Multi-objective Parallel Machine Scheduling Problem For Energy Consumption Optimization

Nowadays,supply chain management has become an important issue,and enterprises should pay enough attention to achieving the highly effective operation.In recent years,the economic globalization brings great challenge to the organization and management of supply chain.As an indispensable part of the supply chain,manufacturing enterprises play an important role in the supply chain operation.As the rapid development of manufacturing industry,the shortage of resources have gradually emerged.Hence,reducing the energy consumption in the production process is particularly critical.Recently,many researches devote to decrease the energy consumption of manufacturing process,and production scheduling is regarded as an effective way to achieve this goal.Parallel machine scheduling is one of the most complex scheduling problems in manufacturing systems.In the actual production process,a great many of resources such as energy and manpower are consumed.Therefore,decision makers should consider both time-oriented and energy-saving criteria simultaneously when making scheduling decisions.Based on the summarization and analysis of existing studies,this paper extends the related content of parallel machine scheduling.The main work is summed up as:(1)To explore parallel machine scheduling problems with deteriorating effect with consideration of energy consumption optimization,a mathematical model aiming at minimizing the makespan and energy consumption is constructed.According to the characteristics of the problem under consideration,a multi-objective ant lion optimization algorithm is proposed.The simulation results are compared with two classical multi-objective optimization algorithms,and the results show that the proposed approach can better solve the scheduling problem.(2)To investigate stochastic parallel machine scheduling problems with learning effect by considering energy consumption optimization,a multi-objective stochastic programming model is established to minimize the makespan and energy consumption.To solve this problem,the non-dominated sorting genetic algorithm and decomposition based multi-objective evolutionary algorithm combining with a stochastic simulation approach are employed by considering the features of the considered problem.Through the experiments on a set of test problems,we make analysis on their performance in handling the considered problem.(3)To study stochastic parallel machine scheduling problems with deteriorating and learning effect considering energy consumption optimization,a multi-objective stochastic programming model is constructed to minimize the makespan and energy consumption.According to the characteristics of the investigated problem,a multi-objective multi-verse optimization combining with a stochastic simulation approach is designed.The simulation results show that the designed method is effective in solving our concerned problem.