Research On Workshop Scheduling Problem Based On ILOG

With the rapid development of technology and increasing global competition, the competition between manufacturing enterprises will be more drastic. In order to continuously improve and maintain the dominant position in the competition and core capability of competition, enterprises must strengthen their inner production management, especially its one of core-scheduling management. As a pillar and core of a whole enterprise, scheduling management is not only the meeting point of material flow, information flow and control flow, but also the foundation and root of realizing the entire company's operation goal. The research and application of effective workshop scheduling methods and optimization techniques were the key elements to promote production efficiency. Therefore, it is of great realistic significance and research value to research workshop scheduling problem.On the basis of the technical review on the domestic and foreign research,combining the actual workshop operation,an extensive study on workshop scheduling problem is carried out in this dissertation.Firstly, the conception,sorts,characteristics and optimization algorithms of workshop scheduling problem are described systematically, and randomization algorithm as a highly effective algorithm is introduced mainly. Secondly, the dissertation provides two workshop scheduling models which have characteristics of nonlinear mixed integer programming problem. One is Job Shop scheduling model which combines with re-entrant scheduling and parallel machine scheduling. Another is mixed workshop scheduling model which combines with Flow Shop scheduling, Job Shop scheduling, re-entrant scheduling and parallel machine scheduling. The former is the basis of the latter. Finally, through using actual data collected in production lines of one company in Xi'an, randomization algorithm designed by programming language such as ILOG Scheduler, ILOG Solver and C++, is used to simulate and optimize the mixed workshop scheduling model provided above, and then validate feasibility of the model and algorithm.