Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions

Mohanad AL-Behadili; Djamila Ouelhadj; Dylan Jones

Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions

Mohanad AL-Behadili, Djamila Ouelhadj, Dylan Jones

University of Basrah

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

Abstract

This paper considers the permutation flow shop scheduling problem (PFSP) under stochastic processing time and in the presence of different types of real-time events. A multi-objective optimisation model and a novel predictive-reactive approach based Simulation-Particle Swarm Optimisation algorithm is designed and adapted for this problem. This algorithm hybridised the Monte-Carol Simulation (MCS) technique with the Particle Swarm Optimaisation algorithm to deal with the the stochastic behavior of the problem. Also, a deterministic version of the benchmark set proposed by [1] is adapted and used to test the aforementioned problem and solution method. Furthermore, the survival analysis based on the Kaplan-Meier estimator is used to analyse the behaviour of stochastic and dynamic solutions.

Original language	English
Title of host publication	Proceedings of the World Congress on Engineering 2019, WCE 2019
Editors	S. I. Ao, Len Gelman, David WL Hukins, Andrew Hunter, A. M. Korsunsky
Publisher	Newswood Limited
Pages	27-31
Number of pages	5
ISBN (Electronic)	9789881404862
Publication status	Published - 3 Jul 2019
Event	2019 World Congress on Engineering - London, United Kingdom Duration: 3 Jul 2019 → 5 Jul 2019

Publication series

Name	Lecture Notes in Engineering and Computer Science
Volume	2240
ISSN (Print)	2078-0958
ISSN (Electronic)	2078-0966

Conference

Conference	2019 World Congress on Engineering
Abbreviated title	WCE 2019
Country/Territory	United Kingdom
City	London
Period	3/07/19 → 5/07/19

Keywords

Multi-objective optimisation model
Permutation flow shop scheduling
Predictive-reactive approach
Simulation-Particle Swarm Optimisation algorithm

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http://www.iaeng.org/publication/WCE2019/

Cite this

AL-Behadili, M., Ouelhadj, D., & Jones, D. (2019). Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions. In S. I. Ao, L. Gelman, D. WL. Hukins, A. Hunter, & A. M. Korsunsky (Eds.), Proceedings of the World Congress on Engineering 2019, WCE 2019 (pp. 27-31). (Lecture Notes in Engineering and Computer Science; Vol. 2240). Newswood Limited. http://www.iaeng.org/publication/WCE2019/

AL-Behadili, Mohanad ; Ouelhadj, Djamila ; Jones, Dylan. / Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions. Proceedings of the World Congress on Engineering 2019, WCE 2019. editor / S. I. Ao ; Len Gelman ; David WL Hukins ; Andrew Hunter ; A. M. Korsunsky. Newswood Limited, 2019. pp. 27-31 (Lecture Notes in Engineering and Computer Science).

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title = "Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions",

abstract = "This paper considers the permutation flow shop scheduling problem (PFSP) under stochastic processing time and in the presence of different types of real-time events. A multi-objective optimisation model and a novel predictive-reactive approach based Simulation-Particle Swarm Optimisation algorithm is designed and adapted for this problem. This algorithm hybridised the Monte-Carol Simulation (MCS) technique with the Particle Swarm Optimaisation algorithm to deal with the the stochastic behavior of the problem. Also, a deterministic version of the benchmark set proposed by [1] is adapted and used to test the aforementioned problem and solution method. Furthermore, the survival analysis based on the Kaplan-Meier estimator is used to analyse the behaviour of stochastic and dynamic solutions.",

keywords = "Multi-objective optimisation model, Permutation flow shop scheduling, Predictive-reactive approach, Simulation-Particle Swarm Optimisation algorithm",

author = "Mohanad AL-Behadili and Djamila Ouelhadj and Dylan Jones",

year = "2019",

month = jul,

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language = "English",

series = "Lecture Notes in Engineering and Computer Science",

publisher = "Newswood Limited",

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booktitle = "Proceedings of the World Congress on Engineering 2019, WCE 2019",

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AL-Behadili, M , Ouelhadj, D & Jones, D 2019, Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions. in SI Ao, L Gelman, DWL Hukins, A Hunter & AM Korsunsky (eds), Proceedings of the World Congress on Engineering 2019, WCE 2019. Lecture Notes in Engineering and Computer Science, vol. 2240, Newswood Limited, pp. 27-31, 2019 World Congress on Engineering, London, United Kingdom, 3/07/19. <http://www.iaeng.org/publication/WCE2019/>

Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions. / AL-Behadili, Mohanad ; Ouelhadj, Djamila ; Jones, Dylan.
Proceedings of the World Congress on Engineering 2019, WCE 2019. ed. / S. I. Ao; Len Gelman; David WL Hukins; Andrew Hunter; A. M. Korsunsky. Newswood Limited, 2019. p. 27-31 (Lecture Notes in Engineering and Computer Science; Vol. 2240).

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

TY - GEN

T1 - Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions

AU - AL-Behadili, Mohanad

AU - Ouelhadj, Djamila

AU - Jones, Dylan

PY - 2019/7/3

Y1 - 2019/7/3

N2 - This paper considers the permutation flow shop scheduling problem (PFSP) under stochastic processing time and in the presence of different types of real-time events. A multi-objective optimisation model and a novel predictive-reactive approach based Simulation-Particle Swarm Optimisation algorithm is designed and adapted for this problem. This algorithm hybridised the Monte-Carol Simulation (MCS) technique with the Particle Swarm Optimaisation algorithm to deal with the the stochastic behavior of the problem. Also, a deterministic version of the benchmark set proposed by [1] is adapted and used to test the aforementioned problem and solution method. Furthermore, the survival analysis based on the Kaplan-Meier estimator is used to analyse the behaviour of stochastic and dynamic solutions.

AB - This paper considers the permutation flow shop scheduling problem (PFSP) under stochastic processing time and in the presence of different types of real-time events. A multi-objective optimisation model and a novel predictive-reactive approach based Simulation-Particle Swarm Optimisation algorithm is designed and adapted for this problem. This algorithm hybridised the Monte-Carol Simulation (MCS) technique with the Particle Swarm Optimaisation algorithm to deal with the the stochastic behavior of the problem. Also, a deterministic version of the benchmark set proposed by [1] is adapted and used to test the aforementioned problem and solution method. Furthermore, the survival analysis based on the Kaplan-Meier estimator is used to analyse the behaviour of stochastic and dynamic solutions.

KW - Multi-objective optimisation model

KW - Permutation flow shop scheduling

KW - Predictive-reactive approach

KW - Simulation-Particle Swarm Optimisation algorithm

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M3 - Conference contribution

AN - SCOPUS:85125557396

T3 - Lecture Notes in Engineering and Computer Science

SP - 27

EP - 31

BT - Proceedings of the World Congress on Engineering 2019, WCE 2019

A2 - Ao, S. I.

A2 - Gelman, Len

A2 - Hukins, David WL

A2 - Hunter, Andrew

A2 - Korsunsky, A. M.

PB - Newswood Limited

T2 - 2019 World Congress on Engineering

Y2 - 3 July 2019 through 5 July 2019

ER -

AL-Behadili M , Ouelhadj D , Jones D. Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions. In Ao SI, Gelman L, Hukins DWL, Hunter A, Korsunsky AM, editors, Proceedings of the World Congress on Engineering 2019, WCE 2019. Newswood Limited. 2019. p. 27-31. (Lecture Notes in Engineering and Computer Science).

Simulation particle swarm optimisation for stochastic permutation flow shop scheduling problem under different disruptions

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