Methods of Planning, Running and Optimization of Material Flow in the Laboratory of Flexible Manufacturing Systems with Robotized Manipulation Supported by No Drawing Production

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.925 ◽

2012 ◽

Vol 220-223 ◽

pp. 925-928

Author(s):

Silvia Sebenova ◽

Katarina Krajcova ◽

Frantisek Pechacek

Keyword(s):

Flexible Manufacturing ◽

Material Flow ◽

Flexible Manufacturing Systems ◽

Manufacturing Systems ◽

Production Costs ◽

Just In Time ◽

Selection Of

This paper is focused on the methods, which are used for planning, running and optimization of material flow. These methods are very important element of each production and company. There are several methods which are used, for example JIT (Just in Time), Kanban, TOC (Theorie of Constraints), etc. A selection of appropriate method affects largely production costs, efficiency and produced quantity. For the laboratory of flexible manufacturing systems with robotized manipulation supported by no drawing production were compared several methods and on the based their advantages, disadvantages and suitability of use was selected the most appropriate method of planning, running and optimization of material flow.

Download Full-text

Simulationsgestützte Produktionsplanung flexibler Fertigungssysteme*/Simulation-based production planning of flexible manufacturing systems

wt Werkstattstechnik online ◽

10.37544/1436-4980-2019-04-40 ◽

2019 ◽

Vol 109 (04) ◽

pp. 242-249

Author(s):

A. Selmaier ◽

T. Donhauser ◽

T. Lechler ◽

J. Zeitler ◽

J. Franke

Keyword(s):

Production Planning ◽

Flexible Manufacturing ◽

Material Flow ◽

Flexible Manufacturing Systems ◽

Manufacturing Systems ◽

Flow Simulation ◽

Limited Extent ◽

Time Data ◽

High Data ◽

Simulation Based

Während sich das Verhalten starr verketteter Systeme relativ einfach mittels Materialflusssimulationen modellieren lässt, sind herkömmliche Simulationsansätze für flexible Fertigungssysteme aufgrund des hohen Datenerhebungs- sowie Parametrisieraufwands nur bedingt geeignet. Jedoch kann durch das automatische Übertragen von Echtzeitdaten in das Simulationsmodell der aktuelle Zustand solcher Systeme deutlich verbessert abgebildet werden. Der Beitrag stellt ein Konzept für die simulationsgestützte Produktionsplanung schnellveränderlicher Systeme vor.   While the behaviour of rigidly linked systems is relatively easy to model by means of material flow simulation, traditional simulation approaches are only suitable to a limited extent for flexible manufacturing systems due to the high data collection and parameterization effort. However, the use of real-time data can significantly improve the simulation of such systems. This paper presents an approach for simulation-based production planning of rapidly changing systems.

Download Full-text

Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation

Sensors ◽

10.3390/s20216333 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6333 ◽

Cited By ~ 1

Author(s):

Fengjia Yao ◽

Bugra Alkan ◽

Bilal Ahmad ◽

Robert Harrison

Keyword(s):

Decision Making ◽

Flexible Manufacturing ◽

Flexible Manufacturing Systems ◽

Manufacturing Systems ◽

Discrete Event ◽

Mixed Integer ◽

Shop Floor ◽

Just In Time ◽

Delivery Performance ◽

Material Transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

Download Full-text