compound value for compound optimality with collaborativeness, and proactiveness. Finally the role of the staff agent is to set the weights for optimality/collaborativity, and proactiveness to make the system work in an opportunistic way (closer to optimum) or carefully (beware of disturbance effects). 3. Steel case demonstrator description 3.1. Case description The steel case addresses the steelmaking area of the steel plant. It includes typical meltshop facilities, from the supply of pig iron at the converters, to the cutting of the slabs behind the continuous casters. The demonstrator focuses on flat product production processes, as it is the usual market of A.I. Systems, but it could easily be adapted to other type of steel products Demonstrator description The demonstrator is built with G2 (Gensym). A common plant layout has been chosen, with a set of representative facilities. Suspended cranes ensure the transport of the ladles between the facilities. A simplified model drives the operation of each facility and crane. Real data can be given as input instead of the model. The number of facilities and their interconnections can be easily changed Specificity The steel case demonstrator has some domain specific elements: steelmaking is a semi continuous process presenting a discontinuity. Liquid steel is treated in batches. But batches are merged in a continuous solid steel strip at the caster. Customer orders can not be mapped onto the units of semi-finished products.

5 Desulphurisation Converter loading O 2 blowing Converter lining repair Chemical analysis Converter unloading Vacuum degasing Ladle treatment Ladle lining repair Trimming Ladle Furnace Casting Tundish lining repair Cutting Figure 3. Steelmaking simplified process diagram The product properties are time dependant. The liquid phase involves high temperature and hot steel is subject to oxidisation. The sequence of operations requested to reach the right quality of steel requires a lot of engineering and metallurgical expertise. This demonstrator makes explicit call on the product agent. Due to the wear of the lining, several resources need frequent repairs that can be considered as processes as well. A specific set of order agents will be needed in the system to handle those processes. Although the steelmaking process is somehow unpredictable, a good scheduling can however result in huge gains. The demonstrator uses an initial schedule delivered by existing A.I. Systems software to drive the production. Scheduling can be seen as a kind of batching (in the sense of grouping the cars by colour in the automotive case). Given the nature of the logistics and process constraints, scheduling rules are more extensive and quite complex. Complex logistics and process constraints, sometimes contradictory from one production unit to the other, result in complex scheduling rules Goals and measurements The meltshop is committed to absorb the pig iron supply from the blast furnace. But it has also to balance the production flows among the downstream lines. Therefore, the objective of the

6 meltshop control system is to safeguard the throughput, not to maximise it. In function of the characteristics of the final product, the batches undergo different treatments resulting in different process time. Balancing the product mix is thus a way to control throughput. Given the target throughput and the equipment capacity, the scheduling package is able to chose the best product mix in order to saturate the converters and the casters. The lines are balanced. The main requirements for the control system are: routing control of the steel batches through the transportation system autonomous decision on solid strip cutting when uncertainties on batch weight are redrawn adaptation of the initial schedule if production problems occur (quality miss) Given the limited size of the WIP inventory in the meltshop and given the relatively long lead-time of the operations, re-scheduling is not a critical issue and has little interactions within the control system. Existing scheduling packages can easily be used for this purpose. On the contrary, negotiating the routing when several hundred of different qualities can be produced, and co-ordinating the transport of empty and full ladles between the facilities is a critical issue. As a consequence, the demonstrator mainly addresses the issue of routing negotiation and crane movement control in the scope of Mascada. The scheduling package takes capacity constraints of the meltshop facilities into account, but neither the routing possibilities nor the constraints of the transportation system. Hence, throughput can be highly affected by transportation problems (jams, delays, trajectory conflicts ). The control system must thus ensure the transport system is able to cope with the production schedule, i.e. steel batches are delivered on time at processing facilities. Difficulties come from limited or competing transport device movements, and from competing transport of empty containers needing maintenance process Purpose of the demonstrator The purpose of the demonstrator is to assess the applicability of the proposed solutions across applications, specifically in the field AI Systems is active in. Ultimately, the demonstrator should allow evaluating the feasibility of a sellable product, and testing the interest of the market. The following advantages are expected: Gain in software development and maintenance. Gain in system configuration. Gain in system tuning. Lower decision step for potential customers. A demonstrator easy to configure allows simulation and evaluation of the product at low cost Principles of the approach In a first phase, the demonstrator will assess the ability of the system to cope with lay-out adaptation and apply the pheromone algorithms to the meltshop control system. The control system should be able to ensure proper routing of theorders through the meltshop facilities, and to take appropriate decisions whatever changes or disturbances occur in the meltshop. In function of the quantity of pig iron to absorb, the capacity of the meltshop may be adapted by shutting down or putting into service some facilities. Delays, failures or breakdowns may affect the production processes or the transportation system and require re-routing of the orders, or even re-scheduling. In any case, the control system should safeguard target throughput and ensure transportation system doesn't hinder the completion of the schedule. In current operations, human dispatchers take routing decisions and give instructions to crane operators on the spot in function of the course of operations on the floor. But JIT strikes in steel production too, and the need for automation increases with the complexity and the flexibility of the production processes. The demonstrator will automate the meltshop logistics and apply the Mascada control algorithms to the meltshop control system. In a first phase, it will assess the applicability of the algorithms in this peculiar context as well as the ability of the system to cope with lay-out adaptation. Resources agents will control all facilities and equipment of the meltshop. In function of the sequence of operations delivered by the product agents, production orders and maintenance orders will negotiate their way through the various processes and request transport from the transportation system. Decisions are taken locally and can be reviewed in function of the course of operations. Processing is constantly monitored by order agents. Staff agent may add for co-ordination

