Parallel bat algorithm for optimizing makespan in job shop scheduling problems

Parallel processing plays an important role in efficient and effective computations of function optimization. In this paper, an optimization algorithm based on parallel versions of the bat algorithm (BA), random-key encoding scheme, communication strategy scheme and makespan scheme is proposed to solve the NP-hard job shop scheduling problem. The aim of the parallel BA with communication strategies is to correlate individuals in swarms and to share the computation load over few processors. Based on the original structure of the BA, the bat populations are split into several independent groups. In addition, the communication strategy provides the diversity-enhanced bats to speed up solutions. In the experiment, forty three instances of the benchmark in job shop scheduling data set with various sizes are used to test the behavior of the convergence, and accuracy of the proposed method. The results compared with the other methods in the literature show that the proposed scheme increases more the convergence and the accuracy than BA and particle swarm optimization.

[1]  Yanchun Liang,et al.  An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Xin-She Yang,et al.  A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest , 2014, Expert Syst. Appl..

[3]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[4]  Satish Vadlamani,et al.  Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem , 2014, Neural Computing and Applications.

[5]  GenMitsuo,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms, part II , 1996 .

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

[7]  Eugene L. Lawler,et al.  Chapter 9 Sequencing and scheduling: Algorithms and complexity , 1993, Logistics of Production and Inventory.

[8]  Shih-Wei Lin,et al.  Makespan minimization for scheduling unrelated parallel machines with setup times , 2010, Journal of Intelligent Manufacturing.

[9]  Xiangtao Li,et al.  An efficient job shop scheduling algorithm based on artificial bee colony , 2011 .

[10]  Carlos A. Coello Coello,et al.  Use of an Artificial Immune System for Job Shop Scheduling , 2003, ICARIS.

[11]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

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

[13]  Mauricio G. C. Resende,et al.  Discrete Optimization A hybrid genetic algorithm for the job shop scheduling problem , 2005 .

[14]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[15]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[16]  John E. Beasley,et al.  OR-Library: Distributing Test Problems by Electronic Mail , 1990 .

[17]  Guan-Chun Luh,et al.  A multi-modal immune algorithm for the job-shop scheduling problem , 2009, Inf. Sci..

[18]  Rui Zhang,et al.  A hybrid immune simulated annealing algorithm for the job shop scheduling problem , 2010, Appl. Soft Comput..

[19]  Christos Koulamas,et al.  A survey of simulated annealing applications to operations research problems , 1994 .

[20]  Jeng-Shyang Pan,et al.  A Parallel Particle Swarm Optimization Algorithm with Communication Strategies , 2005, J. Inf. Sci. Eng..

[21]  David H. Wolpert,et al.  Coevolutionary free lunches , 2005, IEEE Transactions on Evolutionary Computation.

[22]  Tarik Çakar,et al.  Single machine scheduling with unequal release date using neuro-dominance rule , 2011, J. Intell. Manuf..

[23]  Seyyed M. T. Fatemi Ghomi,et al.  A survey of multi-factory scheduling , 2016, J. Intell. Manuf..

[24]  Shi-Jinn Horng,et al.  An efficient job-shop scheduling algorithm based on particle swarm optimization , 2010, Expert Syst. Appl..

[25]  Jeng-Shyang Pan,et al.  Ant colony system with communication strategies , 2004, Inf. Sci..

[26]  Eugene L. Lawler,et al.  Sequencing and scheduling: algorithms and complexity , 1989 .

[27]  Seyedmohsen Hosseini,et al.  A survey on the Imperialist Competitive Algorithm metaheuristic: Implementation in engineering domain and directions for future research , 2014, Appl. Soft Comput..

[28]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

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

[30]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[31]  Bin Jiao,et al.  A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan , 2006, Appl. Math. Comput..

[32]  Mohd Omar,et al.  Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems , 2015 .

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

[34]  Anne Xie,et al.  Advances in electrical engineering and automation , 2012 .

[35]  Henry Y. K. Lau,et al.  An AIS-based hybrid algorithm for static job shop scheduling problem , 2012, Journal of Intelligent Manufacturing.

[36]  David Abramson,et al.  A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIMETABLING PROBLEM , 1992 .

[37]  Hui Wang,et al.  Diversity enhanced particle swarm optimization with neighborhood search , 2013, Inf. Sci..

[38]  Ismail Hakki Cedimoglu,et al.  The strategies and parameters of tabu search for job-shop scheduling , 2004, J. Intell. Manuf..

[39]  Shyi-Ming Chen,et al.  Parallel Cat Swarm Optimization , 2008, 2008 International Conference on Machine Learning and Cybernetics.

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

[41]  David J. Kuck,et al.  A Survey of Parallel Machine Organization and Programming , 1977, CSUR.

[42]  Darrell Whitley,et al.  The Island Model Genetic Algorithm: On Separability, Population Size and Convergence , 2015, CIT 2015.

[43]  S. Meeran,et al.  A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study , 2011, Journal of Intelligent Manufacturing.

[44]  Fariborz Jolai,et al.  A two-stage hybrid flowshop scheduling problem in machine breakdown condition , 2013, J. Intell. Manuf..

[45]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[46]  Trong-The Nguyen,et al.  Parallelized Bat Algorithm with a Communication Strategy , 2014, IEA/AIE.

[47]  Lin Lin,et al.  Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey , 2014, J. Intell. Manuf..

[48]  Shao-zhong Song,et al.  Improved Simulated Annealing Algorithm Used for Job Shop Scheduling Problems , 2012 .

[49]  Scott Kirkpatrick,et al.  Optimization by Simmulated Annealing , 1983, Sci..