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间歇生产调度过程中存在许多不确定因素,其中最重要的是需求不确定。考虑需求不确定的多周期间歇生产调度优化模型采用离散或连续时间表达方式,将调度时间域分割成大量与调度决策相关的时间段,导致模型中存在大量整数变量,给模型求解造成很大困难。本研究对已有求解方法进行了分析,提出分周期逼近算法。将多周期间歇生产调度决策问题分解为第一周期调度决策问题和其余周期调度决策问题,简化结构,加快求解速度。通过方案树聚集将表达需求不确定信息的方案树转化成若干方案文件,针对每个方案文件应用确定性方法获得调度决策,但只保留第一周期调度决策,可以减小最小利益方案对期望利益的影响,提高第一周期调度决策水平;获得若干第一周期候选调度决策后,以时间收缩三阶段方法确定其余周期较优调度决策,同时应用时间收缩策略和补偿策略,提高其余周期调度决策水平;最后用期望利益评估第一周期候选调度决策并确定全部周期调度决策。实例研究证明了本文提出的算法能够提高间歇生产调度决策水平,同时加快求解速度,能够有效求解多周期间歇生产调度优化模型。
There are many uncertainties in the process of batch production scheduling, the most important of which is the uncertainty of demand. Considering the uncertain demand of multi-cycle intermittent production scheduling optimization model using discrete or continuous time expression, the scheduling time domain is divided into a large number of scheduling-related time period, resulting in a large number of integer variables in the model, resulting in great difficulties in solving the model . In this study, we analyze the existing methods and propose the sub-periodic approximation algorithm. The multi-cycle intermittent production scheduling decision-making problem is decomposed into the first-cycle scheduling decision problem and the rest cycle scheduling decision-making problem, simplifying the structure and speeding up the solution. The program tree is used to aggregate the program tree that expresses the uncertainty information demand into several program files. The deterministic method is applied to each program file to obtain the scheduling decision. However, the first-cycle scheduling decision is retained to reduce the impact of the minimum benefit program on the expected benefits , And improve the decision-making level of the first-period scheduling. After obtaining the first-period candidate scheduling decisions, the three-stage method of time shrinkage is used to determine the optimal scheduling decisions for the remaining periods, and the time-shrinking strategies and compensation strategies are applied to improve the scheduling decisions of the remaining periods Finally, we use the expected benefits to evaluate the first-cycle candidate scheduling decision and determine all the periodic scheduling decisions. The case study proves that the proposed algorithm can improve the decision-making level of batch production scheduling and speed up the solution, and can effectively solve the multi-period batch production scheduling optimization model.