| In recent years,China’s manufacturing industry is booming,but analyzing the development of China’s manufacturing industry from an objective perspective,we find that with the constant changes in domestic and foreign markets,resource shortages,diversified and personalized customer needs,and rising manufacturing costs.The current situation makes China’s manufacturing industry unable to adapt to the current rapid economic development needs,which makes manufacturers must continue to innovate to improve resource utilization,production efficiency and improve the quality of their products to improve their competitiveness.As the core part of the production process,production scheduling is an important means to realize the regulation and supervision of the production process and provide information support for the upper decision-making system of the enterprise,which is directly related to improving the resource utilization rate and the ultimate benefit of the enterprise.Under this background,how to establish corresponding production scheduling model and reasonable optimization algorithm according to different production scheduling problems in reality has great practical significance for improving the production theory and technology of enterprises.First of all,this paper summarizes the background,significance and research status of production scheduling problems at home and abroad.Secondly,considering the actual production and processing situation,this paper proposes a general production scheduling model based on position-based DeJong aging/learning effect.When the model indicates the aging effect,as the machining position of the workpiece increases,the machining time of the workpiece does not tend to infinity but tends to a stable value;when the model represents the learning effect,likewise,as the machining position of the workpiece increases,the machining time of the workpiece does not tend to zero but tends to a stable value.Compared with the traditional aging/learning effect scheduling model,this model is more reasonable and practical.On this basis,considering the actual processing time of the workpiece is also affected by factors such as resources,rate-modifying activities and past-sequencedependent delivery times,this paper adds linear resource consumption,convex resource consumption,rate-modifying activities and past-sequence-dependent delivery times on the basis of the general scheduling model,thus forming two different types of scheduling new models,which more closely match the actual production situation.Then,considering that with the increasing competition in the market,in the actual production and processing of the enterprise,time becomes an important factor in the competition of the enterprise.If the enterprise delivers before the agreed time point with the customer,the enterprise will generate early punishment,such as Inventory cost;if it is delivered later,the company will have late punishment,such as losing the credibility of the customer,so on-time delivery is getting more and more attention from the company.In the context of such just-in-time production,we have studied production scheduling problems with due dates.Among them,this paper mainly studies two different due date mechanism,namely common due date and slack due date.The former means that all workpieces have the same due date,and the latter means that each workpiece has its own due date.Thus,we define a workpiece whether it is delivered to the customer before the due date or delivered to the customer after the due date.There is a total weighted absolute lateness value,where the weight is a position-dependent weight.The two types of scheduling new models proposed in the previous paper are combined with these two due date mechanism to form four kinds of scheduling models which are complicated and more suitable for actual production.After analysis and solving,we can give the optimal algorithms to solve these four types of models.Finally,this paper uses LINGO software to analyze the designed algorithm,and analyzes the results obtained by each model,and draws relevant conclusions.The feasibility of the algorithm is verified from the practical point of view. |