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 A Heuristic Genetic Algorithm for Regional Targets’ Small Satellite Image Downlink Scheduling Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yan-Jie Song ◽  
Zhong-Shan Zhang ◽  
Kai Sun ◽  
Feng Yao ◽  
Ying-Wu Chen
Keyword(s):  
Genetic Algorithm ◽  
Satellite Image ◽  
Schedule Algorithm ◽  
Scheduling Problem ◽  
Small Satellite ◽  
Task Sequence ◽  
Satellite Mission ◽  
Downlink Scheduling ◽  
Ground Station ◽  
Scale Test

Small satellite image downlink scheduling problem (SSIDSP) is an important part of satellite mission planning. SSIDSP mainly needs to balance how to better match the limited receiving capacity of the ground station with the limited satellite resources. In this paper, regional targets are considered with SSIDSP. We propose a mathematical model that maximizes profit by considering time value and regional targets. A downlink schedule algorithm (DSA) is proposed to complete the task sequence arrangement and generate scheduling results. A heuristic genetic algorithm (HGA) is used to optimize the generated task sequence to achieve higher profit. Three scale test instances are used to test the effectiveness of HGA and DSA. We compare the effect of HGA, basic genetic algorithm (GA), and construction heuristic algorithm. The experimental results proved that the proposed approach ensures the successful completion of observation tasks and is effective for SSIDSP.

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 A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Atefeh Amindoust ◽  
Milad Asadpour ◽  
Samineh Shirmohammadi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Genetic Algorithm ◽  
Real Case ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Proposed Model ◽  
The World ◽  
New Situation

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.

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