Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Abstract

Currently, energy conservation draws wide attention in industrial manufacturing systems. In recent years, many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach. This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects. In it, the real processing time of jobs is calculated by using their processing speed and normal processing time. To describe this problem in a mathematical way, a multiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated. Furthermore, we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it. In this approach, the multi-verse optimization is adopted to find favorable solutions from the huge solution domain, while the stochastic simulation method is employed to assess them. By conducting comparison experiments on test problems, it can be verified that the developed approach has better performance in coping with the considered problem, compared to two classic multi-objective evolutionary algorithms.

Research on multi-time scale optimal scheduling of integrated energy system based on digital twinning

“…The main purpose of this optimization strategy is to improve the economic performance of the comprehensive energy system [3][4] , gas and heat are taken as the research object, and the objective function constructed is the minimum operating cost, which is taken as the execution basis of the optimal scheduling model. On this basis, the method comprehensively considers the upper and lower limits of the system's electricity, gas, and heat transport capacity, combines the constraints and limitations of the upper and lower limits of the output of various energy conversion equipment on the scheduling, and solves the optimized scheduling model based on the adaptive improvement of the Beetle Antennae Search (BAS) algorithm [5][6] . In the test results of this method, the reasonable optimal scheduling of the comprehensive energy system can be realized with or without considering the time-sharing energy price, which greatly improves the effective utilization rate of energy.…”