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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.


The structure of the proposed neurohybrid solution system based on PSO.
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fig3: The structure of the proposed neurohybrid solution system based on PSO.

Mentions: In this system PSO system works primarily and later it tries to improve the obtained solution by using GA and SA together as a hybrid system to find better solution. GA and SA work interactively. SA gets the best solution found by GA, and it improves this solution and sends the obtained better one to GA again. When working of GA finishes, obtaining the best solution by subhybrid solution is transferred to the initial population of PSO algorithm to search for better solution. The best solution found by PSO (hybrid-PSO solution) is sent to GA again, and GA and SA work interactively to search for a better solution. This loop stops if the obtained solution is fitting with predefined stopping criterion. As a result of working of these three search algorithms interactively, since each algorithm has different search mechanisms, faster and better solutions may be found. Then, neurodominance rule (NDR) is applied to obtained final solution and based on total weighted tardiness criterion, the violating orders are corrected, and the solution gets more excellent. The overall system is named as neurohybrid-PSO. The general working principle of the proposed solution system has been shown in Figure 3; on the other hand, detailed working system has been presented in Figures 4 and 5.


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)

The structure of the proposed neurohybrid solution system based on PSO.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: The structure of the proposed neurohybrid solution system based on PSO.
Mentions: In this system PSO system works primarily and later it tries to improve the obtained solution by using GA and SA together as a hybrid system to find better solution. GA and SA work interactively. SA gets the best solution found by GA, and it improves this solution and sends the obtained better one to GA again. When working of GA finishes, obtaining the best solution by subhybrid solution is transferred to the initial population of PSO algorithm to search for better solution. The best solution found by PSO (hybrid-PSO solution) is sent to GA again, and GA and SA work interactively to search for a better solution. This loop stops if the obtained solution is fitting with predefined stopping criterion. As a result of working of these three search algorithms interactively, since each algorithm has different search mechanisms, faster and better solutions may be found. Then, neurodominance rule (NDR) is applied to obtained final solution and based on total weighted tardiness criterion, the violating orders are corrected, and the solution gets more excellent. The overall system is named as neurohybrid-PSO. The general working principle of the proposed solution system has been shown in Figure 3; on the other hand, detailed working system has been presented in Figures 4 and 5.

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.