A bi-objective stochastic programming model for optimising automated material handling systems with reliability considerations

The optimisation of material handling systems (MHSs) can lead to substantial cost reductions in manufacturing systems. Choosing adequate and relevant performance measures is critical in accurately evaluating MHSs. The majority of performance measures used in MHSs are time-based. However, moving mate...

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Bibliographic Details
Published in:International journal of production research Vol. 52; no. 19; pp. 5597 - 5610
Main Authors: Tavana, Madjid, Fazlollahtabar, Hamed, Hassanzadeh, Reza
Format: Journal Article
Language:English
Published: London Taylor & Francis 01.01.2014
Taylor & Francis LLC
Subjects:
Automated
automated guided vehicle
Automated storage retrieval systems
Automation
Cost reduction
Effectiveness
Goal programming
Manufacturing
material handling system
Materials handling
Mathematical models
Neural networks
Optimization
Optimization algorithms
perceptron neural network
Programming
reliability
Stochastic models
stochastic programming
Stochasticity
Studies
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:The optimisation of material handling systems (MHSs) can lead to substantial cost reductions in manufacturing systems. Choosing adequate and relevant performance measures is critical in accurately evaluating MHSs. The majority of performance measures used in MHSs are time-based. However, moving materials within a manufacturing system utilise time and cost. In this study, we consider both time and cost measures in an optimisation model used to evaluate an MHS with automated guided vehicles. We take into account the reliability of the MHSs because of the need for steadiness and stability in the automated manufacturing systems. Reliability is included in the model as a cost function. Furthermore, we consider bi-objective stochastic programming to optimise the time and cost objectives because of the uncertainties inherent in the optimisation parameters in real-world problems. We use perceptron neural networks to transform the bi-objective optimisation model into a single objective model. We use numerical experiments to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2014.887232