A hybrid algorithm for total tardiness minimisation in flexible job shop: genetic algorithm with parallel VNS execution

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. It is quite difficult to achieve optimal or near-optimal solutions with single traditional optimization approach because the multi objective FJSP has the high computational complexity. An novel hybrid algorithm combined variable neighborhood search algorithm with genetic algorithm is proposed to solve the multi objective FJSP in this paper. An external memory is adopted to save and update the non-dominated solutions during the optimization process. To evaluate the performance of the proposed hybrid algorithm, benchmark problems are solved. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.

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A Hybrid Genetic Algorithm for Flexible Job-Shop Scheduling Problem

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.889-890.1179 ◽

2014 ◽

Vol 889-890 ◽

pp. 1179-1184 ◽

Cited By ~ 2

Author(s):

Shuang Xi Wang ◽

Chao Yong Zhang ◽

Liang Liang Jin

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Hybrid Genetic Algorithm ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Job Shop Scheduling Problem ◽

Shop Scheduling

In this paper, a hybrid genetic algorithm is presented for the flexible job-shop scheduling problem with makespan criterion. A new machine assignment strategy is proposed to improve the initial population. A modified coding scheme is presented, and a population improvement strategy is performed when the best solution of the population did not improve during some generations. This hybrid algorithm is tested on a series of benchmarks instances. Experimental results show that this hybrid algorithm is efficient and competitive compared to some existing methods.

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A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.476 ◽

2011 ◽

Vol 268-270 ◽

pp. 476-481

Author(s):

Li Gao ◽

Ke Lin Xu ◽

Wei Zhu ◽

Na Na Yang

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Job Shop ◽

Flow Shop ◽

Job Shop Scheduling ◽

Selection Procedure ◽

Dynamic Programming Method ◽

Two Stage ◽

Simulated Annealing Genetic Algorithm ◽

Mutation Operators

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

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