Parallel robot scheduling to minimize mean tardiness with precedence constraints using a genetic algorithm

Canay

2015

Neural Comput & Applic

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Section: Simulated Annealingmentioning

confidence: 99%

A new neuro-dominance rule for single-machine tardiness problem with double due date

Canay

2015

Neural Comput & Applic

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“…The role of the temperature is to control the possibility of the acceptance of the disturbing solution. On the other hand, the reason of the usage of the iteration number is to decide the number of repetitions until a solution is found on a stable state under the temperature [ 32 , 33 ]. The temperature may get the following implicit flexibility index meaning.…”

Section: Simulated Annealingmentioning

confidence: 99%

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

Computational Intelligence and Neuroscience

2015

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.

“…Then they improved this solution using the permutation technique of the WI method, which was developed previously by Wilkerson and Irwin (1971) [42]. Cakar et al (2008) proposed a genetic algorithm approach to solve parallel robot scheduling with precedence constraints [43]. Kanjo and Ase (2003) studied scheduling in a multi robot welding system [44].…”

Section: Introductionmentioning

confidence: 99%

Parallel Robot Scheduling to Minimize Mean Tardiness with Unequal Release Date and Precedence Constraints Using a Hybrid Intelligent System

International Journal of Advanced Robotic Systems

Sari

2012

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This paper considers the problem of scheduling a given number of jobs on a specified number of identical parallel robots with unequal release dates and precedence constraints in order to minimize mean tardiness. This problem is strongly NP-hard. The author proposes a hybrid intelligent solution system, which uses Genetic Algorithms and Simulated Annealing (GA+SA). A genetic algorithm, as is well known, is an efficient tool for the solution of combinatorial optimization problems. Solutions for problems of different scales are found using genetic algorithms, simulated annealing and a Hybrid Intelligent Solution System (HISS). Computational results of empirical experiments show that the Hybrid Intelligent Solution System (HISS) is successful with regards to solution quality and computational time.