7 and proactiveness to make the system work in a more optimal way. An important parameter for the productivity is for example the number of ladles in circulation. More ladles in circulation means less probability of deadlocks, but big savings can be reached by cutting the number of ladles. To safeguard throughput, transport priority should go to orders heading for or leaving constrained resources. Constraints may move over time in function of the product mix. Algorithms should avoid crane conflicts and choose the routing to balance the load of the resources. Time plays an important role as characteristics of the product (composition, temperature) are evolving throughout the process. Spreading information over estimated order processing time, transport devices status and estimated transport time, among others, will allow for more appropriate decision taking as orders get through the processes. 4. Automotive testbed The automotive case serves as the main testbed for the Mascada control system. It will be described in detail in the deliverables of WP5. Here only a short description is included to show the differences and similarities with the two demonstrator cases. As a consequence, this section will show a lot of overlap with the WP5 deliverable Case description For details of the properties of the plant, we refer to the report Deliverable of Work Package 5a: Plant configurations of the main testbed: Painting Center of the Mercedes-Benz passenger car plant at Sindelfingen. The short introduction to the automotive case: The result of the painting and inspection processes will determine the eventual need for further processing: repair preparation, grinding, heavy repair, repaint,... Therefore, the route of the car can not be determined up front. The data analysis shows that the yield of the painting process is influenced by the batch size of cars of the same color. Cars coming from the repair loops break the batch size, and therefore increase the risk of sending even more cars towards repair and repaint processes. The workload of the painting units depends on their yield, since the units also have to repaint the badly painted cars. The transport system contains more than 200 routing devices (lifts, crossings, sorting buffer,...) where a car has/may decide which way to go. These are the decision points for the control system. Figure 22 shows the Arena model of the physical layout of the plant. The main goal of the plant is to increase throughput: number of cars produced per day Demonstrator description The agent based control system is written in Java and can run on any Java Virtual Machine. Currently, we use Sun s JDK on Windows NT. A digital mock-up of the physical plant is build in the Arena simulation tool (see Figure 22). The control system communicates to the digital mock-up via sockets. The control system sends commands to the digital mock-up: Lift X: move car from entrance Y to exit Z, SortingBuffer1: release car X,... The control system receives status information from the digital mock-up: car X arrived at lift Y, car X painted, car X needs heavy repair,... The digital mock-up replicates the behavior of the real plant, including the statistics to generate painting problems, and mimicking the influence of batching of cars with the same color. The simulation in Arena is proportional to the real time. This is important, since the control system shall be given the time to decide where to send a car. The effect of the calculation time on the physical flow of cars in the system shall be minimized Specificity The control system (also the current one) does not know up front what type of cars (body type, color) will arrive at the input (North bridge). It is only when the car passes the first sensor at the first crossing of the plant (just behind the North bridge), that the control system reads the identification number of the car, and can read the car type and color information Purpose of the new control approach The new control system shall show a higher throughput (# cars / day). This can only be achieved by better reaction to disturbances.

8 S B 1 S B 1 S B 1 e A r e n a n u m b e r : e I Id ld el e Ie d l e le E N S W e S B 2 e I d l e Figure 4: Part of the physical layout of the car painting plant. Increasing the throughput can be achieved by the minimization of losses. In this case, this is requires: increasing average painting batch size of painting of batches of cars with the same color, reducing the planned work-load on the (current) bottleneck machines, avoid blockages in the transport system, avoid blocking a (bottleneck) machine by a full buffer behind the machine or the transport system behind the machine, avoid starving a bottleneck machine by an empty buffer before the machine or the transport system before the machine, satisfy the customer by delivering cars before their due-date. The control system shall allow changes in the system. This will be evaluated by applying the control system to a totally new layout of the car painting plant (see report on deliverable 5a) Principles of the approach This details of the new control system will be described in the final WP5 deliverable. Main principle of the new control system are: The decision points are distributed over the transport system: at every routing device (lift, crossing, sorting buffer,...) a decision is to be made which car goes where and when. This decision is made by negotiation between the local order agents (cars) and resource agents (routing devices, conveyors). The decision is constraint by the layout of the transport system, and the abilities of the processing stations. The pheromone based control algorithm shall distribute and transform local information, in order to represent at each distributed decision point, suitable information about the global system state. This shall allow the local agents to make a founded decision. If possible, the control system will use no a priori information about the system layout and

9 capabilities. This will increase the applicability of the control system to different plants. As a consequence, aggregated transport resource agents (e.g. buffer before repair) are avoided as much as possible. They hinder the adaptation of the control system to changes and disturbances: the aggregated transport resources impose a certain usage of a section of the transport system. For instance, the buffer before repair will control the underlying conveyors and lifts in order to get a buffering behavior. In the new approach, such buffering behavior shall emerge out of the constraints and goals of the involved car order agents, conveyor agents, and lift agents. This way, the buffering behavior will also automatically disappear in case the current situation requires these lifts and conveyor to quickly move cars, instead of storing them. 5. Acknowledgement This paper presents research results obtained through projects sponsored by the European Community. The Mascada project is supported by ESPRIT LTR. The scientific responsibility is assumed by the authors. 6. References [Lassila98] Lassila K., Heikkilä T., Requirements for Flexible Manufacturing in the Production of Printed Board Assemblies. Proceedings of First International Workshop on Intelligent Manufacturing Systems - IMS Europe April 1998, Lausanne, Switzerland. Pp [Que97] [Are97] QUEST Release Notes Version 3.0., Deneb Robotics, Inc. Auburn Hills, Michigan, USA, Arena User's Guide, Systems Modeling Corporation, Sewickley, USA, 1997.

