Research On The Solution Method Of Single Machine Inverse Scheduling Based On Genetic Algorithm

The scheduling problems are to find an optimal solution in the situation whichvarious kinds of related processing conditions or parameters are known. However, theoriginal scheduling scheme is likely to be not optimal or even not feasible, because thereare a lot of dynamic uncertainty factors, such as downtime, plugging material and so on,in the actual manufacturing plant. In recent years, a new scheduling method for solvingsuch problem appears, which called inverse scheduling. And the inverse scheduling is tomake the scheduling scheme, which is not optimum due to the changed workshopcondition, become the best by adjusting some processing parameters as little as possible.The study focus of this article is to research the single machine inverse schedulingproblem(SMISP).According to the different types of problems and the differentoptimization objectives, the relevant scheduling models are established and thescheduling algorithms are designed.Firstly, the solving method for SMISP is studied. A solution frame based on geneticalgorithm has been put forward, which breaks the previously restricted to using precisemethod. And this framework provides guidance for further research.Secondly, the deep research on SMISP with maximum tardiness has been done. Inthis part, the mathematic model for this problem and a solution method based on geneticalgorithm have been created. To verify the effectiveness of the proposed method, someexperiments about this problem have been done.Thirdly, the SMISP with total weighted completion times is researched. The mainidea of the section is to make the objective of total weighted completion times beoptimized on the basis of the optimal scheduling scheme for maximum tardiness. In thepaper, the mathematical model of this problem is built and a solution method based ongenetic algorithms is proposed. To verify the effectiveness of the proposed method, someexperiments about this problem have been done.And then, the research on the multi-objective SMISP has been done in thisdissertation. The mathematic model of multi-objective SMISP is established, while asolution method based on the multi-objective evolutionary algorithm is proposedaccording to this model. To verify the effectiveness of the proposed method, someexperiments about this problem have been done. Finally, a summary of the whole paper is made and some future study directions arepointed out.