The use of multi-fidelity simulation optimisation for real-time management of a manufacturing line

Abstract

Manufacturers often rely on simulation models to support decision-making for a wide range of tasks from system design to routine operations. There is scope for simulation modelling to extend further to deal with situations that look for fast responses, ideally in real time. Simulation models for manufacturing systems are usually complex and large, meaning they take a long time to run. They are not able to support real-time decision-making when working with conventional simulation optimisation procedures which require multiple replications for producing the recommendations. A multi-fidelity simulation optimisation framework is designed to address this problem. It includes a low-fidelity metamodel to guide the search using the high-fidelity simulation model, which aims to reduce the replications of the high-fidelity model needed in the optimisation. The proposed method has been tested on a well-known simulation model of an inventory system and a new model of a production line. The results from both the textbook example and the manufacturing system show that it satisfies the need for real-time optimisation and performs well. In the production line case, we aim to optimise the repair policy when multiple machines break down simultaneously. The order to repair the failed machines could affect how the system would recover and would lead to different short-term system throughput. By optimising the repair policy, the system throughput is optimised. In order to carry out the optimisation in real time, a ''hot-start'' simulation model is structured for the production line system. The state space of the metamodel for the production line is modified and reduced in order to fit the metamodel with fewer data. An adaptive sequential sampling method is developed to efficiently sample from the stochastic simulation model.

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Identifiers

ORCID for Yiyun Cao: orcid.org/0009-0002-3687-4343

ORCID for Christine Currie: orcid.org/0000-0002-7016-3652

ORCID for Stephan Onggo: orcid.org/0000-0001-5899-304X

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