Research On Energy Consumption Optimization Method In Tire Manufacturing Enterprises

With the rising energy prices and the deepening environmental issues, the development of traditional manufacturing is being constrained by energy costs and environmental problem. The tire manufacturer are high energy-consuming and polluting enterprise. Reducing energy consumption in manufacturing process is one of the effective ways in which companies make cost control. As an important part of production management, production scheduling is the promising direction to achieve energy-saving and emission reduction.Aiming at the problem that energy consumption is not considered for the optimization strategy of machine scheduling in tire mixing workshop, an energy consumption optimization model with influence factors was established. The model takes the costs of total completion time and the energy consurmption as the ingredients. The target of model is comprehensive cost of the two factors. And the influence factors are added to represent the degree to which paying attention to the costs of time and energy in production. For the established energy consumption optimization model, an improved adaptive genetic algorithm (AAGA) is designed to solve the scheduling optimization problem. Based on the analysis of the "early maturity" of the computing results, the AAGA algorithm proposes a method to evaluate the degree of individual differences in each generation. Then, according to the evaluation index, the upper and lower values of the crossover and mutation probabilities of each generation are dynamically adjusted in the process of population evolution. At the same time, according to individual adaptability,the individual crossover and mutation probabilities of each population are adjusted. Based on the above-mentioned strategies for crossover and mutation, the data set of flow shop that Tillard provided is used to test. The experimental results show that the AAGA algoritl could find botter solutions.While considering the actual production data, the AAGA algorithm is used to solve the energy consumption optimization problem. The results show that AA(?)A has some advantages,compared with the application effect of SGA (Simple Genetic Alg(?)hm, SGA) and AGA(Aoaptive Genetic Algorithm, AG A) ,Furthermore, the AAG (?) used to validate the effectiveness of the energy consumption optimization model with influence factors. It shows that the energy consumption optimization model could achieve different levels of energy-saving targets by some values of influence factors.