Simulation study of a semi-automated flexible production line

In today’s highly competitive and challenging marketplace, manufacturing process improvement is more important than ever before. Conversely, it is probably also harder to achieve than at any time in the past. This is due to several factors. High levels of capital investment combined with short product life cycles mean that maximising utilisation levels of expensive equipment is essential. Increasingly complex production facilities are difficult to analyse and improve. The possibility of worsening the situation rather than improving it means that experimentation on the line itself is often a risk not worth taking. One solution to this problem is the use of computer based manufacturing system simulation. Simulation studies are beneficial because they remove the element of risk associated with experimentation. Potential process improvement strategies can be identified, evaluated, compared and chosen in a virtual environment before eventual implementation on the factory floor. This research aimed to evaluate the use of discrete event system simulation in a real world manufacturing environment. To this end, a flexible simulation model of the main transfer line of Läpple Ireland, a large metal panel production facility, was designed and constructed using Extend simulation software. In conjunction with Läpple personnel, various ‘what if’ scenarios were identified and evaluated. These scenarios were aimed at deciding the best position for providing additional automation by investing in robots. From the results of the simulation modelling of the three main proposed modifications to the line, improvements of 9%, 18% and 33% in press line throughput were predicted. The negative effect on these improvements in the case that the proposed robots failed to achieve the desired speeds were evaluated. These negative effects were found to be not as dramatic as could be expected. The results were compared to those of similar research efforts elsewhere. Finally, future steps for the research to take were identified and suggestions for future areas of application for the model were made

Embedding simulation technologies in business processes.

The need to fully integrate simulation as a daily tool has been subject to much attention over the past few years, however little research has previously contributed to this area. This study examines the development of systematic guidelines to enable companies to strategically implement simulation as a mainstream technology within their businesses.An extensive review of the literature was conducted in order to investigate the reasons behind the limited use of simulation and to establish the failure and success factors of companies implementing new technology. The importance of knowledge management in developing simulation technology was also investigated. Additionally, a questionnaire survey was conducted to examine the ways in which simulation technology has been used and developed within different companies. Furthermore, a case study was conducted in order to understand and investigate the processes of implementing simulation in a real organisation.Subsequently, an easy-to-follow framework for enabling companies to embed simulation technologies into their business processes was developed. This framework comprises five key stages, namely: Foundation, Introduction, Infrastructure,Deployment and Embedding. Each stage provides a best practice approach to guide companies in achieving every objective of that stage. Adjustments to the framework were made in the validation and reliability section to reduce any limitations.In creating a relevant and workable framework, this study has contributed significantly to the research gap established within existing simulation integration studies

MATERIAL HANDLING RESOURCE UTILIZATION SIMULATION STUDY FOR STAMPING PLANT

This paper describes the application of dynamic simulation to evaluate material handling resource utilization for a stamping plant in the automotive industry. The other objective of this study was evaluation of throughput relative to press schedules, shift patterns, the number of material handling resources (i.e. fork truck and tugger train drivers), and storage inventory levels. This dynamic simulation study enabled plant managers to balance the driver utilization with respect to time and to accommodate typical pres

