Plan optimization method used for supply chain of chemical enterprise

Abstract

The invention provides a plan optimization method used for a supply chain of a chemical enterprise. The plan optimization method belongs to the field of optimization methods, and comprises the steps of: acquiring initial parameters of the supply chain, and storing the initial parameters of the supply chain in a data warehouse; determining anticipated market requirements corresponding to the chemical enterprise through adopting a self-matching requirement forecasting algorithm by combining with sales data of the chemical company over the years, constructing a supply chain plan optimization model, setting a plan optimization objective function and constraint conditions in the supply chain plan optimization model, and calculating to obtain an optimal solution of the plan optimization objective function under the constraint conditions; and setting parameter step lengths, automatically constructing different plan optimization production scheduling schemes, calculating a profit correspondingto each plan scheme, and selecting the plan scheme corresponding to the highest profit for optimizing relevant parameters of the enterprise supply chain. The plan optimization method can provide theplan scheme for economical optimization solution when balancing supply and demand, so as to achieve dynamic adjustment of product structure, improve the precision, performability and working efficiency of the plan, and provide information support for production management.

Description

technical field [0001] The invention belongs to the field of optimization methods, in particular to an optimization method for chemical enterprise supply chain planning. Background technique [0002] At present, the supply chain planning of most domestic chemical enterprises is carried out by manual experience. However, for a large and medium-sized chemical company, there are many product specifications and frequent changes in business processes, so it is very complicated to manually prepare supply chain plans. [0003] On the one hand, the front-end demand planners of the supply chain plan need to make market forecasts based on historical data. Usually, the forecasting method is human judgment and summary, with a huge workload, without corresponding mathematical models and system assistance, large forecast deviations, and easy to mislead the supply plan; on the other hand On the one hand, in practice, a lot of information (such as inventory, in transit, whether raw materia...

AI Technical Summary

Problems solved by technology

However, for a large and medium-sized chemical company, there are many product specifications and frequent changes in business processes, so it is very complicated to manually prepare supply chain plans.

[0003] On the one hand, the front-end demand planners of the supply chain plan need to make market forecasts based on historical data. Usually, the forecasting method is human judgment and summary, with a huge workload, without corresponding mathematical models and system assistance, large forecast deviations, and easy to mislead the supply plan; on the other hand On the one hand, in practice, a lot of information (such as inventory, in transit, whether raw materials can arrive on time, raw material supply, logistics and transportation information, etc.) required by the supply chain planning of chemical companies in practice is opaque and asymmetric. It is difficult to balance the supply and demand of chemical raw materials and products. Auxiliary optimization of mathematical models, manual planning is difficult to ensure the overall optimal, resulting in frequent inconsistencies in raw material supply, planning, maintenance and inventory, and the overall planning completion rate is not satisfactory

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