A dynamic parameter controlled harmony search algorithm for assembly sequence planning

Assembly sequence planning (ASP) plays an important role in intelligent manufacturing. As ASP is a non-deterministic polynomial (NP) hard problem, it is scarcely possible for a brute force approach to find out the optimal solution. Therefore, increasing meta-heuristic algorithms are introduced to solve the ASP problem. However, due to the discreteness and strong constraints of ASP problem, most meta-heuristics are unsuitable or inefficient to optimize it. Harmony search (HS) algorithm is one of the most suitable meta-heuristics for solving the problem. This paper proposes a dynamic parameter controlled harmony search (DPCHS) for solving ASP problems including a transformation of the assembly sequences by the largest position value (LPV) rule, initializing harmony memory with opposition-based learning (OBL) and designing dynamic parameters to control evolution. The key improvement to former work lies in the introduction of a dynamic pitch adjusting rate and bandwidth, which are adapting their value during the evolution. The performances of the DPCHS and the fixed harmony search algorithm are compared thoroughly in the case studies. Meantime, the efficiency of this algorithm in solving ASP problems is tested using two cases, and the results of other popular algorithms are compared. Furthermore, the DPCHS has been successfully applied to an industrial ASP problem.

[1]  Z. H. Che,et al.  A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB , 2012, Expert Syst. Appl..

[2]  Yuan-Jye Tseng,et al.  A particle swarm optimisation algorithm for multi-plant assembly sequence planning with integrated assembly sequence planning and plant assignment , 2010 .

[3]  Mahmoud Efatmaneshnik,et al.  Assembly sequence planning for processes with heterogeneous reliabilities , 2017 .

[4]  Y. Wang,et al.  Chaotic particle swarm optimization for assembly sequence planning , 2010 .

[5]  H. Zhang,et al.  Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm , 2014 .

[6]  Hwai-En Tseng,et al.  Using memetic algorithms with guided local search to solve assembly sequence planning , 2007, Expert Syst. Appl..

[7]  Xinyu Li,et al.  An enhanced harmony search algorithm for assembly sequence planning , 2013, Int. J. Model. Identif. Control..

[8]  Yanfeng Xing,et al.  Assembly sequence planning based on a hybrid particle swarm optimisation and genetic algorithm , 2012 .

[9]  Quan-Ke Pan,et al.  Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives , 2016, J. Intell. Manuf..

[10]  Ashutosh Tiwari,et al.  A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches , 2012 .

[11]  Cong Lu,et al.  An assembly sequence planning approach with a discrete particle swarm optimization algorithm , 2010 .

[12]  Xinyu Li,et al.  Application of memetic algorithm in assembly sequence planning , 2010 .

[13]  Abdelmajid Benamara,et al.  Subassembly generation algorithm from a CAD model , 2016 .

[14]  Lei Yue,et al.  Hybrid Pareto artificial bee colony algorithm for assembly line balancing with task time variations , 2017, Int. J. Comput. Integr. Manuf..

[15]  Zhou-Ping Yin,et al.  A connector-based hierarchical approach to assembly sequence planning for mechanical assemblies , 2003, Comput. Aided Des..

[16]  Arthur C. Sanderson,et al.  AND/OR graph representation of assembly plans , 1986, IEEE Trans. Robotics Autom..

[17]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[18]  Wenlei Zhang,et al.  Generating interference matrices for automatic assembly sequence planning , 2017 .

[19]  Zaifang Zhang,et al.  A new discrete double-population firefly algorithm for assembly sequence planning , 2016 .

[20]  Blaine Lilly,et al.  Mechanical Assemblies: their Design, Manufacture, and Role in Product Development , 2013 .

[21]  Zhi Kong,et al.  Approximate Normal Parameter Reduction of Fuzzy Soft Set Based on Harmony Search Algorithm , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[22]  Ibrahim Kucukkoc,et al.  A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem , 2019, J. Intell. Manuf..

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

[24]  Shang Jianzhong,et al.  An efficient method of automatic assembly sequence planning for aerospace industry based on genetic algorithm , 2017 .

[25]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[26]  Cem Sinanoğlu,et al.  An assembly sequence‐planning system for mechanical parts using neural network , 2005 .

[27]  Pablo Jiménez,et al.  Survey on assembly sequencing: a combinatorial and geometrical perspective , 2013, J. Intell. Manuf..

[28]  Zong Woo Geem,et al.  Artificial Satellite Heat Pipe Design Using Harmony Search , 2015, ICHSA.

[29]  Hamzah Ahmad,et al.  An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm , 2015 .

[30]  Daniel E. Whitney.  Mechanical Assemblies: Their Design, Manufacture, and Role in Product Development [Book Review] , 2005, IEEE Robotics & Automation Magazine.

[31]  Steven Li,et al.  Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm , 2015, Comput. Oper. Res..

[32]  Liang Gao,et al.  Assembly sequence planning based on an improved harmony search algorithm , 2016 .

[33]  Peng-Jun Zhao,et al.  A Hybrid Harmony Search Algorithm for Numerical Optimization , 2010, 2010 International Conference on Computational Aspects of Social Networks.

[34]  Z. H. Che,et al.  A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence , 2010 .

[35]  Mehmet Fatih Tasgetiren,et al.  A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem , 2008, Comput. Oper. Res..

[36]  Kensuke Harada,et al.  Integrated assembly and motion planning using regrasp graphs , 2016, Robotics and biomimetics.

[37]  Simeng Liu,et al.  Assembly sequence planning for reflector panels based on genetic algorithm and ant Colony optimization , 2016, The International Journal of Advanced Manufacturing Technology.

[38]  Yazhi Li,et al.  Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search , 2015, Expert Syst. Appl..

[39]  Zhen,et al.  COMPUTER-AIDED BLOCK ASSEMBLY PROCESS PLANNING IN SHIPBUILDING BASED ON RULE-REASONING , 2008 .

[40]  Bing Zeng,et al.  The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning , 2016 .