Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling

Rakkiannan, Thamilselvan; Palanisamy, Balasubramanie

doi:10.3844/ajassp.2012.1694.1705

Cited by 9 publications

(1 citation statement)

References 17 publications

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“…Therefore, the proposed algorithm can be applied to most of the known NP-hard assignment optimization models within most extensive enterprise [26]. In future work, we will integrate the parallel computing [27] - [28] and develop a new genetic operator of this hybrid algorithm. As another work, we will also introduce a hybrid model A for productivity prediction of enterprise from the dataset issue of Big Data integrating also the Multi-Objective Optimization process [30]-[31] .…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

Tkatek.¹

2020

IJATCSE

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This paper discusses the implementation of a new hybrid algorithm to efficacy solve a constrained personnel reassignment to preferred posts by generating a high quality and optimal solution. This optimization of posts can be occupied by a qualified employee during a job rotation or redeployment (reassignment) staff operation. This operation is organized by a decision-maker to adapt each post in priority to qualified employees for improving the productivity of enterprise. Generally, this problem is a NP hard problem that has been modeled into a combinatorial optimization problem. Next we have developed a hybrid graph Flow and genetic algorithm called HFGA, to solve it [1]. However, we have noticed that the stagnation phenomenon of this HFGA algorithm is usually persisted producing an increase in the number of iterations and consequently also a significant consummation of CPU time. In order to remedy this problem and improving in parallel the optimal solution and convergence rate, we propose as our first contribution a hybrid genetic HFGA and Adaptive Genetic Immigration AIG operator [2] called HFGA-AIG. In our second contribution, we propose another hybridization of HFGA-AIG with a Simulated Annealing SA sequentially called Hybrid Genetic Simulated Annealing Algorithms HFGA-SA-AIG. Our aim is to obtain an improved solution quality of this NP Hard problem. This proposed algorithm can be adapted to a personnel assignment optimization model and allows us also to ensure an excellent allocation of resources for maximizing the productivity in a multisite entreprise. The obtained numerical results indicate that the HFGA-SA-AIG algorithm outperforms both HFGA-AIG and HFGA. In terms of solution quality and remedying of the stagnation phenomenon.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

Tkatek.¹

2020

IJATCSE

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Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem

et al. 2019

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This paper presents a hybrid genetic algorithm with collective communication (HGACC) using distributed processing for the job shop scheduling problem. The genetic algorithm starts with a set of elite micro-populations created randomly, where the fitness of these individuals does not exceed a tuned upper bound in the makespan value. The computational processes distribute the micro-populations collectively. In the micro-populations, each individual's search for good solutions is directed toward the solution space of the fittest individual, identified by an approximation of genetic traits. In each generation of the genetic algorithm, the best individual from each micro-population migrates to another micro-population to maintain diversity in populations. Changes in the genetic sequence are applied to each individual by the simulated annealing algorithm (iterative mutation). In this paper, the results obtained show that the genetic algorithm achieves excellent results, as compared to other genetic algorithms. It is also better than other non-genetic meta heuristics or competes with them.

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Small launch vehicle optimal design configuration from ballistic missile components

Villanueva¹,

Hashemi

2015

2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling

Cited by 9 publications

References 17 publications

A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem

Small launch vehicle optimal design configuration from ballistic missile components

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