Ant Colony Optimization Algorithms for Scheduling the Mixed Model Assembly Lines

• Published in: Advances in Natural Computation pp. 911 - 914
• Main Authors: Sun, Xin-yu, Sun, Lin-yan
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture 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
• ISBN: 9783540283201; 354028320X; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539902_112

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.