HOLONIC CONTROL OF AN ENGINE ASSEMBLY PLANT AN INDUSTRIAL EVALUATION

HOLONIC CONTROL OF AN ENGINE ASSEMBLY PLANT AN INDUSTRIAL EVALUATION HOLONIC CONTROL OF AN ENGINE ASSEMBLY PLANT AN INDUSTRIAL EVALUATION Stefan BUSSMANN and Jörg SIEVERDING DaimlerChrysler AG Research and Technology 3 Alt-Moabit 96a, 10559 Berlin, Germany {Stefan.Bussmann,

More information

Salzgitter Stahl AG & PSI Metals. more than 30 years of mutual history. Slide 1

Salzgitter Stahl AG & PSI Metals. more than 30 years of mutual history. Slide 1 Salzgitter Stahl AG & PSI Metals more than 30 years of mutual history Slide 1 Salzgitter AG: Group Structure (simplified) Slide 2 Salzgitter AG: Subsidiaries Bad Salzdetfurth Slide 3 PSImetals Systems

More information

UNIT III GROUP TECHNOLOGY AND FMS

UNIT III GROUP TECHNOLOGY AND FMS UNIT III GROUP TECHNOLOGY AND FMS GROUP TECHNOLOGY Group technology is a manufacturing technique and philosophy to increase production efficiency by exploiting the underlying sameness of component shape,

More information

Optimizing Inplant Supply Chain in Steel Plants by Integrating Lean Manufacturing and Theory of Constrains through Dynamic Simulation

Optimizing Inplant Supply Chain in Steel Plants by Integrating Lean Manufacturing and Theory of Constrains through Dynamic Simulation Optimizing Inplant Supply Chain in Steel Plants by Integrating Lean Manufacturing and Theory of Constrains through Dynamic Simulation Atanu Mukherjee, President, Dastur Business and Technology Consulting,

More information

SIMUL8-PLANNER FOR COMPOSITES MANUFACTURING

SIMUL8-PLANNER FOR COMPOSITES MANUFACTURING Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds. SIMUL8-PLANNER FOR COMPOSITES MANUFACTURING Kim Hindle Project

More information

Increasing Your Competitiveness in PCB Assembly

Increasing Your Competitiveness in PCB Assembly Increasing Your Competitiveness in PCB Assembly This article offers a seven step model for analyzing and integrating production systems. This phased approach can reduce costs, improve on-time delivery,

More information

Ch 19 Flexible Manufacturing Systems

Ch 19 Flexible Manufacturing Systems Ch 19 Flexible Manufacturing Systems Sections: 1. What is a Flexible Manufacturing System? 2. FMS Components 3. FMS Applications and Benefits 4. FMS Planning and Implementation Issues 5. Quantitative Analysis

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 MANUFACTURING SYSTEM Manufacturing, a branch of industry, is the application of tools and processes for the transformation of raw materials into finished products. The manufacturing

More information

Autonomous Shop Floor Control Considering Set-up Times

Autonomous Shop Floor Control Considering Set-up Times Autonomous Shop Floor Control Considering Set-up Times B. Scholz-Reiter, T. Jagalski, C. de Beer, M. Freitag Department of Planning and Control of Production Systems, University of Bremen, Germany Abstract

More information

INTEGRATION OF AUTONOMOUS SYSTEM COMPONENTS USING THE JAUS ARCHITECTURE

INTEGRATION OF AUTONOMOUS SYSTEM COMPONENTS USING THE JAUS ARCHITECTURE INTEGRATION OF AUTONOMOUS SYSTEM COMPONENTS USING THE JAUS ARCHITECTURE Shane Hansen Autonomous Solutions, Inc. Phone: (435) 755-2980 Fax: (435) 752-0541 shane@autonomoussolutions.com www.autonomoussolutions.com

More information

Transactions on the Built Environment vol 34, 1998 WIT Press, ISSN

Transactions on the Built Environment vol 34, 1998 WIT Press,  ISSN Improving the Dutch railway services by network-wide timetable simulation Jurjen S. Hooghiemstra", Dick M. Middelkoop", Maurice J.G. Teunisse^ " Railned, Dept. of Innovation, P.O.Box 2025, 3500 HA Utrecht,

More information

AGV Controlled FMS. The ITB Journal. Fergus G. Maughan. Volume 1 Issue 1 Article 5

AGV Controlled FMS. The ITB Journal. Fergus G. Maughan. Volume 1 Issue 1 Article 5 The ITB Journal Volume 1 Issue 1 Article 5 2000 AGV Controlled FMS Fergus G. Maughan Follow this and additional works at: http://arrow.dit.ie/itbj Part of the Other Operations Research, Systems Engineering

More information

A SORTATION SYSTEM MODEL. Arun Jayaraman Ramu Narayanaswamy Ali K. Gunal

A SORTATION SYSTEM MODEL. Arun Jayaraman Ramu Narayanaswamy Ali K. Gunal A SORTATION SYSTEM MODEL Arun Jayaraman Ramu Narayanaswamy Ali K. Gunal Production Modeling Corporation 3 Parklane Boulevard, Suite 910 West Dearborn, Michigan 48126, U.S.A. ABSTRACT Automotive manufacturing

More information

THE ROLE OF SIMULATION IN ADVANCED PLANNING AND SCHEDULING. Kenneth Musselman Jean O Reilly Steven Duket

THE ROLE OF SIMULATION IN ADVANCED PLANNING AND SCHEDULING. Kenneth Musselman Jean O Reilly Steven Duket Proceedings of the 2002 Winter Simulation Conference E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, eds. THE ROLE OF SIMULATION IN ADVANCED PLANNING AND SCHEDULING Kenneth Musselman Jean O Reilly

More information

Development of Material Control System for Next Generation Liquid Crystal Glass

Development of Material Control System for Next Generation Liquid Crystal Glass Vol. No. Development of Material Control System for Next Generation Liquid Crystal Glass HASEGAWA Fumio : Senior Researcher, Control System Project Department, Products Development Center, Corporate Research

More information

Tecnomatix Plant Simulation Validation of Plant Performance and Plant Control Dr. Georg Piepenbrock, Siemens Industry Software

Tecnomatix Plant Simulation Validation of Plant Performance and Plant Control Dr. Georg Piepenbrock, Siemens Industry Software Tecnomatix Plant Simulation Validation of Plant Performance and Plant Control Dr. Georg Piepenbrock, Siemens Industry Software Digital Enterprise is our portfolio of solutions for the digital transformation

More information

ARCHITECTURE OF FMS. Typical Elements of FMS. Two Kind of Integration. Typical Sequence of Operation

