单队段多产品批处理过程的短期调度 2．模型的简化

本文第一部分建立了具有并行处理设备多产品单阶段批处理过程短期调度的基本数学模型，根据该模型和多产品单阶段批处理过程的特点，本文这一部分引入一些启发性规则，并将这些启发性规则融入到模型中，合理地使用这些启发性规则不但能减小模型的整数变量，连续变量和约束的数量，使得模型表达更紧，求解速度加快，而且能得到最优解，大量计算表明该模型求解速度快，尤其对包含多个同种订单的调度问题更为显著。     

单阶段多产品批处理过程的短期调度1. 基本模型的建立

具有并行设备的多产品单阶段批处理过程短期 调度问题需考虑订单发布时间、交货期，订单生产的顺序相关建立时间、禁止生产子序列， 及设备的准备时间等生产约束．本文在考虑上述约束的基础的上，利用时间间隙的概念和连 续时间表达，将设备、订单分配给时间间隙分别表达为两类0-1变量，建立了具有并行生产 线的多产品单阶段批处理过程的短期调度数学模型．模型表达为一个混合整数规划（MILP） 问题．该模型不但比已有的基于时间间隙描述的调度模型0-1变量少，而且能优 化多种目标函数．本文的第二部分将引入一些适当的启发性规则，减小了模型的规模，并应 用大量的计算实例说明该模型的有效性和适用性．     

带分批优化的多级批处理过程自组织调度方法

针对一类带批次划分的多级批处理过程优化调度问题,提出一种自下而上的自组织调度方法．首先,通过构造与批处理生产过程中的订单、批次和设备相对应的自组织个体,建立自组织调度模型框架;然后,分析多级批处理调度问题的最优性质,提出分批优化规则和自组织选择策略,并在此基础上给出自组织优化调度算法;最后,通过调度实例求解结果表明,所提方法能在短时间内获得问题的最优解或近优解,进而验证了该方法的有效性和优越性．     

多目的批处理过程的短期调度

基于状态任务网络（STN）描述批处理过程，提出了多目的批处理过程的混合整数规则（MILP）调度数学模型，建模采用连续时间描述，并将任务和设备分配集中表达为一类0－1变量。为进一步提高模型的求解效率，根据过程和所建数学模型的特点，提出了一有效的方法，以减小模型的规模，实际计算表明，该数学模型及减小模型规模的方法是有效的。     

冷轧合同批量调度的模糊Job Shop模型及算法

针对冷轧薄板生产线机组设备多、产品种类多的特点,提出一种合同批量调度方法．该方法将生产合同按产品种类和交货期组批处理,建立了具有模糊处理时间的Ｊｏｂｓｈｏｐ调度模型来描述整个生产物流情况,并定义了分情况的合同批量加工时间推算规则．针对调度模型的求解,给出一种多子种群并行粒子群算法．利用宝钢冷轧薄板厂的实际生产数据进行了试运行,结果表明,该方法可大幅度提高合同交货期满意度,并能有效减少合同批量调度的工作时间。     

基于改进模拟退火算法的针织生产线调度研究

针对含有多阶段生产流程的针织服装生产线调度问题，构建包含机器资源约束和成批约束的四阶段异构车间调度模型，利用多种调度规则对相邻阶段调度流程进行衔接。提出一种基于织片排序的带有多邻域搜索和重升温操作的改进模拟退火算法，以最大完工时间最小化为目标对上述模型进行求解。结合企业实际背景构建10种混合式订单作为案例进行仿真实验，并与现有优化算法进行对比，证实了该算法在求解针织服装生产线调度问题的有效性。     

冷轧合同批量调度的模糊Ｊｏｂ ｓｈｏｐ模型及算法

针对冷轧薄板生产线机组设备多、产品种类多的特点,提出一种合同批量调度方法．该方法将生产合同按产品种类和交货期组批处理,建立了具有模糊处理时间的Ｊｏｂｓｈｏｐ调度模型来描述整个生产物流情况,并定义了分情况的合同批量加工时间推算规则．针对调度模型的求解,给出一种多子种群并行粒子群算法．利用宝钢冷轧薄板厂的实际生产数据进行了试运行,结果表明,该方法可大幅度提高合同交货期满意度,并能有效减少合同批量调度的工作时间。

     

多目的批处理过程的短期调度

基于状态任务网络(STN)描述批处理过程,提出了多目的批处理过程的混合整数规划(MILP)调度数学模型.建模采用连续时间描述,并将任务和设备分配集中表达为一类0-1变量.为进一步提高模型的求解效率,根据过程和所建数学模型的特点,提出了一些有效的方法,以减小模型的规模.实际计算表明,该数学模型及减小模型规模的方法是有效的.     

多产品批处理不确定生产调度的粒子群算法

针对化工生产中操作时间的不确定性,考虑了含有混合中间存储策略的多产品批处理过程调度问题;采用三角模糊数去描述操作时间的不确定,建立了含有混合中间存储策略的调度模型;通过面积补偿法将原有的模糊调度模型转化为确定性的模型;最后采用结合遗传算法和模拟退火思想的混合粒子群算法进行优化求解,仿真结果证明了模型的有效性和算法的可行性.     

