Value-Based Manufacturing Optimisation in Serverless Clouds for Industry 4.0

Research output: Chapter in Book/Report/Conference proceedingConference contribution


There is increasing impetus towards Industry 4.0, a recently proposed roadmap for process automation across a broad spectrum of manufacturing industries. The proposed approach uses Evolutionary Computation to optimise real-world metrics. Features of the proposed approach are that it is generic (i.e. applicable across multiple problem domains) and decentralised, i.e. hosted remotely from the physical system upon which it operates. In particular, by virtue of being serverless, the project goal is that computation can be performed `just in time' in a scalable fashion. We describe a case study for value-based optimisation, applicable to a wide range of manufacturing processes. In particular, value is expressed in terms of Overall Equipment Effectiveness (OEE), grounded in monetary units. We propose a novel online stopping condition that takes into account the predicted utility of further computational effort. We apply this method to scheduling problems in the (max,+) algebra, and compare against a baseline stopping criterion with no prediction mechanism. Near optimal profit is obtained by the proposed approach, across multiple problem instances.
Original languageEnglish
Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference
Publication statusAccepted/In press - 24 Mar 2018
EventThe Genetic and Evolutionary Computation Conference - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018


ConferenceThe Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO
Internet address


  • Plant optimisation
  • Genetic Algorithm
  • Value Curve
  • Stopping Condition
  • Function as a Service
  • FaaS
  • Serverless Clouds

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