Low-carbon Scheduling Models And Applications In Tire Manufacturing Process

Manufacturing i s t he main e nergy c onsumption i ndustry, i n t he face of increasingly shortage of non-renewable resources, the traditional high discharge, high pollution manufacturing mode has not suitable for today’s industrial development road. One of the problems in domestic manufacturing industry should be solved urgently is how to reduce the energy consumption of the manufacturing process, and it refers to the enterprise overall benefit. Production scheduling is an effective means of realizing manufacturing energy-saving, it can effectively solve the energy waste problems in the pr ocess of pr oducing. According t o the e nergy c onservation and e mission reduction problems in m anufacturing industry, we established the1owest e nergy consumption with the aim o f permutation flow shop low-carbon scheduling model and hybr id flow s hop1ow-carbon scheduling m odel, a nd t hen a pplied to tire manufacturing process, t he prac tical r esults show t he est ablished low-carbon scheduling m odel c an effectively reduce the m anufacturing process ene rgy consumption, realize energy conservation goal. The following is the main job of the dissertation:(1) Summarizes the research significance of the low-carbon manufacturing and low-carbon scheduling model. And the production scheduling, low-carbon scheduling model of t he dom estic and foreign research presen t si tuation are sum marized. Meanwhile, the character of the low-carbon scheduling model are deeply analyzed, and on this basis, the optimization algorithm for solving low-carbon scheduling model is proposed, namely the genetic algorithm and immune algorithm, and then expound the principle of two optimization algorithm.(2) According to the permutation flow shop process waiting results in machine idling, causing energy waste pr oblems, we establish permutation flow s hop low-carbon scheduling model with idle energy consumption minimum for target, after comparing the performance of the genetic algorithm and particle swarm algorithm, the improved differential genetic algorithm with good performance is chosen to verify the low-carbon s cheduling model. The expe rimental r esults show t hat, in the sam e conditions, compare to minimize the makespan as the target of scheduling models, low-carbon scheduling model with minimizing idle energy consumption as the goal can effectively reduce machine proc ess i dle ene rgy consumption after cer tain iteration.(3) In view of the actual processing workshop mostly notless than a machine, permutation flow shop low-carbon scheduling model is not suitable for solving this kind of scheduling pr oblem, t he p roblem be longs to hybr id flow s hop s cheduling problem. Therefore, we establish idle energy consumption minimum as the goal of the hybrid flow shop low-carbon scheduling model, after comparing the performance of the immune genetic algorithm and traditional genetic algorithm, the immune genetic algorithm with good pe rformance is chos en to ve rify hybrid flow s hop1ow-carbon scheduling m odel. E xperimental r esults show t hat t he i dle ene rgy consumption minimum for target of hybrid flow shop low-carbon scheduling model can converge to a small idle energy consumption and achieve energy saving purpose after a certain number of iteration.(4) Thelow-carbon scheduling modelis applied to tire manufacturing process, practice results show that using this paper established low-carbon scheduling model can effectively reduce energy consumption of the tire manufacturing process, realize energy conservation goal.