ARCHITECTURE OF FMS. Typical Elements of FMS. Two Kind of Integration. Typical Sequence of Operation Typical Elements of FMS ARCHITECTURE OF FMS Versatile NC machines equipped with automatic tool changing and inprocess gauging, with capability to carry out a variety of operations An automated Material

More information

Drum-Buffer-Rope in PlanetTogether Galaxy

Drum-Buffer-Rope in PlanetTogether Galaxy Drum-Buffer-Rope in PlanetTogether Galaxy This document provides background on Theory of Constraints and Drum-Buffer-Rope scheduling. It describes how to assess whether the DBR approach is appropriate

More information

A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture

A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture Jin-Lung Chirn, Duncan C. McFarlane Institute for Manufacturing, University of Cambridge Mill Lane, Cambridge, CB2

More information

Program Evaluation and Review Technique (PERT)

Program Evaluation and Review Technique (PERT) Program Evaluation and Review Technique (PERT) PERT Bar charts and CPM networks assume all activity durations are constant or deterministic. The assumption of constant durations may not be realistic because

More information

On the Identification of Agents in the Design of Production Control Systems

On the Identification of Agents in the Design of Production Control Systems On the Identification of Agents in the Design of Production Control Systems Stefan Bussmann 1, Nicholas R. Jennings 2, and Michael Wooldridge 3 1 DaimlerChrysler AG, Research and Technology 3 Alt-Moabit

More information

PRODUCTIVITY IMPROVEMENT IN APPLIANCE MANUFACTURING

PRODUCTIVITY IMPROVEMENT IN APPLIANCE MANUFACTURING Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. PRODUCTIVITY IMPROVEMENT IN APPLIANCE MANUFACTURING Charles Harrell

More information

standard component library

standard component library standard component library manual standard component library /21 standard component library manual 2/21 Table of Contents layouts Airport Baggage Handling High Volume Consumer Goods (HVCG) Packing Line

More information

FACULTATEA DE INGINERIE HERRMANN OBERTH MASTER-PROGRAM EMBEDDED SYSTEMS. Plant Control

FACULTATEA DE INGINERIE HERRMANN OBERTH MASTER-PROGRAM EMBEDDED SYSTEMS. Plant Control FACULTATEA DE INGINERIE HERRMANN OBERTH MASTER-PROGRAM EMBEDDED SYSTEMS Plant Control PROFESSOR: PROF. DR. ING. ZAMFIRESCU STUDENT: STEFAN FEILMEIER - 03.12.2014 - Agent-based modeling and simulation of

More information

Description of the Rear-Axle Assembly Demo Model for Tecnomatix Plant Simulation March 2013 CONTENT

Description of the Rear-Axle Assembly Demo Model for Tecnomatix Plant Simulation March 2013 CONTENT Description of the Rear-Axle Assembly Demo Model for Tecnomatix Plant Simulation March 2013 CONTENT 1. Description... 2 1.1 Objective... 2 1.2 System Characteristics... 2 1.3 Sequence... 2 1.4 Results...

More information

Ch 15 Manual Assembly Lines

Ch 15 Manual Assembly Lines Ch 15 Manual Assembly Lines Sections: 1. Fundamentals of Manual Assembly Lines 2. Analysis of Single Model Assembly Lines 3. Line Balancing Algorithms 4. Mixed Model Assembly Lines 5. Workstation Considerations

More information

Investigating the Influences of Automated Guided Vehicles (AGVs) as Material Transportation for Automotive Assembly Process

Investigating the Influences of Automated Guided Vehicles (AGVs) as Material Transportation for Automotive Assembly Process Journal of Mechanical Engineering Vol SI 4 (1), 47-60, 2017 Investigating the Influences of Automated Guided Vehicles (AGVs) as Material Transportation for Automotive Assembly Process Seha Saffar * Centre

More information

What we are expecting from this presentation:

What we are expecting from this presentation: What we are expecting from this presentation: A We want to inform you on the most important highlights from this topic D We exhort you to share with us a constructive feedback for further improvements

More information

Flexible Manufacturing Systems

Flexible Manufacturing Systems Flexible Manufacturing Systems FMS is: Machine Cell used to implement the Group Technology Composed of Multiple automated stations Capable of Variable Routings (Type IIA) Integrate CNC, Computer Control,

More information

Introduction to Software Engineering

Introduction to Software Engineering CHAPTER 1 Introduction to Software Engineering Structure 1.1 Introduction Objectives 1.2 Basics of Software Engineering 1.3 Principles of Software Engineering 1.4 Software Characteristics 1.5 Software

More information

Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds.

Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds. Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds. A SIMULATION-BASED LEAN PRODUCTION APPROACH AT A LOW-VOLUME PARTS MANUFACTURER

More information

Figure 2: Industrial robots performing spot-welding operations in a respot line.

Figure 2: Industrial robots performing spot-welding operations in a respot line. Automobile Final Assembly Plants Introduction: Demand for the automobile and the development of production technology to meet this demand have been responsible for much of the economic growth in many countries

More information

Whitepaper: PSItraffic Train Management System safe operations with modern system architecture

Whitepaper: PSItraffic Train Management System safe operations with modern system architecture Whitepaper: PSItraffic Train Management System safe operations with modern system architecture PSItraffic/TMS PSItraffic Train Management safe operations with modern system architecture In the future,

More information

Benefits of Advanced Configuration and Simulation Study

Benefits of Advanced Configuration and Simulation Study 1(11) Benefits of Advanced Configuration and Simulation Study Teemu Kolkka Jagannathan Rajagopalan Juha Suksi 2(11) Benefits of Advanced Configuration and Simulation Study May 31, 2013 Dear Reader, This

More information

CONFIGURATION OF AN AUTONOMOUS DECENTRALIZED SDIGITAL FACTORY USING PRODUCT AND MACHINE AGENTS

CONFIGURATION OF AN AUTONOMOUS DECENTRALIZED SDIGITAL FACTORY USING PRODUCT AND MACHINE AGENTS 23 CONFIGURATION OF AN AUTONOMOUS DECENTRALIZED SDIGITAL FACTORY USING PRODUCT AND MACHINE AGENTS Michiko Matsuda and Nobuyuki Sakao Kanagawa Institute of Technology, matsuda@ic.kanagawa-it.ac.jp The configuration

