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

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

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 language	English
Title of host publication	The Genetic and Evolutionary Computation Conference
Publication status	Accepted/In press - 21 Mar 2019
Event	The Genetic and Evolutionary Computation Conference - Prague, Czech Republic Duration: 13 Jul 2019 → 17 Jul 2019 https://gecco-2019.sigevo.org

Conference

Conference	The Genetic and Evolutionary Computation Conference
Abbreviated title	GECCO
Country/Territory	Czech Republic
City	Prague
Period	13/07/19 → 17/07/19
Internet address	https://gecco-2019.sigevo.org

Keywords

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

Access to Document

GECCO19___Distributed_evolutionary_optimisation_in_clouds_PureAccepted author manuscript, 737 KB

Cite this

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title = "Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories",

abstract = "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.",

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author = "Shuai Zhao and Piotr Dziurzanski and Michal Przewozniczek and Marcin Komarnicki and {Soares Indrusiak}, Leandro",

year = "2019",

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day = "21",

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TY - CHAP

T1 - Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories

AU - Zhao, Shuai

AU - Dziurzanski, Piotr

AU - Przewozniczek, Michal

AU - Komarnicki, Marcin

AU - Soares Indrusiak, Leandro

PY - 2019/3/21

Y1 - 2019/3/21

N2 - 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.

AB - 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.

KW - Integrated process planning and scheduling

KW - Multi-objective Genetic Algorithm

KW - Function as a Service

KW - Serverless Clouds

M3 - Chapter

BT - The Genetic and Evolutionary Computation Conference

T2 - The Genetic and Evolutionary Computation Conference

Y2 - 13 July 2019 through 17 July 2019

ER -