An improved cuckoo search algorithm for scheduling jobs on identical parallel machines

Laha, Dipak; Gupta, Jatinder N. D.

doi:10.1016/j.cie.2018.09.016

Cited by 48 publications

(14 citation statements)

References 40 publications

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“…The achievements of the research on multi-agent dynamic task allocation [30][31][32] are mainly based on heuristic intelligent algorithms. Intelligent algorithms mainly use environmental learning or heuristic search, such as A* algorithms [33], evolutionary algorithms [34][35][36], and neural network-based methods, etc. Evolutionary algorithms based on simulated organisms mainly include ant colony algorithm(ACO), genetic algorithm or algorithms combining the two.…”

Section: Algorithms For Task Allocationmentioning

confidence: 99%

A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

et al. 2021

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In intelligent unmanned warehouse goods-to-man systems, the allocation of tasks has an important influence on the efficiency because of the dynamic performance of AGV robots and orders. The paper presents a hierarchical Soft Actor-Critic algorithm to solve the dynamic scheduling problem of orders picking. The method proposed is based on the classic Soft Actor-Critic and hierarchical reinforcement learning algorithm. In this paper, the model is trained at different time scales by introducing sub-goals, with the top-level learning a policy and the bottom level learning a policy to achieve the sub-goals. The actor of the controller aims to maximize expected intrinsic reward while also maximizing entropy. That is, to succeed at the subgoals while moving as randomly as possible. Finally, experimental results for simulation experiments in different scenes show that the method can make multi-logistics AGV robots work together and improves the reward in sparse environments about 2.61 times compared to the SAC algorithm. INDEX TERMS multi-logistics robot; task allocation; deep reinforcement learning; Actor-Critic; hierarchical reinforcement learning

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Section: Algorithms For Task Allocationmentioning

confidence: 99%

A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

et al. 2021

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“…In general, the initial solutions are randomly generated in GA, which reduces the execution efficiency of the algorithm. Some literature have shown that the better solution can be found quickly by seeding the initial solution with heuristic rules in an artificial intelligence algorithm [51][52][53]. Therefore, we use the LSPT rule and Property 1 improve accuracy and efficiency of the initial solution.…”

Section: Sptmentioning

confidence: 99%

A Single Machine Scheduling with Periodic Maintenance and Uncertain Processing Time

Shen¹,

Zhu²

2020

IJCIS

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A single machine scheduling problem with periodic maintenance is studied in this paper. Due to many uncertainties in reality, the processing time is recognized as an uncertain variable. The aim is to minimize the makespan at a confidence level. An uncertain chance-constrained programming model is developed to delve into the impact of uncertainties on decision variables, and an algorithm for calculating the objective function is proposed. According to the theoretical analysis, a novel method named longest shortest processing time (LSPT) rule is proposed. Subsequently, an improved genetic algorithm (GA-M) combined with LSPT rule is proposed. Numerical experiments are used to verify the feasibility of this model and algorithm.

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“…With respect to applications, CS has been extensively applied to many domains, such as facility layout design [21], continuous dynamic optimization [22], reliability redundancy allocation [23], structural optimization [24], mining industry [25], scheduling [26], [27] and fault diagnosis [28] and so on. These applications indicate that CS algorithm is an effective and efficient optimizer for solving some real-world problems.…”

Section: Related Workmentioning

confidence: 99%

Cuckoo Search Algorithm With Neighborhood Attraction for Numerical Optimization

Cheng

Wang

2019

IEEE Access

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Cuckoo search (CS) algorithm has been proved to be an effective method in solving numerical optimization problems. Nevertheless, with regard to Levy flight, each individual is attracted by the best solution found so far in the entire population, which may lead to premature convergence. Motivated by this observation, a new cuckoo search with neighborhood attraction (NACS) is proposed. In NACS, the neighborhood attraction scheme based on ring topology is firstly designed, where the best solution in a predefined neighborhood is employed to guide individual evolution. Then, to further enhance the exploration ability, the neighborhood attraction scheme and Levy flight are combined to generate potential candidate solutions. Moreover, the step size is adaptively regulated according to the degree of individual evolution. To validate the effectiveness of the presented algorithm, 25 extensively used benchmark test problems with different dimensions are employed. Experimental results reveal that the presented method is a competitive optimizer compared with other algorithms.

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An improved cuckoo search algorithm for scheduling jobs on identical parallel machines

Cited by 48 publications

References 40 publications

A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

A Single Machine Scheduling with Periodic Maintenance and Uncertain Processing Time

Cuckoo Search Algorithm With Neighborhood Attraction for Numerical Optimization

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