Integrated Optimization For Efficient And Low-Carbon Process Planning And Workshop Scheduling

With the development of industrialization,human beings use a lot of energy to enerate carbon emissions,which leads to global warming.As a supporting industry for the country’s basic economic development,the machinery manufacturing industry still has shortcomings such as low energy utilization,large carbon emissions,and unbalanced production capacity.As a typical manufacturing system,machining has the characteristics of large carbon emission,complex composition and many influencing factors.Therefore,machining low-carbon manufacturing as a sustainable development concept has a large space for energy saving and emission reduction optimization.Process planning and workshop scheduling are two systems,which are closely related,interact with each other and can be coordinated.Therefore,this paper studies and analyzes the integrated system for low-carbon process planning and workshop scheduling,establishes a carbon emission evaluation function based on machine tool equipment and various auxiliary facilities manufacturing systems,and integrates the maximum completion time function.From the perspective of multi-objective integrated optimization,Build an optimization model based on high-efficiency and low-carbon process planning and workshop scheduling.And applied genetic algorithm to solve it,and finally verified the validity of the model through experiments.Firstly,the carbon emission model of machining workshop process planning and workshop scheduling is established,and the carbon emission composition of each part of process planning and workshop scheduling is analyzed,and the impact mechanism of carbon emission of each part is explored.On this basis,an integrated carbon emission model of process planning and workshop scheduling is established,which closely integrates multi-workpiece feature processing methods,process routes,workpiece processing sequences and machine tool allocation of processes,making up for the separate optimization of workshop process planning and workshop scheduling,which ignores process planning.Due to the lack of influence of the processing method and process route on the workshop scheduling stage in the scheme,a high-efficiency and low-carbon model is established to realize the multi-objective integrated optimization of the process planning and workshop scheduling integration in machining.Next,the second-generation Non-Dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ)is used to solve the model.For the process planning,the design includes three characteristic strings,optional process strings and optional machine strings.Segmentation to achieve high-efficiency,low-carbon,multi-objective optimization.For workshop scheduling,multi-layer coding of process strings and optional machine strings is designed to achieve high-efficiency,low-carbon and multi-objective optimization.Because there are many parameter optimizations in the integration of process planning and workshop scheduling,and the process route selection,workpiece sequence sequence and machine tool selection are interrelated and inseparable.Therefore,a four-segment coding is designed to achieve high-efficiency and low-carbon multi-objective optimization.Then,according to the proposed machining process planning and shop scheduling model,a set of examples are selected for matlab simulation analysis,and the NSGA-Ⅱ algorithm is used to solve the multi-objective optimization of process planning,shop scheduling and the integration of process planning and shop scheduling.First,the process sequence of the target workpiece is solved through multi-objective optimization of process planning,and then the results of multi-objective optimization of workshop scheduling are used to compare the results of multi-objective optimization of process planning and workshop scheduling,and conclusions are drawn to verify the effectiveness of the high-efficiency and low-carbon model.Finally,summarize the full text,analyze the problems of the research methods and then look forward to the research prospects.