Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem

The solution to the job shop scheduling problem (JSSP) is of great significance for improving resource utilization and production efficiency of enterprises. In this paper, in view of its non-deterministic polynomial properties, a multi-agent genetic algorithm based on tabu search (MAGATS) is proposed to solve JSSPs under makespan constraints. Firstly, a multi-agent genetic algorithm (MAGA) is proposed. During the process, a multi-agent grid environment is constructed based on characteristics of multi-agent systems and genetic algorithm (GA), and a corresponding neighbor interaction operator, a mutation operator based on neighborhood structure and a self-learning operator are designed. Then, combining tabu search algorithm with a MAGA, the algorithm MAGATS are presented. Finally, 43 benchmark instances are tested with the new algorithm. Compared with four other algorithms, the optimization performance of it is analyzed based on obtained test results. Effectiveness of the new algorithm is verified by analysis results.

[1]  A. Noorul Haq,et al.  Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach , 2017, J. Intell. Manuf..

[2]  Yuan Yan Tang,et al.  Multi-agent oriented constraint satisfaction , 2002, Artif. Intell..

[3]  Egon Balas,et al.  Guided Local Search with Shifting Bottleneck for Job Shop Scheduling , 1998 .

[4]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[5]  Congxin Li,et al.  Research on immune genetic algorithm for solving the job-shop scheduling problem , 2007 .

[6]  Stéphane Dauzère-Pérès,et al.  A batch-oblivious approach for Complex Job-Shop scheduling problems , 2017, Eur. J. Oper. Res..

[7]  Dawei Chen,et al.  Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network , 2017, IEEE Transactions on Industrial Informatics.

[8]  Mauro Dell'Amico,et al.  Applying tabu search to the job-shop scheduling problem , 1993, Ann. Oper. Res..

[9]  Orhan Engin,et al.  A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems , 2018, Appl. Soft Comput..

[10]  Peigen Li,et al.  A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem , 2007, Comput. Oper. Res..

[11]  Tung-Kuan Liu,et al.  Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms , 2017, J. Intell. Manuf..

[12]  Emanuela Merelli,et al.  A tabu search method guided by shifting bottleneck for the job shop scheduling problem , 2000, Eur. J. Oper. Res..

[13]  Mario Vanhoucke,et al.  A hybrid single and dual population search procedure for the job shop scheduling problem , 2011, Eur. J. Oper. Res..

[14]  Peter Brucker,et al.  Job-shop Scheduling Problem , 2009, Encyclopedia of Optimization.

[15]  Mitsuo Gen,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation , 1996 .

[16]  Fei Tao,et al.  A novel search algorithm based on waterweeds reproduction principle for job shop scheduling problem , 2015, The International Journal of Advanced Manufacturing Technology.

[17]  Mohamed Kurdi,et al.  An effective new island model genetic algorithm for job shop scheduling problem , 2016, Comput. Oper. Res..

[18]  Egon Balas,et al.  Machine Sequencing Via Disjunctive Graphs: An Implicit Enumeration Algorithm , 1969, Oper. Res..

[19]  Banu Çalis,et al.  A research survey: review of AI solution strategies of job shop scheduling problem , 2013, Journal of Intelligent Manufacturing.

[20]  Haibo Hu,et al.  An Effective Hybrid Genetic Algorithm for Job Shop Scheduling Problem , 2011 .

[21]  Jing Li,et al.  Hybrid flow shop rescheduling algorithm for perishable products subject to a due date with random invalidity to the operational unit , 2017 .

[22]  Abdelghani Bekrar,et al.  Solving the flexible job-shop just-in-time scheduling problem with quadratic earliness and tardiness costs , 2015 .

[23]  Leila Asadzadeh,et al.  A local search genetic algorithm for the job shop scheduling problem with intelligent agents , 2015, Comput. Ind. Eng..

[24]  Chao Lu,et al.  A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem , 2015, Comput. Ind. Eng..

[25]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[26]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[27]  E. Nowicki,et al.  A Fast Taboo Search Algorithm for the Job Shop Problem , 1996 .

[28]  Christian Bierwirth,et al.  A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective , 2016, Comput. Oper. Res..

[29]  Yang Liu,et al.  DeepPF: A deep learning based architecture for metro passenger flow prediction , 2019, Transportation Research Part C: Emerging Technologies.