An Improved Nondominated Sorting Genetic Algorithm-II for Multi-objective Flexible Job-shop Scheduling Problem Considering Worker Assignments

To solve the multi-objective flexible job shop scheduling problem, an improved non-dominated sorting genetic algorithm is proposed. Multi-objective mathematical model is established, four objectives, makespan, maximal workload, total workload and total tardiness are considered together. In this paper a dual coding method is employed, and infeasible solutions were avoided by new crossover and mutation methods. Pareto optimal set was taken to deal with multi-objective optimization problem, in order to reduce computational complexity, the non-dominated sorting method was improved. The niche technology is adopted to increase the diversity of solutions, and a new self adaptive mutation rate computing method is designed. The proposed algorithm is tested on some instances, and the computation results demonstrate the superiority of the algorithm.

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A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

Computational Intelligence and Neuroscience ◽

10.1155/2017/5232518 ◽

2017 ◽

Vol 2017 ◽

pp. 1-20 ◽

Cited By ~ 13

Author(s):

Qianwang Deng ◽

Guiliang Gong ◽

Xuran Gong ◽

Like Zhang ◽

Wei Liu ◽

...

Keyword(s):

Genetic Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Job Shop Scheduling Problem ◽

Nsga Ii ◽

Shop Scheduling ◽

Nondominated Sorting

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm withTiteration times is first used to obtain the initial populationN, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm withGENiteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

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A Novel Variable Neighborhood Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.118-120.369 ◽

2010 ◽

Vol 118-120 ◽

pp. 369-373 ◽

Cited By ~ 5

Author(s):

Guo Hui Zhang ◽

Liang Gao ◽

Yang Shi

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Job Shop Scheduling Problem ◽

Shop Scheduling ◽

Multi Objective

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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