scholarly journals A Hybrid Genetic Algorithm and Variable Neighborhood Search for Task Scheduling Problem in Grid Environment

2012 ◽  
Vol 29 ◽  
pp. 3808-3814 ◽  
Author(s):  
Sara Kardani-Moghaddam ◽  
Farzad Khodadadi ◽  
Reza Entezari-Maleki ◽  
Ali Movaghar
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Variable Neighborhood Search ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Grid Environment

scholarly journals A Hybrid Genetic Algorithm for Satellite Image Downlink Scheduling Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bingyu Song ◽  
Feng Yao ◽  
Yuning Chen ◽  
Yingguo Chen ◽  
Yingwu Chen
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Satellite Image ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Satellite Mission ◽  
Downlink Scheduling ◽  
The Given

The satellite image downlink scheduling problem (SIDSP) is included in satellite mission planning as an important part. A customer demand is finished only if the corresponding images are eventually downloaded. Due to the growing customer demands and the limited ground resources, SIDSP is an oversubscribed scheduling problem. In this paper, we investigate SIDSP with the case study of China’s commercial remote sensing satellite constellation (SuperView-1) and exploit the serial scheduling scheme for solving it. The idea is first determining a permutation of the downlink requests and then producing a schedule from the given ordered requests. A schedule generation algorithm (SGA) is proposed to assign the downlink time window for each scheduled request according to a given request permutation. A hybrid genetic algorithm (HGA) combined with neighborhood search is proposed to optimize the downlink request permutation with the purpose of maximizing the utility function. Experimental results on six groups of instances with different density demonstrate the effectiveness of the proposed approach.

scholarly journals Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

2019 ◽  
Vol 9 (19) ◽  
pp. 4005 ◽  
Author(s):  
Geunho Yang ◽  
Byung Do Chung ◽  
Sang Jin Lee
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Space ◽  
Product Structure ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Resource Constrained

This study addresses the dual resource constrained flexible job shop scheduling problem (DRCFJSP) with a multilevel product structure. The DRCFJSP is a strong NP-hard problem, and an efficient algorithm is essential for DRCFJSP. In this study, we propose an algorithm for the DRCFJSP with a multilevel product structure to minimize the lateness, makespan, and deviation of the workload with preemptive priorities. To efficiently solve the problem within a limited time, the search space is limited based on the possible start and end time, and focus is placed on the intensification rather than diversification, which can help the algorithm spend more time to find an optimal solution in a reasonable solution space. The performance of the proposed algorithm is compared with those of a genetic algorithm and a hybrid genetic algorithm with variable neighborhood search. The numerical experiments demonstrate that the strategy limiting the search space is effective for large and complex problems.

scholarly journals Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880409 ◽  
Author(s):  
Rui Wu ◽  
Yibing Li ◽  
Shunsheng Guo ◽  
Wenxiang Xu
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

scholarly journals Correction to: Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

2021 ◽  
Author(s):  
Poria Pirozmand ◽  
Ali Asghar Rahmani Hosseinabadi ◽  
Maedeh Farrokhzad ◽  
Mehdi Sadeghilalimi ◽  
Seyedsaeid Mirkamali ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Multi Objective
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