Reactive scheduling of job processing against machine breakdowns for non-disruptive cell manufacturing systems

Wan Ling Li, Muhammad Hafidz Fazli Muhammad Hafidz, Tomohiro Murata

研究成果: Article

抄録

This paper proposes a novel method of reactive scheduling of job processing for non-disruptive cell manufacturing systems (CMS) against unexpected machine breakdown occurrence. Reactive scheduling problem for reassigning pending jobs in CMS is formulated as a discrete optimization problem based on Integer Programming (IP) and effective solving method of BPSO-SA which consists of hybridized Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) methods is proposed to cope with time-critical recovery situation. BPSO is adopted to explore near optimal feasible solution of the discrete rescheduling problem and SA is used for the global optimization of locating a good approximation to the global optimum in a large search space. Numerical experiment demonstrates the effectiveness of the proposed model through case problems of non-disruptive jobs processing in CMS with unexpected machine breakdowns to seek for multiple objectives to minimize tardiness, number of intercellular movements, as well as maximizing cell load balancing. The results show that BPSO-SA provides a near optimal solution with significant reduction of calculation time compared to IP-based mathematical optimization method.

元の言語English
ページ(範囲)730-739
ページ数10
ジャーナルIEEJ Transactions on Electronics, Information and Systems
133
発行部数4
DOI
出版物ステータスPublished - 2013

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Simulated annealing
Particle swarm optimization (PSO)
Scheduling
Integer programming
Processing
Global optimization
Resource allocation
Recovery
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

これを引用

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title = "Reactive scheduling of job processing against machine breakdowns for non-disruptive cell manufacturing systems",
abstract = "This paper proposes a novel method of reactive scheduling of job processing for non-disruptive cell manufacturing systems (CMS) against unexpected machine breakdown occurrence. Reactive scheduling problem for reassigning pending jobs in CMS is formulated as a discrete optimization problem based on Integer Programming (IP) and effective solving method of BPSO-SA which consists of hybridized Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) methods is proposed to cope with time-critical recovery situation. BPSO is adopted to explore near optimal feasible solution of the discrete rescheduling problem and SA is used for the global optimization of locating a good approximation to the global optimum in a large search space. Numerical experiment demonstrates the effectiveness of the proposed model through case problems of non-disruptive jobs processing in CMS with unexpected machine breakdowns to seek for multiple objectives to minimize tardiness, number of intercellular movements, as well as maximizing cell load balancing. The results show that BPSO-SA provides a near optimal solution with significant reduction of calculation time compared to IP-based mathematical optimization method.",
keywords = "Cellular manufacturing, Machine breakdown, Particle swarm optimization, Reactive scheduling, Simulated annealing",
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AU - Muhammad Hafidz, Muhammad Hafidz Fazli

AU - Murata, Tomohiro

PY - 2013

Y1 - 2013

N2 - This paper proposes a novel method of reactive scheduling of job processing for non-disruptive cell manufacturing systems (CMS) against unexpected machine breakdown occurrence. Reactive scheduling problem for reassigning pending jobs in CMS is formulated as a discrete optimization problem based on Integer Programming (IP) and effective solving method of BPSO-SA which consists of hybridized Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) methods is proposed to cope with time-critical recovery situation. BPSO is adopted to explore near optimal feasible solution of the discrete rescheduling problem and SA is used for the global optimization of locating a good approximation to the global optimum in a large search space. Numerical experiment demonstrates the effectiveness of the proposed model through case problems of non-disruptive jobs processing in CMS with unexpected machine breakdowns to seek for multiple objectives to minimize tardiness, number of intercellular movements, as well as maximizing cell load balancing. The results show that BPSO-SA provides a near optimal solution with significant reduction of calculation time compared to IP-based mathematical optimization method.

AB - This paper proposes a novel method of reactive scheduling of job processing for non-disruptive cell manufacturing systems (CMS) against unexpected machine breakdown occurrence. Reactive scheduling problem for reassigning pending jobs in CMS is formulated as a discrete optimization problem based on Integer Programming (IP) and effective solving method of BPSO-SA which consists of hybridized Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) methods is proposed to cope with time-critical recovery situation. BPSO is adopted to explore near optimal feasible solution of the discrete rescheduling problem and SA is used for the global optimization of locating a good approximation to the global optimum in a large search space. Numerical experiment demonstrates the effectiveness of the proposed model through case problems of non-disruptive jobs processing in CMS with unexpected machine breakdowns to seek for multiple objectives to minimize tardiness, number of intercellular movements, as well as maximizing cell load balancing. The results show that BPSO-SA provides a near optimal solution with significant reduction of calculation time compared to IP-based mathematical optimization method.

KW - Cellular manufacturing

KW - Machine breakdown

KW - Particle swarm optimization

KW - Reactive scheduling

KW - Simulated annealing

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