Research On Optimization Algorithm For Distributed Permutation Flow Shop Problem With Robot Constraints

In recent years,the state has deeply promoted the implementation of the manufacturing power strategy intensely by "made in China 2025".An excellent opportunity for innovation and development of the intelligent manufacturing industry is arriving with essential support of the "Internet + advanced manufacturing industry".Intelligent manufacturing technology is also gradually applied to the pharmaceutical industry,in which the optimization of scheduling problems is a vital link to improve the production efficiency of the intelligent manufacturing industry.Distributed permutation flow shop scheduling problem(DPFSP)is a canonical scheduling problem in the process of production and pharmaceuticals.This paper understands the prior knowledge and theory of related problems starting from the actual situation of processing scheduling,combined with realistic constraints such as robot loading and unloading time.Therefore,the DPFSP problem with robot constraints is proposed,an improved iterated greedy algorithm(IIG)is explored according to the structural characteristics of the problem,and a variety of effective strategies are designed.The research work of this paper includes the following contents.1.A mathematical programming model is established by focusing on the DPFSP with robot constraints,where the maximum completion time is minimized,and an encoding,decoding scheme with robot constraints is designed.Firstly,in the initialization stage of the IIG algorithm,two methods of assigning jobs to the factory are proposed,making the initial population more diverse.Secondly,in the local search phase of the algorithm,the swap and insertion neighborhood are applied to balance the algorithm’s ability to search for the solution.Then the simulated annealing(SA)algorithm is embedded to avoid falling into local optimization.Finally,the scale of the deleted job is set in the destruction and reconstruction phase,and the insertion position of the deleted job is tested to determine the optimal scheduling sequence of the job.2.By focusing on the DPFSP with robot and order constraints,which is solved by the IIG algorithm,a mathematical model is established according to the constraints of solving objectives and order assignment.An encoding and decoding scheme for assigning the jobs of the same order and factory is designed.Firstly,three kinds of assignment order modes are designed.Secondly,various neighborhood structure strategies are proposed to strengthen the local search,combined with the SA algorithm,which is embedded as the acceptance criterion.Then the destruction and reconstruction strategy is improved.The quality of the solution is further improved,the convergence ability of the algorithm is enhanced.Finally,the effects of different parameter levels are compared.3.Based on the model and optimization algorithm,simulation instances are constructed with the production scheduling of the pharmaceutical industry as the background.The IIG algorithm solves the DPFSP with robot and order assignment constraints to minimize the maximum completion time of the production factory.Finally,the simulation experimental results of different algorithms are compared and analyzed,which verifies the feasibility and effectiveness of the IIG algorithm to solve the above problems.