The EDA Algorithm Solves Three Types Of Complex Distributed Pipeline Scheduling Problems

As a kind of artificial complex system,the production and manufacturing system has the characteristics of non-linearity,uncertainty,multi-objective,multi-constraint,strong binding,NP hard,and multi-local optimality.The scheduling problem of production process is an important research field of manufacturing systems,and it is also one of the most complex and difficult problems in theoretical research.Intelligent optimization algorithm has been studied for a long time in solving complex production shop scheduling problems,which has become a hot research field in academia and industry.Estimation of distribution algorithm is an emerging algorithm in the field of evolutionary algorithm.Different from the processing mechanism generating new solution based on old solutions of other evolutionary algorithms,distribution estimation algorithm do it based on the information of distribution probability model for solution,it can effectively solve the optimization problems of complex flow shop scheduling.In this paper,the estimation of distribution algorithm is applied to solve three kinds of emerging complicated flow shop scheduling problems.The main research work is as follows:(1)For the distributed No-wait flow shop with Sequence Dependent Setup Times and Arrival times,this paper propose s an adaptive estimation of distribution algorithm to solve this problem,and the optimization goal is to minimize the m akespan of this problem.The algorithm uses the probability model of distribution to describe the information of the fine solutions and guide the generation of the new solution.W e uses variable neighborhood local Search Algorithm search the Neighbor Solut ion space of fine solutions.we verify the validity and robustness of the proposed the estimation of distribution algorithm by comparing with other algorithms,the test problem scales are different.(2)For Distributed Assembly Permutation Flow-Shop Scheduling Problem,we proposed a kind of modified estimation distribution algorithm to solve this problem,and the optimization goal is to minimize the makespan of this problem.The algorithm uses the probability model of distribution to guide the generation of the new solution from the product list level,the job list level and the feasible solutions level.By using the local search operations,we searched the neighbor solution space from three levels: product sequence level,work sequence level and the feasibl e solutions level.Through the simulation of different scale test problems and comparing with other algorithms,we verified the validity and robustness of the proposed distribution estimation algorithm.(3)For Distributed Heterogeneous Assembly Permutatio n Flow-Shop Scheduling Problem,We proposes a kind of ameliorated estimation distribution algorithm to solve this problem,and the optimization goal is to minimize the makespan of this problem.we adopt the ECF rule and apply it to this problem.Moreover,we propose local search adapting to this problem.Through the simulation of different scale test problems and comparing with other algorithms,we verified the validity and robustness of the proposed distribution estimation algorithm...