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Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

Cakar T, Koker R - Comput Intell Neurosci (2015)

Bottom Line: PSO searches for better solution than this solution.For each stage, local optimizers are used to perform exploitation to the best particle.All system is named as neurohybrid-PSO solution system.

View Article: PubMed Central - PubMed

Affiliation: Industrial Engineering Department, Engineering Faculty, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey.

ABSTRACT
A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

No MeSH data available.


Comparison of PSO, GA, and SA for a SMTWT problem.
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fig7: Comparison of PSO, GA, and SA for a SMTWT problem.

Mentions: The solution values given by PSO and NHPSO for 100 generations have been presented in the graphical representation on Figure 6. It is evident that the proposed NHPSO is working better, reaching the solution quicker and giving better solution in a certain generation. These features are given to the NHPSO by interactive working SA, GA, and NDR applying these to the final solution. Comparison of PSO, GA, and SA can be seen in Figure 7.


Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

Cakar T, Koker R - Comput Intell Neurosci (2015)

Comparison of PSO, GA, and SA for a SMTWT problem.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493311&req=5

fig7: Comparison of PSO, GA, and SA for a SMTWT problem.
Mentions: The solution values given by PSO and NHPSO for 100 generations have been presented in the graphical representation on Figure 6. It is evident that the proposed NHPSO is working better, reaching the solution quicker and giving better solution in a certain generation. These features are given to the NHPSO by interactive working SA, GA, and NDR applying these to the final solution. Comparison of PSO, GA, and SA can be seen in Figure 7.

Bottom Line: PSO searches for better solution than this solution.For each stage, local optimizers are used to perform exploitation to the best particle.All system is named as neurohybrid-PSO solution system.

View Article: PubMed Central - PubMed

Affiliation: Industrial Engineering Department, Engineering Faculty, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey.

ABSTRACT
A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

No MeSH data available.