Research On Scheduling Algorithm Of Whole-set Orders Based On Bottleneck Analysis

With the progress of science and technology and with the development of economy, enterprises face more and more intense market competition, and customers demand individual products and delivery time of products are more and more strict. In the environment, enterprises make production in make-to-order. But because of the uncertainty of demand, it may be very difficult that the delivery time of all customers are met. Specially to whole-set orders in which any product that is delayed makes customers unsatisfied, scheduling production tasks to minimize the number of tardy jobs make the satisfaction degree of customers very low. So a new problem, scheduling problem to maximize the number of whole-set orders, is put forwarded.Mathematical models of the problem are presented based on the analysis of the scheduling problem of whole-set orders in single machine, parallel machines and multi-plants. And heuristic algorithm based on the bottleneck analysis is designed. Experiments are made to confirm the effectiveness of the algorithm. At first, the idea of the heuristic algorithm based on the bottleneck analysis is introduced, the feature of the delivery time bottleneck in production systems is analyzed, the method to determine and express the delivery time bottleneck and the description of the heuristic algorithm based on the bottleneck analysis is given. Then mathematical models of the scheduling problems of whole-set orders in single machine, parallel machines and multi-plants are presented. And the heuristic algorithm based on the bottleneck analysis and hybrid genetic algorithm is designed. Finally, the experiment data in single machine, parallel machines and multi-factory is produced, and the results of experiments by comparison of the heuristic algorithm, hybrid genetic algorithm and the heuristic algorithm which objective is to maximize the number of tardy jobs show that the heuristic algorithm is effective and scheduling problem to maximize the number of whole-set orders can raise customer service level.