scholarly journals A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hamed Piroozfard ◽  
Kuan Yew Wong ◽  
Adnan Hassan
Keyword(s):  
Genetic Algorithm ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Function Evaluation ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Knowledge Based

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

scholarly journals Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Noor Hasnah Moin ◽  
Ong Chung Sin ◽  
Mohd Omar
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Search Space ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Crossover Operator ◽  
Shop Scheduling ◽  
Generation Procedure

The job shop scheduling problem (JSSP) is one of the well-known hard combinatorial scheduling problems. This paper proposes a hybrid genetic algorithm with multiparents crossover for JSSP. The multiparents crossover operator known as extended precedence preservative crossover (EPPX) is able to recombine more than two parents to generate a single new offspring distinguished from common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained, a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces competitive results.

A Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problems

2013 ◽  
Vol 845 ◽  
pp. 559-563
Author(s):  
Hamed Piroozfard ◽  
Adnan Hassan ◽  
Ali Mokhtari Moghadam ◽  
Ali Derakhshan Asl
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Computational Time ◽  
Insertion Mutation ◽  
Scheduling Problems ◽  
Exact Methods ◽  
Shop Scheduling ◽  
Mutation Operators

Job shop scheduling problems are immensely complicated problems in machine scheduling area, and they are classified as NP-hard problems. Finding optimal solutions for job shop scheduling problems with exact methods incur high cost, therefore, looking for approximate solutions with meta-heuristics are favored instead. In this paper, a hybrid framework which is based on a combination of genetic algorithm and simulated annealing is proposed in order to minimize maximum completion time i.e. makespan. In the proposed algorithm, precedence preserving order-based crossover is applied which is able to generate feasible offspring. Two types of mutation operators namely swapping and insertion mutation are used in order to maintain diversity of population and to perform intensive search. Furthermore, a new approach is applied for arranging operations on machines, which improved solution quality and decreased computational time. The proposed hybrid genetic algorithm is tested with a set of benchmarking problems, and simulation results revealed efficiency of the proposed hybrid genetic algorithm compared to conventional genetic based algorithm.

Entropy-Enhanced Genetic Algorithm with Tabu Search for Job Shop Scheduling Problems

2012 ◽  
Vol 590 ◽  
pp. 557-562 ◽  
Author(s):  
Ying Jie Huang ◽  
Xi Fan Yao ◽  
Dong Yuan Ge ◽  
Yong Xiang Li
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Traditional Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Benchmark Instances

By combining Genetic algorithm with Tabu search algorithm and adjusting crossover rate and mutation rate based on information entropy, a hybrid genetic algorithm was proposed for larger-scale job shop scheduling problems, and the benchmark instances were used to verify the algorithm with simulation. Simulation results show that the proposed algorithm can solve larger-scale job shop scheduling problems, and it has obvious advantages over traditional scheduling algorithms.

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.

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