A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

Lin, Jian; Zhu, Ling; Gao, Kaizhou

doi:10.1016/j.eswa.2019.112915

Cited by 118 publications

(47 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can solve cross problem areas [21]. It is widely used in vehicle routing [22], nurse scheduling [23], reinforcement learning [24], production scheduling [25,26] and has great application potential.…”

Section: Hyper-heuristic Algorithmmentioning

confidence: 99%

A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

et al. 2021

View full text Add to dashboard Cite

Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.

show abstract

Section: Hyper-heuristic Algorithmmentioning

confidence: 99%

A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Lin et al 31 had suggested that the stage job shop planning issue with the goal of diminishing make the traverse. The algorithm of ABC is the inquiry heuristics used to take care of worldwide improvement issues in complex search spaces.…”

Section: Literature Reviewmentioning

confidence: 99%

A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem

Kavitha

Venkumar

2020

Measurement and Control

View full text Add to dashboard Cite

Job shop scheduling is one of the major issues in which the scheduling process is associated with the real-time manufacturing industry. A flexible job shop scheduling problem is one of the most important issues among the hardest combinatorial advancement issues. Flexible job shop scheduling is extremely a nondeterministic polynomial combinatorial problem. In this paper, it is proposed that a mixture of improvement demonstrates to make makespan minimization in the flexible job shop scheduling problem issue. This paper includes the hybridization of social spider optimization and genetic algorithm that is effectively controlled by the calculation via optimization techniques. Most of the part in this method is given as the scavenging methodology of social insects, which use the vibrations spread over the bug-catching network to decide the position of the target. These hybridization approaches after arachnid upgrading process hereditary calculation chromosomes are chosen to produce new arrangements nearer to the minimum makespan time. The main objective of this paper is to minimize the makespan time of ''n'' jobs and ''m'' machines. The proposed algorithms have effectively investigated many benchmark problems and the computational results were compared with existing metaheuristic, including progressive calculations and algorithms for the swarm intelligence in the flexible job shop scheduling problem.

show abstract

“…In order to achieve a specific objective of the organization, this resource may be a person, property, equipment or capital.The personnel assignment problem was first addressed by Votaw and Orden in 1952 [14], illustrating a method of assignment that helps to match a task to the most appropriate agent, in order to achieve the most efficient result. Many articles address the RAPunder several variances that appear in several areas such as: staff transfer/mobility [15]- [18], scheduling [19], [20], Timetabling Problem [21]- [23], maintenance [24] and also team formation [21], [25]- [28].…”

Section: About the Np-hard Problems: Resource Assignment And Vr Problemsmentioning

confidence: 99%

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

2020

IJATCSE

View full text Add to dashboard Cite

Artificial intelligence (AI) techniques play an important role in the development and optimization of complex problems in various fields. This study presents a systematic analysis of previously publications on the use of AI techniques to optimize the NP-hard problems in the area of human resource assignment to multi-constrained activities in order to increase the productivity and performance of a company, and in the area of vehicle routing in order to cost minimization. These two problems belonged to the NP-hard family. Among these AI techniques used for a better optimization of this type of problems, we discuss the genetic algorithms (GA), hybrid GA, parallel GA, GA with Big Data and improved AI techniques with special features are also discussed and presented through the different contributions of the researchers.

show abstract

A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

Cited by 118 publications

References 47 publications

A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

Contact Info

Product

Resources

About