Efficient Material Flow in Mixed Model Assembly Lines

Effizienten Materialfluss im Mischmodell Montagelinien Diese Arbeit untersucht ein Materialflusssystem für Fließlinien zur Fertigung von variantenreichen Produkten. Sogenannte Routenzüge kommen häufig zur Bereitstellung von Teilen an den Arbeitsstationen einer Variantenfließlinien zum Einsatz. Die Teile werden in kleinen Behältern (Kleinladungsträgern) im Zentrallager oder in verteilten Zwischenlagern, sogenannten Supermärkten, auf den Routenzug geladen. Bei jeder Tour des Routenzugs werden mehrere Arbeitsstationen mit Kleinladungsträgern versorgt. Der zeitliche Abstand zwischen zwei Belieferungen definiert die Zugumlaufzeit. Ein derartiges Materialbereitstellungssystem, bezeichnet als In-Plant Milk Run, reduziert Bestands- und Transportkosten, weil es eine regelmäßige Just-in-Time Anlieferung der Materialien realisiert. Außerdem bringt es aufgrund der Nutzung kleiner Behälter ergonomische Vorteile mit sich. Weiterhin sinkt das Unfallrisiko. Deshalb findet das In-plant Milk Run System als Alternative zu Gabelstaplern in vielen Branchen, insbesondere in der Automobilindustrie, zunehmend Verwendung. Die Untersuchung dieser Systeme und die optimale Auswahl ihrer Parameter sind wichtige Anliegen, um die Transport- und Bestandskosten zu reduzieren. Diese Arbeit unterscheidet bei der Gestaltung von In-plant Milk Run Systemen fünf verschiedene Problemstellungen (Systeme). Für jedes System wird ein bestimmtes Planungsvorgehen zur Minimierung der kritischen Kosten vorgeschlagen. Zwischen den Systemen gibt es sowohl Ähnlichkeiten als auch Unterschiede. Die Methodik verwendet genetische Algorithmen, ganzzahlige und dynamische Programmierung, Simulation und analytische Untersuchung. Die fünf Systeme werden anhand von Kriterien klassifiziert. Als solche werden das Ausmaß von Störungen, die Verfügbarkeit der Routenzüge, die Genauigkeit der Materialbedarfsschätzungen, die Länge der Montagelinien, der mittlerer Teilebedarf und die Verfügbarkeit von technischer Infrastruktur, wie RFID- oder Barcode-Systemen, herangezogen. Unterschieden werden damit das bedarfsorientierte Zentrallager, der dezentrale bedarfsorientierte Supermarkt, das traditionelle Kanban-System, das elektronische Kanban-System und ein hybrides System, bestehend aus bedarfsorientiertem und e-Kanban-System. Dabei kann das Kanban-System sowohl im Zentrallager, als auch im System dezentraler Supermärkte zum Einsatz kommen. In bedarfsorientierten Systemen wird der Materialbedarf der Arbeitsstationen für eine gewisse Anzahl an Schichten aus der Produktionssequenz und den entsprechenden Stücklisten abgeleitet und ist damit exakt bekannt. In allen Systemen werden einige Restriktionen berücksichtigt. Hierunter fallen die Routenzugkapazität, die Dauer einer Tour und die Kapazität der Lagerflächen direkt an der Montagelinie. In jedem System sind drei Entscheidungsprobleme, das Routing, Scheduling und Loading Problem, zu lösen. Das Routing Problem beinhaltet die Zuordnung von Zügen zu Gruppen von Arbeitsstationen. Im Scheduling Problem werden die Zugumlaufzeit und der Zeitpunkt der ersten Belieferung für jeden Routenzug festgelegt. Die Lösung des Loading Problems erfordert die Determinierung von Art und Menge der in jedem Zyklus und an jede Arbeitsstation ausgelieferten Behälter. Im Falle des Vorhandenseins von Zyklen, in denen der Materialbedarf an einzelnen Arbeitsstationen die Routenzugkapazität übersteigt, werden einige Behälter vorzeitig angeliefert. Dieser Fall wird als „Early Loading“ bezeichnet und tritt in Kanban-Systemen nicht auf. Im System dezentraler bedarfsorientierter Supermärkte ist zusätzlich die Anzahl und der Standort der Supermärkte zu bestimmen („Supermarket Location Problem“). Im traditionellen Kanban-System erfolgt die Festlegung der Kanbanzahl basierend auf dem Zielkonflikt zwischen mittlerem Linienbestand und Fehlbestandswahrscheinlichkeit. Im e-Kanban-System wird der Umfang des zirkulierenden Bestands analog bestimmt. Außerdem wird ein neues Konzept, der sogenannte „Adjusted Electronic Kanban“, zur Behandlung von Kapazitätsengpässen des Routenzugs vorgestellt. Die Ergebnisse sind abhängig vom betrachteten System. Die Leistungsfähigkeit des genetischen Algorithmus zur Lösung des Supermarket Location Problems wurde anhand der Ergebnisqualität, CPU Zeit und der Variabilität dieser beiden Größen untersucht. Es wurden akzeptable CPU Zeiten und eine hohe Ergebnisqualität erreicht. Die Leistungsfähigkeit der drei Kanban-Systeme wurde unter Verwendung von Simulation getestet. Hierbei wurde die Vorteilhaftigkeit des Adjusted Electronic Kanban insbesondere im Fall begrenzter Routenzugkapazität bewiesen. Der inverse Zusammenhang zwischen mittlerem Linienbestand und Fehlbestandswahrscheinlichkeit konnte aufgezeigt werden. Im Falle der bedarfsorientierten Systeme wurde der Effekt von dynamischer Disposition, Early Loading und Minimierung der Anzahl zusätzlicher Anhänger deutlich gemacht. Bei Verwendung des hybriden Systems aus e-Kanban und bedarfsorientiertem System, liefert die dynamische Disposition