Research On Energy-efficient Scheduling Problems For Discrete Manufacturing Process

The rapidly growing demand for manufactured products has led to a continued rise in global energy consumption levels.Until renewable energy sources such as solar energy and wind energy have not become the main source of global energy consumption,fossil non-renewable energy sources such as coal,oil and natural gas are still the main part of global energy consumption sources.Faced with the possible environmental pollution and shortage of fossil energy,energy conservation and emission reduction have become a common concern for governments and energy-intensive enterprises in various countries.In addition to the development of hardware technologies such as energy-saving devices,the efficient use of energy based on planning and scheduling is becoming a research hotspot in academia.This dissertation investigates four types of energy-saving mechanisms such as interruptible power supply,time-varying energy consumption cost,controllable machine processing speed,and flexible workshop configuration.The background is the cutting process of CNC machine tools and the continuous casting and rolling process of steel.The four types of energy-efficient scheduling optimization problems are studied separately.In order to solve the above problems,the impact of various energy-saving mechanisms on the production scheduling scheme of the manufacturer was first analyzed,and the structured properties of the optimal scheduling scheme for the problem was proposed.Based on this,the targeted heuristic algorithms or improved meta-heuristic algorithms were developed.The solution performance of the above algorithms has been illustrated in a series of simulation experiments.The main research contents and innovations of this article can be summarized as follows:1)The problem of order acceptance and scheduling with energy consumption and machine launch cost is studied.The structural properties of the single machine scheduling problem are analyzed.An improved variable neighborhood search algorithm is proposed and the effectiveness of this algorithm is verified by computational experiments.Factors such as machine launch selection,order release time,and order production deterioration effects are introduced into the traditional order accepting and scheduling problem.The goal is to maximize the net profit of the green manufacturing system.The structural properties of the optimal scheme for the sequence of jobs in an order,the sequence of the orders,and the calculation of energy consumption costs in the single machine scheduling problem are analyzed.The proof of the NP-hard property of the problem is given.Aiming at solving the general problem,an improved variable neighborhood search algorithm is proposed.In order to expand the neighborhood space,two types of neighborhood structures such as cross neighborhood structure and variant neighborhood structure are developed in combination with the coding and decoding rules.The fitness measurement method of the variable neighborhood search algorithm based on the dynamic programming algorithm is developed to provide relevant decisions on order selection and machine launch selection.Simulation experiment results show that the improved variable neighborhood search algorithm is superior to other variable neighborhood search algorithms and other baseline algorithms.2)The rescheduling optimization problem of energy-efficient production under time-varying energy consumption costs is studied.The optimization properties of the original scheduling scheme and the rescheduling scheme are analyzed.An improved variable neighborhood search algorithms are proposed.The comparison of algorithms verifies the effectiveness of the proposed algorithm.Based on the classic parallel machine rescheduling optimization model,factors such as time-varying energy consumption cost and linear deterioration effect are considered.The objective function is to minimize the total energy consumption of the manufacturing system under the limit of rescheduling disturbance indicators.The original scheduling scheme and rescheduling scheme are analyzed in terms of job sequencing,energy consumption cost calculation.An improved variable neighborhood search algorithm including three new interchange neighborhood structures is designed.In order to optimize the allocation of disturbance indicators on each machine,a fitness calculation based on dynamic programming algorithm is developed.Simulation experiment results verify that the proposed algorithm has certain advantages in solving the above rescheduling optimization model compared with artificial bee colony algorithm and differential evolution algorithm.3)The problem of mixed production enery-efficient scheduling optimization with controllable processing rate is studied,and the structural properties of single machine scheduling problem are analyzed.A single-machine scheduling algorithm based on dynamic programming algorithm and a series of heuristic algorithm for scheduling optimization problem of hybrid manufacturing system are proposed.The scheduling optimization model considering the variable processing speed and the deterioration effect in the hybrid manufacturing system.The objective function is to shorten the manufacturing cycle to the greatest extent with the limit of total energy consumption cost and remanufacturing cost budget.Based on the structural properties of the single machine optimal scheduling scheme,a single machine scheduling algorithm based on dynamic programming algorithm is proposed,which can provide decision support for product remanufacturing selection,job processing sequence,and job processing speed.At the same time,the special case of unlimited energy cost budget and minimizing energy consumption cost for a given set of jobs are studied,and corresponding dynamic programming algorithms are proposed.Aiming at the scheduling optimization problem of multi-machine hybrid manufacturing system,two simple heuristic algorithms and a greedy algorithm are proposed.Simulation experiments analyze the performance of the three types of algorithms.4)The problem of production scheduling optimization considering the deterioration effect of jobs in flexible supply chain is studied,and the collaborative scheduling model of production and transportation considering parallel batch processing and deterioration effect is constructed.For any given machine situation,structural properties about batch production and batch sequencing are proposed.Based on these properties,a heuristic algorithm based on dynamic programming is developed to determine each batch transportation point.In order to solve general problems,an effective hybrid intelligent optimization algorithm is proposed,which combines a basied random key genetic algorithm and a flower pollination algorithm.Among them,the basied random key genetic algorithm as the peripheral framework of the algorithm is mainly to allocate job to the machine.The operator of the flower pollination algorithm is used for population iteration.Simulation experiment results show that the proposed algorithm is superior to basied random key genetic algorithm,flower pollination algorithm,and particle swarm algorithm in solving quality and convergence.This dissertation systematically studies the optimal scheduling problem of energy efficiency utilization for discrete manufacturing processes.The research content covers four types of effective industrial production energy-saving mechanisms and considers actual industrial production factors such as production deterioration effects and batch production.The research results can enrich the theoretical research on energy-efficient scheduling problem,and provide energy-saving and emission-reducing decision support for energy-intensive enterprises such as iron and steel enterprises.