Ant Colony Optimization Algorithms for Scheduling the Mixed Model Assembly Lines

Solving the mixed-model scheduling problem is the most important goal for Just-in-time production systems. But it is a difficult combinatorial optimization problem. This study presents a novel co-operative agents approach, Ant Colony Optimization algorithm (ACO) scheme, for solving the scheduling mi...

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Published inAdvances in Natural Computation pp. 911 - 914
Main Authors Sun, Xin-yu, Sun, Lin-yan
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Assembly Line
Computer science; control theory; systems
Exact sciences and technology
Generation Schedule Strategy
Satisfy Solution
Satisfy Tour
Simulated Annealing Algorithm
Production system
Assembly line
Combinatorial problem
Insecta
Scheduling
Social insect
Combinatorial optimization
Formicoidea
Genetic algorithm
Just in time
Arthropoda
Cooperation
Mixed model
Simulated annealing
Swarm intelligence
Hymenoptera
Invertebrata
Aculeata
Mathematical programming
Online AccessGet full text
ISBN9783540283201
354028320X
3540283234
9783540283232
ISSN0302-9743
1611-3349
DOI10.1007/11539902_112

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Abstract Solving the mixed-model scheduling problem is the most important goal for Just-in-time production systems. But it is a difficult combinatorial optimization problem. This study presents a novel co-operative agents approach, Ant Colony Optimization algorithm (ACO) scheme, for solving the scheduling mixed-model assembly lines. The results show that the solution which ant algorithm produces is better than the one which Toyota’s goal chasing algorithm, simulated annealing algorithm and genetic algorithm produce. Finally, this example may extend to a bigger scale, and the satisfied solutions, benchmark results and CPU time to generate a satisfied tour are given.
AbstractList Solving the mixed-model scheduling problem is the most important goal for Just-in-time production systems. But it is a difficult combinatorial optimization problem. This study presents a novel co-operative agents approach, Ant Colony Optimization algorithm (ACO) scheme, for solving the scheduling mixed-model assembly lines. The results show that the solution which ant algorithm produces is better than the one which Toyota’s goal chasing algorithm, simulated annealing algorithm and genetic algorithm produce. Finally, this example may extend to a bigger scale, and the satisfied solutions, benchmark results and CPU time to generate a satisfied tour are given.
Author Sun, Xin-yu
Sun, Lin-yan
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  givenname: Lin-yan
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  organization: School of management, the State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, P. R. China
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Ong, Yew Soon
Chen, Ke
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Keywords Production system
Assembly line
Combinatorial problem
Insecta
Scheduling
Social insect
Combinatorial optimization
Formicoidea
Genetic algorithm
Just in time
Arthropoda
Cooperation
Mixed model
Simulated annealing
Swarm intelligence
Hymenoptera
Invertebrata
Aculeata
Mathematical programming
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Snippet Solving the mixed-model scheduling problem is the most important goal for Just-in-time production systems. But it is a difficult combinatorial optimization...
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SubjectTerms Applied sciences
Artificial intelligence
Assembly Line
Computer science; control theory; systems
Exact sciences and technology
Generation Schedule Strategy
Satisfy Solution
Satisfy Tour
Simulated Annealing Algorithm
Title Ant Colony Optimization Algorithms for Scheduling the Mixed Model Assembly Lines
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