Research On Green Scheduling Optimization Of Complex Batch Production Based On Genetic Algorithm

Industrial energy consumption is huge,energy consumption is high,and how to improve energy efficiency is a long-term focus of attention in industry and academia.Electrical energy is the main form of industrial energy consumption.Time-of-use(TOU)electricity price,as one of the important methods to achieve demand response(DR),has great advantages in helping electric power users to save energy.Different from the traditional energy-saving mode,the purpose of TOU electricity price is to encourage power users to optimize energy consumption mode by avoiding peak hours of electricity price,thus reducing power cost.Production scheduling under TOU electricity price has become an attractive means of green dispatching.Based on this,this paper discusses a green batch scheduling problem on a non-homogeneous parallel machine under TOU electricity price.Based on the characteristics of the model's strong constraints and nonlinearity,an efficient genetic algorithm(SPGA)for solving large-scale problems is proposed.The algorithm distributes products in batches to parallel machines with different performances,minimizing total electricity cost(TEC)by adjusting the batch production sequence and start-up time on each machine through a greedy strategy.The computational solution on a given example was performed using the CPLEX solver,and the results were evaluated for SPGA performance evaluation.The experimental results show that SPGA has shorter calculation time and better calculation effect than CPLEX in large-scale instance calculation.In order to further improve the calculation speed and solution quality,a parameter adaptive multi-population genetic algorithm(MPGA)is proposed for SPGA improvement.Different operational strategies and parameters are adopted for different populations,and information between populations is exchanged in co-evolution to enhance population diversity.The experimental results show that the proposed MPGA algorithm can achieve better performance in terms of solving quality,and MPGA achieved by parallel computing is better than SPGA in computing time performance.