in Bezug auf die Verarbeitung von Störungenerheblich bessere Resultate als die Einzelsysteme, insbesondere bei hohem Materialbedarf an den Arbeitsstationen.This study investigates the material handling system used in mixed model assembly lines which are important to produce diversified product models to satisfy the increasing customer demand. Tugger trains are used to feed by parts the workstations in the assembly lines. These parts are loaded on trains in small containers (bins) from the warehouse or intermediate stores scattered in the factory. These stores are called supermarkets, which are closer to workstations than the main warehouse. In each train tour, several workstations are replenished by bins every a certain time period called train cycle time. This replenishment system is called in-plant milk run which is used to reduce inventory and transportation costs because of its dependence on repetitive just-in-time parts delivery. Besides reducing costs, ergonomic advantages are obtained due to the use of small-sized bins. Safety hazards are also reduced. As an alternative to forklift system, in-plant milk run was used by several industries especially the automotive industry. It is important to investigate this system and to design its parameters to reduce the total material handling and inventory cost. The study divides the general problem to five different problems (systems) based on the situation on the ground. For each system, a certain planning approach is designed to optimize the parameters of the system to minimize its critical costs. There are some similarities and differences between the systems. The methodology is based on genetic algorithm, integer programming, dynamic programming, simulation, and analytical investigation. The five different systems are classified based on factors such as level of assembly line disturbances, availability of tugger trains, accuracy of expectation of workstations demand for parts, the length of assembly lines and their average demand for parts, and the availability of technical infrastructure such as radio frequency identification (RFID) or bar code technologies. These systems are main warehouse demand-oriented, decentralized supermarket demand-oriented, traditional kanban, electronic kanban, and a hybrid system of e-kanban and demand-oriented systems. The two kanban systems can be applied in both main warehouse and decentralized supermarkets systems. In demand-oriented systems, the exact workstations demand for parts is assumed to be known for the next few shifts based on the predetermined sequencing of product models and needed parts for each product model. Generally some constraints are considered in all the five systems. These constraints are tugger train capacity, tour time, and the capacity of area beside stations. There are three general problems that must be investigated in the systems. These problems are routing, scheduling, and loading problems. Routing problem is the assignment of trains to different stations. In scheduling problem, the train cycle time and the beginning of the movement of the each train are determined. In loading problem, the type and quantities of bins delivered in each train cycle to each workstation are determined. In the case that there are some peak demand periods in which the total stations demand for parts is more than the tugger trains capacity, some bins are delivered before they are needed. This case is called ‘early loading’. Early loading does not exist in both the traditional and electronic kanban systems. In decentralized supermarket demand-oriented system, the location and number of supermarkets are determined. In traditional kanban system, the number of kanban is determined based on the tradeoff between the average line-side inventory and workstation starvation. In e-kanban, the size of circulating inventory in the system is determined for the same purpose. A new approach namely, adjusted electronic kanban, is presented to accommodate train capacity problems. The results depend on the systems investigated. The performance of genetic algorithm used in supermarket location problem was tested based on the quality of the results, CPU time, and variability in both of them. Reasonable CPU time and high quality of results were obtained. The performances of e-kanban, adjusted electronic kanban, and traditional kanban were tested using simulation, where the superiority of adjusted electronic kanban was proven especially in the case of limited tugger trains capacity. The inverse relationship between the average line-side inventory and workstation starvation was presented. In the case of demand-oriented system, the effects of using dynamic scheduling, early loading, and the objective of minimizing the number of extra trailers were obvious to reduce the problems of tugger train limited capacity. In the case of using the hybrid system of e-kanban and demand-oriented systems, the dynamic planning approach outperforms the traditional systems to accommodate the line disturbances especially in the case of large workstations demand