Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories

Shuai Zhao, Piotr Dziurzanski, Michal Przewozniczek, Marcin Komarnicki, Leandro Soares Indrusiak

Research output: Chapter in Book/Report/Conference proceedingChapter


In smart factories, process planning and scheduling need to be performed every time a new manufacturing order is received or a factory state change has been detected. A new plan and schedule need to be determined quickly to increase the responsiveness of the factory and enlarge its profit. Simultaneous optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation of process planning and scheduling is presented. It executes a multi-objective genetic algorithm on multiple subpopulations (islands). The number of islands is automatically decided based on the current optimisation state. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.
Original languageEnglish
Title of host publicationThe Genetic and Evolutionary Computation Conference
Publication statusAccepted/In press - 21 Mar 2019
EventThe Genetic and Evolutionary Computation Conference - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019


ConferenceThe Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO
Country/TerritoryCzech Republic
Internet address


  • Integrated process planning and scheduling
  • Multi-objective Genetic Algorithm
  • Function as a Service
  • Serverless Clouds

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