More information

Flexible Manufacturing System (FMS) IE447

Flexible Manufacturing System (FMS) IE447 Flexible Manufacturing System (FMS) A Closer Look IE447 Spring2011 At the turn of the century FMS did not exist. There was not a big enough need for efficiency because the markets were national and there

More information

DELMIA QUEST. The Systems Integration, Process Flow Design and Visualization Solution

DELMIA QUEST. The Systems Integration, Process Flow Design and Visualization Solution Resour ce Modeling & Simulation DELMIA QUEST The Systems Integration, Process Flow Design and Visualization Solution QUEST DELMIA QUEST is a complete 3D digital factory environment for The Systems Integration,

More information

Material Handling Automation Driving Wider Adoption of WES Warehouse Execution Systems (WES) Evolve to Meet E-commerce Demands

Material Handling Automation Driving Wider Adoption of WES Warehouse Execution Systems (WES) Evolve to Meet E-commerce Demands Material Handling Automation Driving Wider Adoption of WES Warehouse Execution Systems (WES) Evolve to Meet E-commerce Demands Material Handling Automation Driving Wider Adoption of WES www.intelligrated.com

More information

Smart Data Analytics: BMW Group relies on intelligent use of production data for efficient processes and premium quality

Smart Data Analytics: BMW Group relies on intelligent use of production data for efficient processes and premium quality Smart Data Analytics: BMW Group relies on intelligent use of production data for efficient processes and premium quality Data analysis creates growing added value for continuous improvement in the production

More information

BITO LEO LOCATIVE READY, STEADY, GO! This driverless transport system is immediately.

BITO LEO LOCATIVE READY, STEADY, GO! This driverless transport system is immediately. solutions BITO LEO LOCATIVE This driverless transport system is immediately READY, STEADY, GO! www.leo-locative.com » BITO LEO LOCATIVE There is no easier way to move your bins from one workstation to

More information

SAPP Simulation aided production planning at Flensburger

SAPP Simulation aided production planning at Flensburger SAPP Simulation aided production planning at Flensburger White Paper Helping shipbuilding planners and shop floor foremen easily analyze and verify all phases of production plans. White Paper SAPP Simulation

More information

An Agent-based Approach to the Control of Flexible Production Systems

An Agent-based Approach to the Control of Flexible Production Systems An Agent-based Approach to the Control of Flexible Production Systems Bussmann, S. and Schild, K. DaimlerChrysler AG, Research and Technology 3, Alt-Moabit 96a, 10559 Berlin (GERMANY) T. +49-30-39982-215,

More information

Assigning Storage Locations in an Automated Warehouse

Assigning Storage Locations in an Automated Warehouse Proceedings of the 2010 Industrial Engineering Research Conference A. Johnson and J. Miller, eds. Assigning Storage Locations in an Automated Warehouse Mark H. McElreath and Maria E. Mayorga, Ph.D. Department

More information

Performance Improvement of the Flexible Manufacturing System (FMS) with a Proper Dispatching Rules Planning

Performance Improvement of the Flexible Manufacturing System (FMS) with a Proper Dispatching Rules Planning Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Performance Improvement of the Flexible Manufacturing System (FMS)

More information

A TUTORIAL ON ERGONOMIC AND PROCESS MODELING USING QUEST AND IGRIP. Deidra L. Donald

A TUTORIAL ON ERGONOMIC AND PROCESS MODELING USING QUEST AND IGRIP. Deidra L. Donald Proceedings of the 1998 Winter Simulation Conference D.J. Medeiros, E.F. Watson, J.S. Carson and M.S. Manivannan, eds. A TUTORIAL ON ERGONOMIC AND PROCESS MODELING USING QUEST AND IGRIP Deidra L. Donald

More information

Introduction to software testing and quality process

Introduction to software testing and quality process Introduction to software testing and quality process Automated testing and verification J.P. Galeotti - Alessandra Gorla Engineering processes Engineering disciplines pair construction activities activities

More information

Paper Review: Proactive Traffic Merging Strategies for Sensor-Enabled Cars. Brian Choi - E6778 February 22, 2012

Paper Review: Proactive Traffic Merging Strategies for Sensor-Enabled Cars. Brian Choi - E6778 February 22, 2012 Paper Review: Proactive Traffic Merging Strategies for Sensor-Enabled Cars Brian Choi - E6778 February 22, 2012 Objective Highway traffic throughput at a ramp merging section can be optimized when sensor-enabled

More information

Integrate production and logistics areas in ERP-SYSTEMS

Integrate production and logistics areas in ERP-SYSTEMS Integrate production and logistics areas in ERP-SYSTEMS Automated food production requires deep IT integration Management Summary Industry everywhere is under pressure to increase productivity: The critical

More information

Vision of Congestion-Free Road Traffic and Cooperating Objects

Vision of Congestion-Free Road Traffic and Cooperating Objects I. Vision Vision of Congestion-Free Road Traffic and Cooperating Objects Ricardo Morla November 2005 Overview. This is a vision of cooperating vehicles that help keep roads free of traffic congestion.

More information

Real-Time Selection of Process Plan in Flexible. Manufacturing Systems: Simulation Study

Real-Time Selection of Process Plan in Flexible. Manufacturing Systems: Simulation Study International Conference on Industrial Engineering and Systems Management IESM 2007 May 30 - June 2, 2007 BEIJING - CHINA Real-Time Selection of Process Plan in Flexible * Manufacturing Systems: Simulation

More information

Proactive Resequencing of the Vehicle Order in Automotive Final Assembly to Minimize Utility Work

Proactive Resequencing of the Vehicle Order in Automotive Final Assembly to Minimize Utility Work Journal of Industrial and Intelligent Information Vol. 6, No. 1, June 2018 Proactive Resequencing of the Vehicle Order in Automotive Final Assembly to Minimize Utility Work Marius Schumacher, Kai D. Kreiskoether,

More information

Mission Planning Systems for Earth Observation Missions

Mission Planning Systems for Earth Observation Missions Mission Planning Systems for Earth Observation Missions Marc Niezette Anite Systems GmbH Robert Bosch StraJ3e 7 Darmstadt, Germany Marc.Niezette@AniteSystems.de Abstract This paper describes two different

More information

Automatic Panel Bender: Today's solution. For your tomorrow.

