Evolutionary Optimisation for Robotic Disassembly Sequence Planning and Line Balancing

The performance of an evolutionary algorithm in solving disassembly sequence planning or disassembly line balancing greatly depends on six parts: the evolutionary operator; encoding scheme; solution selection and update strategy; population initialisation; solution maintenance; and terminal condition. This chapter introduces classical single-objective evolutionary algorithms (SOEAs) with typical evolutionary operators, and multi-objective evolutionary algorithms (MOEAs) with typical solution selection and update strategies. The chapter also elaborates on common encoding schemes. Typical settings on algorithm initialisation, solution maintenance and terminal conditions are introduced to help engineers to design efficient evolutionary algorithms for robotic disassembly optimisation problems.

[1]  Ayyuce Aydemir-Karadag,et al.  Multi-objective optimization of stochastic disassembly line balancing with station paralleling , 2013, Comput. Ind. Eng..

[2]  Andrew M. Sutton,et al.  Genetic Algorithms - A Survey of Models and Methods , 2012, Handbook of Natural Computing.

[3]  Surendra M. Gupta,et al.  A balancing method and genetic algorithm for disassembly line balancing , 2007, Eur. J. Oper. Res..

[4]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[5]  Hisao Ishibuchi,et al.  Evolutionary many-objective optimization , 2019, GECCO.

[6]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[7]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[8]  Bernhard Sendhoff,et al.  A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[9]  Wei-Chang Yeh,et al.  Simplified swarm optimization in disassembly sequencing problems with learning effects , 2012, Comput. Oper. Res..

[10]  Xin Yao,et al.  Many-Objective Evolutionary Algorithms , 2015, ACM Comput. Surv..

[11]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[12]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  Amit Singh,et al.  Artificial Bee Colony Algorithm with Uniform Mutation , 2011, SocProS.

[15]  Chaoyong Zhang,et al.  Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm , 2017, Eng. Appl. Artif. Intell..

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[18]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[19]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[20]  Surendra M. Gupta,et al.  Disassembly sequencing using genetic algorithm , 2006 .

[21]  Markus Wagner,et al.  Evolutionary many-objective optimization: A quick-start guide , 2015 .

[22]  Tao Zhang,et al.  Evolutionary Many-Objective Optimization: A Comparative Study of the State-of-the-Art , 2018, IEEE Access.

[23]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[24]  Yixiong Feng,et al.  A new multi-objective ant colony algorithm for solving the disassembly line balancing problem , 2010 .

[25]  Ajd Fred Lambert,et al.  Disassembly sequencing: A survey , 2003 .

[26]  L. Coelho.  A quantum particle swarm optimizer with chaotic mutation operator , 2008 .

[27]  E. Hughes.  Multiple single objective Pareto sampling , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[28]  R.SIVARAJ,et al.  A REVIEW OF SELECTION METHODS IN GENETIC ALGORITHM , 2011 .

[29]  Duc Truong Pham,et al.  Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing , 2018, Int. J. Prod. Res..

[30]  Surendra M. Gupta,et al.  A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem , 2013, The International Journal of Advanced Manufacturing Technology.

[31]  Adam Lipowski,et al.  Roulette-wheel selection via stochastic acceptance , 2011, ArXiv.

[32]  Xiang-Jun Zhao,et al.  Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation , 2018, Memetic Comput..

[33]  Qingfu Zhang,et al.  An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition , 2015, IEEE Transactions on Evolutionary Computation.

[34]  Ezequiel A. Di Paolo,et al.  An efficient genetic algorithm with uniform crossover for air traffic control , 2009, Comput. Oper. Res..

[35]  Liang Gao,et al.  Disassembly sequence planning using a Simplified Teaching-Learning-Based Optimization algorithm , 2014, Adv. Eng. Informatics.