A multi-objective algorithm for U-shaped disassembly line balancing with partial destructive mode

The disassembly line is the best way to deal with large-scale waste electrical and electronic equipment. Balancing of disassembly line is a hot and challenging problem in recent years. Given the uncertainty factors including corrosion and deformation of parts and components of waste products, this paper introduces the destructive mode and uncertainty disassembly time into the disassembly line and establishes a multi-objective disassembly line balancing model, considering partial destructive mode and U-shaped layout. The model aims to reduce the number of stations, balance the workload and reduce energy consumption while increasing the disassembly profit. A new multi-objective discrete flower pollination algorithm is proposed to solve the problem. Both task assignment and disassembly modes are considered in the encoding and decoding strategies of the flowers. Combining the discrete characteristics of the problem, the cross-pollination and self-pollination behaviors of the algorithm are redefined. The performance of the proposed algorithm is verified by solving two classical examples and by comparing with seven meta-heuristic algorithms. Then the proposed model and method are applied to a television disassembly line of a disassembly enterprise in China. The disassembly schemes of the proposed algorithm are superior to that of the five classical multi-objective algorithms. The results show that the proposed method can improve the performance of the disassembly line.

[1]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[2]  Xinyu Li,et al.  Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit , 2019, Journal of Cleaner Production.

[3]  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.

[4]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[5]  Askiner Gungor,et al.  A solution approach to the disassembly line balancing problem in the presence of task failures , 2001 .

[6]  Liang Gao,et al.  Service-oriented disassembly sequence planning for electrical and electronic equipment waste , 2016, Electron. Commer. Res. Appl..

[7]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[8]  Surendra M. Gupta,et al.  A tabu search algorithm for balancing a sequence-dependent disassembly line , 2014 .

[9]  Surendra M. Gupta,et al.  Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem , 2007 .

[10]  Peter J. Fleming,et al.  Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[11]  Yi Wang,et al.  A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem , 2017, Expert Syst. Appl..

[12]  Tommy W. S. Chow,et al.  Object-Level Video Advertising: An Optimization Framework , 2017, IEEE Transactions on Industrial Informatics.

[13]  Xinyu Li,et al.  Partial disassembly line balancing for energy consumption and profit under uncertainty , 2019, Robotics Comput. Integr. Manuf..

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

[15]  Yongquan Zhou,et al.  Discrete greedy flower pollination algorithm for spherical traveling salesman problem , 2017, Neural Computing and Applications.

[16]  Surendra M. Gupta,et al.  Ant colony optimization for sequence‐dependent disassembly line balancing problem , 2013 .

[17]  Chaoyong Zhang,et al.  Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm , 2018 .

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

[19]  Rajeev Jain,et al.  A Kano model, AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment , 2014, Appl. Soft Comput..

[20]  Liang Gao,et al.  A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem , 2019, Comput. Ind. Eng..

[21]  Surendra M. Gupta,et al.  Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm , 2015 .

[22]  Manoj Kumar Tiwari,et al.  A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem , 2008 .

[23]  Alexandre Dolgui,et al.  Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty , 2018, Int. J. Prod. Res..

[24]  Xiaowen Song,et al.  Disassembly sequence planning for electro-mechanical products under a partial destructive mode , 2014 .

[25]  Levent Kandiller,et al.  Profit-oriented disassembly-line balancing , 2008 .

[26]  Surendra M. Gupta,et al.  Disassembly line in product recovery , 2002 .

[27]  Allouani Fouad,et al.  A novel modified flower pollination algorithm for global optimization , 2018, Neural Computing and Applications.

[28]  Alexandre Dolgui,et al.  An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times , 2015 .

[29]  Shengxiang Yang,et al.  A Grid-Based Evolutionary Algorithm for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[30]  Manu Bansal,et al.  An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs , 2019, Neural Computing and Applications.

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

[32]  Surendra M. Gupta,et al.  Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem , 2013, Expert Syst. Appl..

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

[34]  Mohamed Abdel-Basset,et al.  Solving 0–1 knapsack problem by binary flower pollination algorithm , 2019, Neural Computing and Applications.

[35]  Surendra M. Gupta,et al.  Simulated Annealing Algorithm for Solving Sequence-Dependent Disassembly Line Balancing Problem , 2013, MIM.

[36]  Jaime Llorca,et al.  Nature-Inspired Self-Organization, Control, and Optimization in Heterogeneous Wireless Networks , 2012, IEEE Transactions on Mobile Computing.

[37]  Chaoyong Zhang,et al.  An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem , 2017, Int. J. Prod. Res..

[38]  Shengxiang Yang,et al.  Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[39]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[40]  Nurcan Deniz,et al.  An extended review on disassembly line balancing with bibliometric & social network and future study realization analysis , 2019, Journal of Cleaner Production.

[41]  Surendra M. Gupta,et al.  Ant colony optimization for disassembly sequencing with multiple objectives , 2006 .