Automatic Panel Bender: Today's solution. For your tomorrow. Automatic Panel Bender: Today's solution. For your tomorrow. A winning solution to shape the future. The P4 Panel Bender is a smart manufacturing tool, invented by Guido Salvagnini in 1979, designed for

More information

Planning manufacturing systems

Planning manufacturing systems Planning manufacturing systems When planning manufacturing systems, the degree of automation that can economically be justified must be considered. Experience has shown that the most successful ones are

More information

Manual Assembly Lines

Manual Assembly Lines Manual Assembly Lines Sections: 1. Fundamentals of Manual Assembly Lines 2. Analysis of Single Model Assembly Lines 3. Line Balancing Algorithms 4. Other Considerations in Assembly Line Design 5. Alternative

More information

Lean Six Sigma Assembly Transformation

Lean Six Sigma Assembly Transformation Lean Six Sigma Assembly Transformation Assembly Operation converted to one piece flow assembly lines to improve throughput and efficiency. Problem Statement The first issue was ramping production to meet

More information

Will Flexible-Cell Manufacturing Revolutionize Carmaking?

Will Flexible-Cell Manufacturing Revolutionize Carmaking? Will Flexible-Cell Manufacturing Revolutionize Carmaking? The Boston Consulting Group (BCG) is a global management consulting firm and the world s leading advisor on business strategy. We partner with

More information

DEVELOPMENT OF AUTOMATIC CONVEYOR SYSTEM AT CONSTRUCTION SITES

DEVELOPMENT OF AUTOMATIC CONVEYOR SYSTEM AT CONSTRUCTION SITES 355 DEVELOPMENT OF AUTOMATIC CONVEYOR SYSTEM AT CONSTRUCTION SITES Hiroshi Nojima, Yoshimi Nakata, Masakazu Kakuyama, Seiichi Shibayama, Wataru Isomura, Taro Okamoto Technical Research Institute Fujita

More information

PROMAT S LATEST & GREATEST

PROMAT S LATEST & GREATEST PROMAT S LATEST & GREATEST John Hill St. Onge Company John Sarinick Beumer Corporation Session 108 Sponsored by: 2015 MHI Copyright claimed for audiovisual works and sound recordings of seminar sessions.

More information

Level 2/3 control system PSImetals of PSI. Ira Vollenberg, PSI Metals DissTec Webinar,

Level 2/3 control system PSImetals of PSI. Ira Vollenberg, PSI Metals DissTec Webinar, Level 2/3 control system PSImetals of PSI Ira Vollenberg, PSI Metals DissTec Webinar, 18.10.2017 PSI Process Control & Information Systems PSI is a leading supplier of process control software for utilities

More information

Planning for the Semiconductor Manufacturer of the Future

Planning for the Semiconductor Manufacturer of the Future From: AAAI Technical Report SS-92-01. Compilation copyright 1992, AAAI (www.aaai.org). All rights reserved. Planning for the Semiconductor Manufacturer of the Future Hugh E. Fargher 8~ Richard A. Smith

More information

Design and simulation of integration system between automated material handling system and manufacturing layout in the automotive assembly line

Design and simulation of integration system between automated material handling system and manufacturing layout in the automotive assembly line IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS and simulation of integration system between automated material handling system and manufacturing layout in the automotive assembly

More information

Tools for Assessing the Responsiveness of Existing Production Operations

Tools for Assessing the Responsiveness of Existing Production Operations Tools for Assessing the Responsiveness of Existing Production Operations Jeremy Matson and Duncan McFarlane Manufacturing Automation & Control Group Department of Engineering, University of Cambridge Mill

More information

Computer-Integrated Manufacturing

Computer-Integrated Manufacturing Supplement F Supplement F Computer-Integrated Manufacturing Computer-Integrated Manufacturing TRUE/FALSE 1. Computer-integrated manufacturing (CIM) is an umbrella term for the total integration of product

More information

MFS605/EE605 Systems for Factory Information and Control

MFS605/EE605 Systems for Factory Information and Control MFS605/EE605 Systems for Factory Information and Control Fall 2004 Larry Holloway Dept. of Electrical Engineering and Center for Robotics and Manufacturing Systems 1 Collect info on name, major, MS/PhD,

More information

Technology Improving Your Meltshop Performance

Technology Improving Your Meltshop Performance Technology Improving Your Meltshop Performance Graham Cooper INTRODUCTION Every casting (Fig.1) begins with molten metal and every foundry wants to make a profit. To melt metal, it requires energy and

More information

ScienceDirect. Cloud Communication Concept for Bionic Assembly System

ScienceDirect. Cloud Communication Concept for Bionic Assembly System Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 69 ( 2014 ) 1562 1568 24th DAAAM International Symposium on Intelligent Manufacturing and Automation, 2013 Cloud Communication

More information

Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.

Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. MACHINE CONTROL LEVEL SIMULATION OF AN AS/RS IN THE AUTOMOTIVE INDUSTRY Min

More information

STUDY NO 5 INTRODUCTION TO FLEXIBLE MANUFACTURING SYSTEM

STUDY NO 5 INTRODUCTION TO FLEXIBLE MANUFACTURING SYSTEM STUDY NO 5 INTRODUCTION TO FLEXIBLE MANUFACTURING SYSTEM A flexible manufacturing system (FMS) is in which there is some amount of flexibility that allows the system to react in case of changes, whether

More information

MB0044 Production and Operations Management. Assignment Set - 1

MB0044 Production and Operations Management. Assignment Set - 1 Production and Operations Management Assignment Set - 1 Q1. Explain briefly the Computer Integrated Manufacturing. Answer: Computer Integrated Manufacturing Integration occurs when a broad range of manufacturing

More information

Capacity Modelling for Virtual Enterprises

Capacity Modelling for Virtual Enterprises Capacity Modelling for Virtual Enterprises JoiioA. Rastos (1), Jorge P. Sousa (1,2) ( 1)/NESC- Rua lose Falciio, I 10-4300 Porto, Portugal (2)FEUP- University of Porto email: {jbastos, jsousaj@inescn.pt

More information

WHITE PAPER. CONTROL-M: Empowering the NetWeaver Solution

WHITE PAPER. CONTROL-M: Empowering the NetWeaver Solution WHITE PAPER CONTROL-M: Empowering the NetWeaver Solution e TABLE OF CONTENTS INTODUCTION...3 SAP NETWEAVER OVERVIEW... 3 COMPREHENSIVE TECHNOLOGY - INCREASING THE CHALLENGE... 4 CHALLENGES IN THE NETWEAVER

More information

Optimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory

Optimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory Optimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory Satoshi Hoshino 1, Jun Ota 1, Akiko Shinozaki 2, and Hideki Hashimoto 2 1 Dept. of Precision Engineering,

More information

MATERIAL HANDLING IN FLEXIBLE MANUFACTURING SYSTEM Mr. Neeraj Nirmal 1, Mr. Neeraj Dahiya 2

MATERIAL HANDLING IN FLEXIBLE MANUFACTURING SYSTEM Mr. Neeraj Nirmal 1, Mr. Neeraj Dahiya 2 International Journal of Computer Science and Management Studies, Vol. 11, Issue 02, Aug 2011 40 MATERIAL HANDLING IN FLEXIBLE MANUFACTURING SYSTEM Mr. Neeraj Nirmal 1, Mr. Neeraj Dahiya 2 1 M.Tech. Scholar,

More information

Use of automation to improve productivity and quality in long product rolling mills

Use of automation to improve productivity and quality in long product rolling mills Use of automation to improve productivity and quality in long product rolling mills Automation directly impacts long product rolling mill productivity and product quality. By identifying areas of under-performance,

More information

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 9, September 2017, pp. 274 280, Article ID: IJMET_08_09_029 Available online at http://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=8&itype=9

More information

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 9, September 2017, pp. 274 280, Article ID: IJMET_08_09_029 Available online at http://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=8&itype=9

More information

Efficient messaging through cluster coordinators in decentralized controlled material flow systems

Efficient messaging through cluster coordinators in decentralized controlled material flow systems Efficient messaging through cluster coordinators in decentralized controlled material flow systems Christian Lieberoth-Leden 1, Daniel Regulin 2 and W. A. Günthner 1 1 Technical University of Munich, Institute

More information

Automatic Vehicle Identification System (AVI) Training Manual

Automatic Vehicle Identification System (AVI) Training Manual Automatic Vehicle Identification System (AVI) Training Manual Chapter 6: Vehicle Management Owner: APICS Page 1 of 22 Revision: 1.1 TABLE OF CONTENTS 6 VEHICLE MANAGEMENT...4 6.1 OVERVIEW...4 6.2 VEHICLE

More information

COMAU S FIRST AGV: A FLEXIBLE SOLUTION FOR THE SMART FACTORY

COMAU S FIRST AGV: A FLEXIBLE SOLUTION FOR THE SMART FACTORY COMAU S FIRST AGV: A FLEXIBLE SOLUTION FOR THE SMART FACTORY The Turin-based giant chooses the motion control specialist as its AGV solution provider The result of this collaboration is AGILE1500 Comau

More information

An Agent-Based Scheduling Framework for Flexible Manufacturing Systems

An Agent-Based Scheduling Framework for Flexible Manufacturing Systems An Agent-Based Scheduling Framework for Flexible Manufacturing Systems Iman Badr International Science Index, Industrial and Manufacturing Engineering waset.org/publication/2311 Abstract The concept of

More information

Autonomy Requirements for Smart Vehicles

Autonomy Requirements for Smart Vehicles Autonomy Requirements for Smart Vehicles Emil Vassev April 24, 2017 09/08/2017 Lero 2015 1 Outline Autonomy Autonomous Vehicles and Safety Autonomy Requirements Engineering (ARE) KnowLang The ARE Formal

More information

IIE/RA Contest Problems

IIE/RA Contest Problems 1 CONTEST PROBLEM 7 IIE/RA Contest Problems Seventh Annual Contest: SM Testing SM Testing is the parent company for a series of small medical laboratory testing facilities. These facilities are often located

More information

Xerox International Partners (XIP), established in 1991 as a joint venture between Fuji Xerox Co. Ltd.

Xerox International Partners (XIP), established in 1991 as a joint venture between Fuji Xerox Co. Ltd. IMPROVED ORDER FILL RATE, DSI AND OPERATIONAL EFFICIENCY FOR DEMAND DRIVEN SUPPLY CHAIN BY UPGRADING AND INTEGRATING MANUGISTICS SCPO AND COLLABORATE SOLUTION TO JDA 7.4.X SUCCESSFULLY Sudam Sahoo, President,

More information

FUNCTIONS CRYOGENIC-GASES TERMINAL AUTOMATION SYSTEM FUNCTION OVERVIEW DESCRIPTION. Graphical User Interface (GUI)

FUNCTIONS CRYOGENIC-GASES TERMINAL AUTOMATION SYSTEM FUNCTION OVERVIEW DESCRIPTION. Graphical User Interface (GUI) FUNCTIONS CRYOGENIC-GASES TERMINAL AUTOMATION SYSTEM DESCRIPTION CRYO.TAS is a freely scalable terminal automation system. It can be purchased as a turn-key overall system with our standard hardware, or

