Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the “evaporation concept” applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The “evaporation concept” is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

[1]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[2]  Jan Karel Lenstra,et al.  Complexity of machine scheduling problems , 1975 .

[3]  D. Ackley.  A connectionist machine for genetic hillclimbing , 1987 .

[4]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[5]  Safia Kedad-Sidhoum,et al.  A faster branch-and-bound algorithm for the earliness-tardiness scheduling problem , 2008, J. Sched..

[6]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[7]  Hideo Tanaka,et al.  Genetic algorithms for flowshop scheduling problems , 1996 .

[8]  Pei-Chann Chang,et al.  A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties , 2009 .

[9]  Jorge M. S. Valente,et al.  Improved heuristics for the early/tardy scheduling problem with no idle time , 2005, Comput. Oper. Res..

[10]  Fred Glover,et al.  Critical Event Tabu Search for Multidimensional Knapsack Problems , 1996 .

[11]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..

[12]  Zbigniew Michalewicz,et al.  Evolutionary algorithms for constrained engineering problems , 1996, Computers & Industrial Engineering.

[13]  Pei-Chann Chang,et al.  Mining gene structures to inject artificial chromosomes for genetic algorithm in single machine scheduling problems , 2008, Appl. Soft Comput..

[14]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..

[15]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[16]  M. Selim Akturk,et al.  Theory and Methodology a New Dominance Rule to Minimize Total Weighted Tardiness with Unequal Release Dates , 1998 .

[17]  C. N. Potts,et al.  Scheduling with release dates on a single machine to minimize total weighted completion time , 1992, Discret. Appl. Math..

[18]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[19]  Thomas E. Morton,et al.  The single machine early/tardy problem , 1989 .

[20]  Kwang Mong Sim,et al.  Ant colony optimization for routing and load-balancing: survey and new directions , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[21]  Qingfu Zhang,et al.  An evolutionary algorithm with guided mutation for the maximum clique problem , 2005, IEEE Transactions on Evolutionary Computation.

[22]  George Z. Li.  Single machine earliness and tardiness scheduling , 1997 .

[23]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[24]  J. P. Kelly,et al.  Meta-heuristics : theory & applications , 1996 .

[25]  Pei-Chann Chang.  A branch and bound approach for single machine scheduling with earliness and tardiness penalties , 1999 .

[26]  José Ignacio Hidalgo,et al.  A hybrid heuristic for the traveling salesman problem , 2001, IEEE Trans. Evol. Comput..

[27]  Jorge M. S. Valente,et al.  Heuristics for the Early/Tardy Scheduling Problem with Release Dates , 2007 .

[28]  Pei‐Chann Chang,et al.  A two‐phase approach for single machine scheduling problems: Minimizing the total absolute deviation , 1992 .

[29]  Pei-Chann Chang,et al.  A greedy heuristic for bicriterion single machine scheduling problems , 1992 .

[30]  Maria João Alves,et al.  MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem , 2007, Comput. Oper. Res..

[31]  G. Harik.  Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

[32]  Shu-Cherng Fang,et al.  An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..

[33]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[34]  Pei-Chann Chang,et al.  One-machine rescheduling heuristics with efficiency and stability as criteria , 1993, Comput. Oper. Res..

[35]  T. S. Abdul-Razaq,et al.  Dynamic Programming State-Space Relaxation for Single-Machine Scheduling , 1988 .

[36]  Safia Kedad-Sidhoum,et al.  The One-Machine Problem with Earliness and Tardiness Penalties , 2003, J. Sched..

[37]  Jose M. Framiñan,et al.  A review and classification of heuristics for permutation flow-shop scheduling with makespan objective , 2004, J. Oper. Res. Soc..

[38]  S. Baluja.  An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics , 1995 .

[39]  Qingfu Zhang,et al.  On the convergence of a class of estimation of distribution algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[40]  Gilbert Syswerda,et al.  Simulated Crossover in Genetic Algorithms , 1992, FOGA.

[41]  Michael Pinedo,et al.  Current trends in deterministic scheduling , 1997, Ann. Oper. Res..

[42]  A. J. Clewett,et al.  Introduction to sequencing and scheduling , 1974 .

[43]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons , 2000, IEEE Trans. Evol. Comput..

[44]  Jung Woo Jung,et al.  Flowshop-scheduling problems with makespan criterion: a review , 2005 .

[45]  Rubén Ruiz,et al.  A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime , 2013, Comput. Oper. Res..

[46]  Reza Rastegar,et al.  A Step Forward in Studying the Compact Genetic Algorithm , 2006, Evolutionary Computation.

[47]  Ching-Fang Liaw,et al.  A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem , 1999, Comput. Oper. Res..

[48]  Shumeet Baluja,et al.  Fast Probabilistic Modeling for Combinatorial Optimization , 1998, AAAI/IAAI.

[49]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[50]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[51]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[52]  Philippe Baptiste,et al.  Dominance-based heuristics for one-machine total cost scheduling problems , 2008, Eur. J. Oper. Res..

[53]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .