Research On Optimization Of Energy Consumption For Flexible Job Shop Scheduling Problem Under Uncertainty

As an important resource for human survival and development,energy plays an important role in industrialization,urbanization and living standards.Energy is also one of the most important resources for enterprise development.Nowadays,China faces a very grim energy situation because we produce and consume large amounts of energy.Because of its large energy consumption,high cost of consumption,large amount of carbon dioxide emissions,the manufacturing industry has caused serious impact on the environment.With the rise in energy prices and the trend of sustainable development,the research on energy has become a hot pot and is also heating up.Therefore,the manufacturing industry must reduce energy consumption to thrift costs and protect the environment.Flexible job shop scheduling problem is a more complicated combinatorial optimization problem which is developed on the basis of classical job shop scheduling problem.Flexible job shop scheduling plays an important role in the processes of production,which can better fulfill the requirements of modern manufacturing systems.Based on the analysis of relevant research theories at home and abroad,this paper systematically studies the optimization of energy consumption for flexible job shop scheduling problem under uncertainty.The main research work of this paper includes:First of all,Based on the relevant theoretical research,this paper summarizes the current research situation and the development about the optimization of energy consumption in manufacturing industry,flexible job shop scheduling problem and job shop scheduling problem under uncertainty condition.This paper puts forward the idea of energy saving and consumption reduction by establishing energy consumption optimization model under the premise of ensuring normal production.Secondly,in this paper,an optimal energy consumption scheduling model for a flexible job shop is established under the uncertainty which includes two goals: makespan and energy consumption.In this model,the processing time of the machine is random variable,and the corresponding assumptions and constraints are also given.Thirdly,intelligent optimization algorithm is used to solve the problem of energy consumption optimization in flexible job shop under uncertainty in this paper.This article designs the genetic operations related to encoding and decoding,crossover operation and mutation operation of non-dominated sorting genetic algorithm-II.At the same time,the algorithm is improved accordingly when considering the algorithm in the process of solving scheduling problems prone to redundant solutions.Eventually,this paper applies the scheduling model to the actual production workshop and simulates the actual data.The experimental results show that the improved non-dominated sort genetic algorithm-II is superior to the standard non-dominated sort genetic algorithm-II.And convergence and feasibility of the algorithm are verified by experiments.This paper not only expands the theoretical research on flexible job shop scheduling,but also provides guidance for enterprises to carry out energy consumption optimization.