Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling

This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature.

[1]  Nasr Al-Hinai,et al.  An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem , 2011 .

[2]  Mostafa Zandieh,et al.  Flexible job-shop scheduling with parallel variable neighborhood search algorithm , 2010, Expert Syst. Appl..

[3]  Kazem Abhary,et al.  A genetic algorithm for the optimisation of assembly sequences , 2006, Comput. Ind. Eng..

[4]  Romeo M. Marian,et al.  Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 2: Genetic operators and results , 2013, 2013 5th International Conference on Knowledge and Smart Technology (KST).

[5]  Quan-Ke Pan,et al.  A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem , 2010 .

[6]  L. Luong,et al.  Solving Part Type Selection and Loading Problem in Flexible Manufacturing System using Real Coded Genetic Algorithms – Part II : Optimization , 2012 .

[7]  Yue-wen Fu,et al.  Hybrid particle swarm optimization for flexible job-shop scheduling problem and its implementation , 2010, The 2010 IEEE International Conference on Information and Automation.

[8]  M. Zandieh,et al.  A Simulated Annealing Algorithm for Flexible Job-Shop Scheduling Problem , 2009 .

[9]  Pierre Borne,et al.  Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[10]  Sun Yao-hui.  Hybrid Particle Swarm Optimization for Flexible Job-Shop Scheduling , 2007 .

[11]  Liang Gao,et al.  Variable Neighborhood Genetic Algorithm for the Flexible Job Shop Scheduling Problems , 2008, ICIRA.

[12]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[13]  Nhu Binh Ho,et al.  GENACE: an efficient cultural algorithm for solving the flexible job-shop problem , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[14]  Liang Gao,et al.  An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem , 2009, Comput. Ind. Eng..

[15]  Lee Luong,et al.  Solving Part Type Selection and Loading Problem in Flexible Manufacturing System Using Real Coded Genetic Algorithms - Part I: Modeling , 2012 .

[16]  Imed Kacem,et al.  Genetic algorithm for the flexible job-shop scheduling problem , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[17]  Wayan Firdaus Mahmudy,et al.  Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization , 2013 .