Sequencing Optimization Of Mixed Model Assembly Lines Based On Shuffled Frog Leaping Algorithm

With the continuous improvement of people' consumption level,the diversification and personalized demands of products are becoming mainstream of multi-species,small batch.Mixed production mode is gradually becoming the main mode of production of manufacturing production.The mixed model assembly line based on customers' requirements and produce different production tasks is with similar characteristics of the products would produce in the same production line,Although the process is similar,the needing production methods,standard time,the using of machine parts are also different.Therefore,the sequencing problem of mixed model assembly line is a complex combinatorial optimization problem,which belongs to NP-hard problem.In view of the complexity of mixed production process,it is very important to achieve the goals of coordinating production,making full use of the existing production line and ensuring the efficiency of the production line.It is necessary to study the sequencing optimization problem of the mixed model assembly line.This can improve assembly line production efficiency,short the production cycle and make full use of production resources.At present,China's mixed-model assembly line sequencing research is still in its infancy,the efficiency of mixed line production is not very high.Therefore,aiming at shuffled frog leaping algorithm(SFLA)is improved the multi-objective sequencing problem of mixed-flow assembly line.The main contents are as follows:1.Aiming at the slow convergence speed and low precision of traditional SFLA,a Chaotic Shuffled Frog Leaping Algorithm(CSFLA)was proposed.By using the characteristics of randomness and ergodicity of chaos optimization and few parameters and good optimizing ability of Shuffled Frog Leaping Algorithm,the strategies of local deep searching subpopulations and global update are done,and the worst of the random update strategy or the global optimal individual is optimized,the optimization result replaces randomly the worst individual of present population.This can make the algorithm jump out of local optimum to obtain better global optimal solution.Using the algorithm to solve the problem of the optimal objectives are minimum total utility time and average consumption rate of parts for the multi-objective optimization model of mixed-model assembly line.The example shows that the proposed CSFLA can obtain better results than SFLA,GA and TAPSO algorithm.2.Shuffled frog leaping algorithm can solve multi-objective sequencing optimization problem to avoid local optimization,but the optimal speed is too slowbecause of much randomness.To solve the problem better,optimization objective is minimum total production rate and minimum idle-overload time,a niche chaos shuffled frog leaping algorithm(NCSFLA)is proposed.Species are classified by niching technique,child populations within the radius scope of Euclidean distance are signed by selecting niche radius values,and the high fitness populations are chosen as a temporary optimal solution.The optimal solution is iterated by chaos,and spatial variation decreases gradually with iteration increase.At the beginning of evolution,big scale variation is advantageous to the algorithm which can search the global optimal solution in vast space,at later evolution,small scale variation is good to improve solution accuracy and convergence speed by fine searching around the local pole in small space.The example shows that the proposed algorithm can obtain better results than SFLA and PSO algorithm.3.Shuffled Frog Leaping algorithm(SFLA)is used to solve multi-objective sequencing problem of mixed flow assembly line.Local convergence can be avoided and optimal solution can be obtained to a certain extent.However,the multi-objective sequencing problem of mixed flow assembly line is a NP-hard problem,the shortcomings are slow convergence rate and low precision.To better solve the problems with optimization objectives of minimizing total utility time and keeping average consumption rate of parts,a quantum differential evolution shuffled frog leaping algorithm(QDESFLA)is proposed.The algorithm uses Bloch spherical coordinates of qubits to encode individuals and Bloch spherical rotation of qubits to update individuals.The individuals are mutated and population diversity is enhanced by Hadamard gates.This can enhance the ergodicity of the solution space and global search ability.the evolutionary operator of Differential evolutionary algorithm is as a local search strategy and the difference mutation operator is introduced into the chaotic sequence to prevent premature convergence.The example results show that the solution precision and convergence rate of the proposed algorithm are superior to other comparative algorithms.