Research On Multi-objective Shop Scheduling Based On Improved Ant Colony Algorithm

With the continuous development of social economy,the intelligent degree of industrial production is also getting higher and higher.As the core of enterprise production development,production scheduling has also turned to rely on intelligent optimization scheduling algorithm to solve multi-objective,large-scale production planning.Reasonable scheduling scheme can not only make full use of limited resources,reduce the working time of workers,but also improve the production efficiency of enterprises.Shop scheduling problem is a typical combinatorial optimization problem,and it is also a multi-objective optimization problem,and these objectives often conflict with each other.In solving multi-objective optimization problems,the traditional way is to transform multi-objective into single-objective solution through certain rules.The solution obtained is unique and can not meet the requirements of enterprise production in many cases.In order to get more feasible solutions,it is of great theoretical and practical significance to apply artificial intelligence algorithm to solve job shop scheduling problems.Ant Colony Algorithms(ACA)is an intelligent optimization algorithm with characteristics of robustness,parallelism and positive feedback mechanism.It has strong optimization ability in solving discrete problems.The main works are as follows: First,how to improve the performance of ACA in solving multi-objective optimization problems is studied.The second is to study the application of ACA algorithm in job shop scheduling optimization.Based on the analysis and research of ACA,the ACA is improved.The improvements are mainly embodied in the following four aspects: Aiming at the shortcoming that blind search at the initial time of ant colony algorithm leads to a long search time,Logistic chaotic processing is applied to pheromone initialization;At the same time,the parameters alpha(pheromone importance factor)and beta(heuristic importance factor)are set to change with the number of iterations.In pheromone updating,Max-Min Ant system is introduced to limit pheromone concentration.Pareto ranking is used to evaluate the searched feasible solutions.The improvement of the algorithm is realized by MATLAB programming.The improved ACA is tested with the famous benchmark function and compared with other algorithms to verify the effectiveness of the improved algorithm in solving multi-objective optimization problems.The improved ACA is applied to solve multi-objective shop scheduling problem.A mathematical model of job shop scheduling is established with the objective of minimizing the maximum completion time and total tardiness time.The initial solution is obtained by HEN algorithm.In the transition probability,the ACA for solving multi-objective shop scheduling problem is constructed by combining the pseudo-random proportional rule with the random proportional rule.The standard test example of classical scheduling model is used to test the feasibility of the improved ACA in solving multi-objective job shop scheduling model.