More information

MANUFACTURING SYSTEM BETP 3814 INTRODUCTION TO MANUFACTURING SYSTEM

MANUFACTURING SYSTEM BETP 3814 INTRODUCTION TO MANUFACTURING SYSTEM MANUFACTURING SYSTEM BETP 3814 INTRODUCTION TO MANUFACTURING SYSTEM Tan Hauw Sen Rimo 1, Engr. Mohd Soufhwee bin Abd Rahman 2, 1 tanhauwsr@utem.edu.my, 2 soufhwee@utem.edu.my LESSON OUTCOMES At the end

More information

MetRo Warehouse Management System. Maximize. your. logistic. performance. MetRo WMS means intelligence

MetRo Warehouse Management System. Maximize. your. logistic. performance. MetRo WMS means intelligence MetRo Warehouse Management System Maximize your performance logistic MetRo WMS means intelligence MetRo WMS Rocla MetRo is a software package for warehouse management. Warehouse Management System, WMS,

More information

We fabricate your system

We fabricate your system We fabricate your system Schleicher designs each VZ/VZM heavy duty shredder to fulfil customer-specific needs by using a highly flexible modular system. This concept assures fulfilment of your requirements

More information

Unit WorkBook 1 Level 5 ENG U48 Manufacturing Systems Engineering UniCourse Ltd. All Rights Reserved. Sample

Unit WorkBook 1 Level 5 ENG U48 Manufacturing Systems Engineering UniCourse Ltd. All Rights Reserved. Sample Pearson BTEC Levels 5 Higher Nationals in Engineering (RQF) Unit 48: Manufacturing Systems Engineering Unit Workbook 1 in a series of 1 for this unit Learning Outcome LO1 to LO4 Manufacturing Systems Engineering

More information

Introduction to Computer Integrated Manufacturing Environment

Introduction to Computer Integrated Manufacturing Environment Introduction to Computer Integrated Manufacturing Environment I. What are the problems facing manufacturing industries today? External pressures: *Technological advancements *Increased cost, quality, and

More information

Simulation Using. ProModel. Dr. Charles Harrell. Professor, Brigham Young University, Provo, Utah. Dr. Biman K. Ghosh, Project Leader

Simulation Using. ProModel. Dr. Charles Harrell. Professor, Brigham Young University, Provo, Utah. Dr. Biman K. Ghosh, Project Leader T H R D E D T 0 N Simulation Using ProModel Dr. Charles Harrell Professor, Brigham Young University, Provo, Utah Director, PROMODEL Corporation, Oram, Utah Dr. Biman K. Ghosh, Project Leader Professor,

More information

Automated Guided Vehicles: Complete End-to-End Solutions Jana Kocianova & Craig Henry Manufacturing in America March 20-21, 2019

Automated Guided Vehicles: Complete End-to-End Solutions Jana Kocianova & Craig Henry Manufacturing in America March 20-21, 2019 Automated Guided Vehicles: Complete End-to-End Solutions Jana Kocianova & Craig Henry Manufacturing in America March 20-21, 2019 Optimizing Production Flexibility with Automated Guided Vehicles (AGVs)

More information

Multi-Agent Model for Power System Simulation

Multi-Agent Model for Power System Simulation Multi-Agent Model for Power System Simulation A.A.A. ESMIN A.R. AOKI C.R. LOPES JR. G. LAMBERT-TORRES Institute of Electrical Engineering Federal School of Engineering at Itajubá Av. BPS, 1303 Itajubá/MG

More information

PRODUCTION PLANNING PROBLEMS IN CELLULAR MANUFACTURE

PRODUCTION PLANNING PROBLEMS IN CELLULAR MANUFACTURE PRODUCTION PLANNING PROBLEMS IN CELLULAR MANUFACTURE J. Riezebos Assistant professor Production systems design, University of Groningen, P.O.Box 800, 9700 AV Groningen, The Netherlands, email J.Riezebos@Bdk.Rug.NL,

More information

ADAPTIVE MULTIAGENT SYSTEMS APPLIED ON TEMPORAL LOGISTICS NETWORKS. P. Knirsch (1) andi.j.timm (1)

ADAPTIVE MULTIAGENT SYSTEMS APPLIED ON TEMPORAL LOGISTICS NETWORKS. P. Knirsch (1) andi.j.timm (1) ADAPTIVE MULTIAGENT SYSTEMS APPLIED ON TEMPORAL LOGISTICS NETWORKS P. Knirsch (1) andi.j.timm (1) (1) Logistics Research Group, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany {knirsch,

More information

Implementation and Validation of a Holonic Manufacturing Control System

Implementation and Validation of a Holonic Manufacturing Control System 400 Flexible Automation & Intelligent Manufacturing, FAIM2005, Bilbao, Spain Implementation and Validation of a Holonic Manufacturing Control System Paulo Leitão 1, Francisco Restivo 2 1 Department of

More information

Design. Production. Service. Services

Design. Production. Service. Services simply automation Company profile Founded in 1991, Evolut started as a technological partner for the main robot producers. Today Evolut is active on the market as one of the most important Italian and

More information

EMBEDDING HUMAN SCHEDULING IN A STEEL PLANT SIMULATION. David Briggs

EMBEDDING HUMAN SCHEDULING IN A STEEL PLANT SIMULATION. David Briggs Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. EMBEDDING HUMAN SCHEDULING IN A STEEL PLANT SIMULATION David Briggs Business

More information

Utilization vs. Throughput: Bottleneck Detection in AGV Systems

Utilization vs. Throughput: Bottleneck Detection in AGV Systems Utilization vs. Throughput: Bottleneck Detection in AGV Systems Christoph Roser Masaru Nakano Minoru Tanaka Toyota Central Research and Development Laboratories Nagakute, Aichi 480-1192, JAPAN ABSTRACT

More information

ControllerMES Manufacturing execution system For the wood and furniture industry

ControllerMES Manufacturing execution system For the wood and furniture industry ControllerMES Manufacturing execution system For the wood and furniture industry HOMAG Consulting & Software Karl-Berner-Strasse 4 72285 Pfalzgrafenweiler Germany Phone: + 49 (0) 7445 830-0 software.solutions@homag.com

More information