Simulation of Production Lines Involving Unreliable Machines; the Importance of Machine Position and Breakdown Statistics (original) (raw)

Simulation modelling and analysis of a production line

International Journal of Simulation and Process Modelling, 2017

Production lines modelling has many problems that are difficult to be solved using analytical solutions, due to uncertainty and/or variability in variables and parameters. This paper is targeting unreliable production lines with finite buffers for the objective of evaluating and analysing the current situation and identifying bottlenecks. We use discrete event simulation to model a real production line based on one-year historical data about the breakdowns of all the production line's machinery. This data is used to find appropriate probability distributions to represent each machinery downtime and uptime. The simulation model is built using AnyLogic software to abstract the actual production line, and the model is validated using the actual data and information about the production line. Improvement scenarios are proposed to resolve the observed bottlenecks, and therefore give managerial insights to increase the throughput according to the increasing demand. Finally, production plans are set for different demands along the year.

Reducing negative impact of machine failures on performance of filling and packaging production line - a simulative study

2016 Winter Simulation Conference (WSC), 2016

The paper demonstrates the use of a Discrete Event Simulation tool to reduce the negative impact of machine failures on the performance of a filling line. The buffer allocation problem has received a lot of attention, but still there are examples of unreliable production systems for which a buffer can be allocated in order to increase their productivity. The subject of the study is a filling and packaging production line which consisted of seven machines connected by conveyors. Machine failures are registered by maintenance Data Acquisition system. Those data are used to derive statistical distributions for Time To Repair and Time Between Failures. The model is built using FlexSim simulation software and different allocation scenarios are considered. Introduction of buffers results in an increase in mean line throughput by 15%. The initial results indicate that the proposed approach may lead to the reduction of negative effects of machine failures.

Analysis of two-machine lines with multiple failure modes

IIE Transactions, 2002

This paper presents an analytical method for evaluating the performance of production lines with a finite buffer and two unreliable machines. Unlike in earlier papers, each machine can fail in more than one way. For each failure mode, geometrically distributed times to failure and times to repair are specified. The method evaluates the steady state probabilities of the states of the system with a computational effort that depends only on the number of failure modes considered and not on the capacity of the buffer. A comparison of performance of the method with those obtained with existing techniques that consider only one failure mode is reported.

Capacity improvement of an unreliable production lineā€”an analytical approach

Computers & Operations Research, 2006

This paper addresses the problem of capacity estimation and improvement of a multi-stage, multi-product production line where workstations are subject to random failure and repair. The production line can process a variety of products in a batch production environment. Products are processed according to a predefined sequence. A linear programming model is used and modified by taking into account the random behaviour of unreliable stations. Station's downtime is modelled as a fictive product added to the production sequence at appropriate positions. A general procedure for the insertion of fictive products is presented. The procedure considers the states up and down that a station may experience while processing the product mix. It consists of two main steps. Firstly, enumerate the station's states and insert fictive products where appropriate. Secondly, find the best buffer size that minimizes the cycle time. The proposed approach considers more parameters than the Markovian models and the approximation methods where multi-product production lines longer than 2-station 1-buffer can be studied. Numerical examples are presented to show all the steps involved to compute the expected cycle time. Buffer contribution to minimize the cycle time of the production line is also addressed. Simulation is used to validate the results obtained.

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