Research On The Scheduling Problem And It's Ant Colony Optimization Algorithm For Collaborative Manufacturing Process

With the development of manufacturing, the complex products are often produced through the cooperation of several jobshops in an enterprise or different enterprises. These jobshops who produce different parts of the complex products compose a collaborative manufacturing system. Compared with the traditional manufacturing system, each jobshop in this system should arrange processing progress and pace, according to not only its own resources and constraint conditions, but also the others with the relationship of supply-demand, which impoves the complexity of scheduling. Research of new scheduling strategy and optimization algorithms are the new requirements of the manufacturing scheduling.Ant colony optimization (ACO) algorithm is a distributed intelligence algorithm, which is effective in solving combinatorial optimization problems, such as job shop scheduling problem. However, the ant colony algorithm also has some shortcomings, such as long computing time, slow convergence speed and easily plunged into local optimal, etc. Tabu search (TS) algorithm is a global neighborhood search algorithm, and it could jump out of local optimal to guide the algorithm to reach the global optimization by virtue of the tabu list and aspiration criterion.Based on the characteristics of Ant colony algorithm and tabu search algorithm, a hybrid ant colony algorithm (TSACO) is proposed. The initial solution of the tabu search algorithm is provided by the ant colony algorithm, which integrates the tabu search processes into the ant colony optimization and avoids the local convergence of the ant colony algorithm:design pheromone updating rules by the bi-directional convergence strategy to impove the search effciency:design jobshop-machine selection rules and neighbors function.In allusion to the basic principles of cooperative manufacturing scheduling, a two-stage distributed cooperative scheduling system is constructed to meet the actual needs of complex products in the manufacturing process. With the optimization objective of minimize the makespan,further research on improved ant colony algorithm to solve the cooperative scheduling problem:design a smaller simply collaborative scheduling example, and realize it in the VC++ environment, the results test and verify the effectiveness of the algorithm.