为协调离散制造过程中多重不确定性因素对柔性作业车间调度的影响，提出综合

In order to coordinate the influence of multiple uncertain factors on flexible job shop scheduling in the discrete manufacturing process, a flexible job shop scheduling strategy that comprehensively considers the uncertain processing time, the fuzzy delivery date and the uncertainty of the equipment maintenance cycle is proposed. First, combined with digital twin technology, the overall framework and process of flexible job shop production scheduling are constructed. Then, with the optimization goals of minimizing completion time, lowest production cost, least carbon emissions, and maximizing customer satisfaction, fuzzy functions are used to describe the uncertainty of processing time and delivery date, and interval numbers are used to describe the uncertainty of the equipment maintenance cycle. Determinism, thereby constructing a multi-objective collaborative optimization scheduling model. Then the multi-strategy fusion fuzzy particle swarm optimization algorithm is used to optimize the pareto feasible solution set, and combined with the gray correlation analysis to select the satisfactory solution that meets the actual production. Finally, a case study was carried out with the production task of a manufacturing company as an example to verify the effectiveness and feasibility of this method.

In order to coordinate the influence of multiple uncertain factors on Flexible Job Shop Scheduling in discrete manufacturing process, a flexible job shop scheduling strategy considering the uncertainty of processing time, fuzzy delivery time and equipment maintenance cycle is proposed. Firstly, combined with digital twin technology, the overall framework and process of flexible job shop production scheduling are constructed. Then, taking minimizing the completion time, minimum production cost, minimum carbon emission and maximizing customer satisfaction as the optimization objectives, the uncertainty of processing time and delivery date is described by fuzzy function, and the uncertainty of equipment maintenance cycle is described by interval number, so as to build a multi-objective collaborative optimization scheduling model. Then, the Pareto feasible solution set is obtained by Multi Strategy fusion fuzzy particle swarm optimization algorithm, and the satisfactory solution in line with the actual production is selected combined with grey correlation analysis. Finally, taking the production task of a manufacturing enterprise as an example, the effectiveness and feasibility of this method are verified.

In order to coordinate the influence of multiple uncertain factors on flexible job shop scheduling in discrete manufacturing process, a flexible job shop scheduling strategy was proposed, which comprehensively considered uncertainty of processing time, fuzzy delivery date and uncertainty of equipment maintenance cycle. Firstly, the overall framework and process of flexible job shop production scheduling are constructed by combining digital twinning technology. Then, with the optimization objectives of minimizing the completion time, the lowest production cost, the least carbon emissions and maximizing customer satisfaction, the uncertainty of processing time and delivery date is described by fuzzy function, and the uncertainty of equipment maintenance period is described by interval number, so as to build a multi-objective collaborative optimization scheduling model. Then, the pareto feasible solution set is obtained by multi-strategy fusion fuzzy particle swarm optimization algorithm, and the satisfactory solution which accords with the actual production is selected by grey relational analysis. At last, a case study of a manufacturing enterprise's production task proves the effectiveness and feasibility of this method.