Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

Tangpattanakul, Panwadee; Jozefowiez, Nicolas; López, Pierre

doi:10.1007/978-3-319-12631-9_9

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…Compared with standard GA methods, BRKGA offers more flexibility in encoding solutions [25] and produces as good or better solutions [26]. BRKGA has shown competitive performance on a series of optimization problems [27], including the satellite scheduling problem [28], which is very similar to the problem studied in this article. The ALNS algorithm was first proposed by Pisinger and Ropke [29].…”

Section: Related Workmentioning

confidence: 97%

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Guijt

Weerdt

et al. 2019

Computers & Industrial Engineering

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The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling.This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. A novel memetic algorithm for this problem, called Sparrow, comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to stateof-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.Adaptive large neighbourhood search, Order acceptance and scheduling, Sequence-dependent setup times 2. We compare Sparrow with state-of-the-art algorithms on a set of standard benchmark instances from the literature. The proposed algorithm obtains better-quality solutions with comparable running time.3. We study the correlation of the problem properties and the algorithm performance and find that the congestion ratio, the length of time windows, and the correlation of processing time and revenue of orders are highly related to the difficulty of the problem. 2 4. We further generate new instances that are more representative of real problem instances in satellite scheduling (more congestion), commerce (high correlation between revenue and processing time), and the travelling repairman problem (short processing times and long time windows), and compare the performance of multiple state-of-the-art algorithms on these new instances. The remainder of this article is summarized as follows: Section 2 provides 1 This algorithm combines a population-based genetic algorithm with adaptive large neighbourhood search. Each individual in a "swarm" thus has a bird's eye view of the search space -hence Sparrow. 2 These terms are defined in Section 4.3. Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max 0.10 0.10

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Section: Related Workmentioning

confidence: 97%

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Guijt

Weerdt

et al. 2019

Computers & Industrial Engineering

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“…Studies [ 36 , 37 , 38 ] presented a tabu search heuristic algorithm to select a subset of requests to maximize profit. Studies [ 39 , 40 ] used the biased random key genetic and local search heuristic algorithm to solve a multi-user observation scheduling problem. Study [ 41 ] considered many technical and managerial constraints and developed a constructive algorithm that produced a feasible plan in a very short time.…”

Section: Introductionmentioning

confidence: 99%

A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area

Zheng

Yue

Liu

et al. 2023

Sensors

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This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit. The SMA is solved by means of a new method composed of three successive phases, namely, grid space construction, candidate strip generation and strip selection. First, the grid space construction is proposed to discretize the irregular area into a set of points in a specific plane rectangular coordinate system and calculate the total profit of a solution of SMA. Then, the candidate strip generation is designed to produce numerous candidate strips based on the grid space of the first phase. At last, in the strip selection, the optimal schedule for all the SAR satellites is developed based on the result of the candidate strip generation. In addition, this paper proposes a normalized grid space construction algorithm, a candidate strip generation algorithm and a tabu search algorithm with variable neighborhoods for the three successive phases, respectively. To verify the effectiveness of the proposed method in this paper, we perform simulation experiments on several scenarios and compare our method with the other seven methods. Compared to the best of the other seven methods, our proposed method can improve profit by 6.38% using the same resources.

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“…There are also several profit-based construction heuristics [3,9] and the iterative local search [10]. Many meta-heuristics include the tabu search algorithm [11,12], the hybrid differential evolution algorithm [13], the improved genetic algorithm [14][15][16][17], and the adaptive large neighbourhood search algorithm [5,18]. However, the search difficulty and solution time of these algorithms increase dramatically as the problem scale increases.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem

Chun,

Yang,

Liu

et al. 2023

Mathematics

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The agile earth observation satellite scheduling problem (AEOSSP) is a combinatorial optimization problem with time-dependent constraints. Recently, many construction heuristics and meta-heuristics have been proposed; however, existing methods cannot balance the requirements of efficiency and timeliness. In this paper, we propose a graph attention network-based decision neural network (GDNN) to solve the AEOSSP. Specifically, we first represent the task and time-dependent attitude transition constraints by a graph. We then describe the problem as a Markov decision process and perform feature engineering. On this basis, we design a GDNN to guide the construction of the solution sequence and train it with proximal policy optimization (PPO). Experimental results show that the proposed method outperforms construction heuristics at scheduling profit by at least 45%. The proposed method can also calculate the approximate profits of the state-of-the-art method with an error of less than 7% and reduce scheduling time markedly. Finally, we demonstrate the scalability of the proposed method.

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Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

Cited by 12 publications

References 11 publications

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area

Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem

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