Computer experiment – a case study for modelling and simulation of manufacturing systems

Deterministic computer simulation of physical experiments is now a common technique in science and engineering. Often, physical experiments are too time consuming, expensive or impossible to conduct. Complex computer models or codes, rather than physical experiments lead to the study of computer experiments, which are used to investigate many scientific phenomena. A computer experiment consists of a number of runs of the computer code with different input choices. The Design and Analysis of Computer Experiments is a rapidly growing technique in statistical experimental design. This paper aims to discuss some practical issues when designing a computer simulation and/or experiments for manufacturing systems. A case study approach is reviewed and presented.

Dynamic scheduling in manufacturing systems using brownian approximations

Recently, Brownian networks have emerged as an effective stochastic model to approximate multiclass queueing networks with dynamic scheduling capability, under conditions of balanced heavy loading. This paper is a tutorial introduction to dynamic scheduling in manufacturing systems using Brownian networks. The article starts with motivational examples. It then provides a review of relevant weak convergence concepts, followed by a description of the limiting behaviour of queueing systems under heavy traffic. The Brownian approximation procedure is discussed in detail and generic case studies are provided to illustrate the procedure and demonstrate its effectiveness. This paper places emphasis only on the results and aspires to provide the reader with an up-to-date understanding of dynamic scheduling based on Brownian approximations.

Performability of automated manufacturing systems with centralized material handling

We present a framework for performance evaluation of manufacturing systems subject to failure and repair. In particular, we determine the mean and variance of accumulated production over a specified time frame and show the usefulness of these results in system design and in evaluating operational policies for manufacturing systems. We extend this analysis for lead time as well. A detailed performability study is carried out for the generic model of a manufacturing system with centralized material handling. Several numerical results are presented, and the relevance of performability analysis in resolving system design issues is highlighted. Specific problems addressed include computing the distribution of total production over a shift period, determining the shift length necessary to deliver a given production target with a desired probability, and obtaining the distribution of Manufacturing Lead Time, all in the face of potential subsystem failures.

Stochastic modelling of flexible manufacturing systems

Mathematical modelling plays a vital role in the design, planning and operation of flexible manufacturing systems (FMSs). In this paper, attention is focused on stochastic modelling of FMSs using Markov chains, queueing networks, and stochastic Petri nets. We bring out the role of these modelling tools in FMS performance evaluation through several illustrative examples and provide a critical comparative evaluation. We also include a discussion on the modelling of deadlocks which constitute an important source of performance degradation in fully automated FMSs.

Structured systems methodology for evaluation of random interruptions in continuous process type manufacturing systems

A structured systems methodology was developed to analyse the problems of production interruptions occurring at random intervals in continuous process type manufacturing systems. At a macro level the methodology focuses on identifying suitable investment policies to reduce interruptions of a total manufacturing system that is a combination of several process plants. An interruption-tree-based simulation model was developed for macroanalysis. At a micro level the methodology focuses on finding the effects of alternative configurations of individual process plants on the overall system performance. A Markov simulation model was developed for microlevel analysis. The methodology was tested with an industry-specific application.

Coloured Petri net models for automated manufacturing systems

In this paper, we propose an approach, using Coloured Petri Nets (CPN) for modelling flexible manufacturing systems. We illustrate our methodology for a Flexible Manufacturing Cell (FMC) with three machines and three robots. We also consider the analysis of the FMC for deadlocks using the invariant analysis of CPNs.

Distributed Simulation Of Flexible Manufacturing Systems

Flexible Manufacturing Systems (FMS), widely considered as the manufacturing technology of the future, are gaining increasing importance due to the immense advantages they provide in terms of cost, quality and productivity over the conventional manufacturing. An FMS is a complex interconnection of capital intensive resources and high levels of system performance is very crucial for survival in a competing environment.Discrete event simulation is one of the most popular methods for performance evaluation of FMS during planning, design and operation phases. Indeed fast simulators are suggested for selection of optimal strategies for flow control (which part type to enter and at what instant), AGV scheduling (which vehicle to carry which part), routing (which machine to process the part) and part selection (which part for processing next). In this paper we develop a C-net based model for an FMS and use the same for distributed discrete event simulation. We illustrate using examples the efficacy of destributed discrete event simulation for the performance evaluation of FMSs.