基于改进粒子群算法的离子膜车间调度问题研究

针对某电化厂离子膜车间的调度问题,以产值最大化为目标函数,建立具有中间存储的连续和批处理过程相结合的多产品多批次调度模型.提出一种改进的粒子群算法(IPSO),加入自适应混沌变异操作,在加强算法局部搜索能力的同时保证搜索过程中种群的多样性,并利用IPSO对建立的模型进行求解.仿真结果表明了模型和算法的有效性,在满足计划的前提下,获得了满意的日生产总值.     

列队竞争算法求解多产品间歇生产过程排序问题

由于组合爆炸特性,多产品厂的排序问题很难求解大规模甚至中等规模的问题,本文采用一种新的随机型进化搜索算法——列队竞争算法来对该问题进行求解,引入新的选择策略和变异方法。计算表明同已有的方法相比,该方法求解效率高、收敛速度快、使用简单方便,是一种求解多产品间歇过程排序问题的有效算法,为多目的厂间歇过程排序研究提供了新思路。     

A hybrid instance-intensive workflow scheduling method in private cloud environment

Aiming to solve the problem of instance-intensive workflow scheduling in private cloud environment, this paper first formulates a scheduling optimization model considering the communication time between tasks. The objective of this model is to minimize the execution time of all workflow instances. Then, a hybrid scheduling method based on the batch strategy and an improved genetic algorithm termed fragmentation based genetic algorithm is proposed according to the characters of instance-intensive cloud workflow, where task priority dispatching rules are also taken into account. Simulations are conducted to compare the proposed method with the canonical genetic algorithm and two heuristic algorithms. Our simulation results demonstrate that the proposed method can considerably enhance the search efficiency of the genetic algorithm and is able to considerably outperform the compared algorithms, in particular when the number of workflow instances is high and the computational resource available for optimization is limited.

     

基于DEPSO 的模糊时间ZW多产品厂间歇调度

研究以最小化完工时间为目标的模糊加工时间零等待多产品厂间歇调度问题, 提出一种基于差分进化粒子群优化(DEPSO) 的间歇调度算法. 以基本粒子群算法为整体进化框架, 采用基于反向学习的方法初始化种群, 引入群体极值保持代数作为阈值, 利用基于排序的差分进化算法优化粒子个体极值位置, 改变粒子的搜索范围, 防止粒子陷入局部极值. 仿真实验验证了所提算法在解决模糊加工时间零等待多产品厂间歇调度问题上的有效性和优越性.

     

A Bayesian Approach to Batch Process Scheduling

The problem of decision timing in the context of batch scheduling is addressed in this paper. The representation of time in any scheduling model affects the number of integer variables and the convexity of the model. The usual procedure in batch process scheduling is to divide the scheduling horizon into equal size intervals to achieve the required accuracy. This construction generates a formulation with a potentially large number of binary variables. In this paper, the time events arising in the schedule are modeled directly, and thus the use of binary variables over periods during which no changes in system state occur is avoided. The problem is formulated as a mixed integer nonlinear program (MINLP). The Bayesian heuristic (BH) approach is used to implement a global optimization algorithm which effectively solves the resulting model. Computational comparisons using two text examples are made against a UDM (uniform discretization model) formulation. The results suggest that the BH approach combined with the nonuniform time discretization formulation shows promise for the solution of batch scheduling problems.     

Scheduling unrelated parallel batch processing machines with non-identical job sizes and unequal ready times

This research analyzes the problem of scheduling a set of n jobs with arbitrary job sizes and non-zero ready times on a set of m unrelated parallel batch processing machines so as to minimize the makespan. Unrelated parallel machine is a generalization of the identical parallel processing machines and is closer to real-world production systems. Each machine can accommodate and process several jobs simultaneously as a batch as long as the machine capacity is not exceeded. The batch processing time and the batch ready time are respectively equal to the largest processing time and the largest ready time among all the jobs in the batch. Motivated by the computational complexity and the practical relevance of the problem, we present several heuristics based on first-fit and best-fit earliest job ready time rules. We also present a mixed integer programming model for the problem and a lower bound to evaluate the quality of the heuristics. The small computational effort of deterministic heuristics, which is valuable in some practical applications, is also one of the reasons that motivates this study. The results show that the heuristic proposed in this paper has a superior performance compared to the heuristics based on ideas proposed in the literature.     

染缸排产建模及滑动时间窗启发式调度算法

针对染缸排产问题约束复杂、任务规模大、排产效率要求高的特点,为了提高问题模型和算法在实际场景中的适用性,建立了染缸排产增量调度模型,提出了滑动时间窗启发式调度（STWS）算法。该算法以最小化延误代价、洗缸成本、染缸切换成本为优化目标,使用启发式调度规则,按照优先级顺序调度产品;对于每个产品的调度,先用动态拼缸算法和拆缸算法进行批次划分,然后调用批次最佳排序算法调度批次。使用某染纱企业车间实际生产数据仿真调度,所提算法可在10 s内完成月度计划的调度。相对于人工排产方式,所提算法提高了排产效率,显著优化了三个目标,在增量调度中洗缸成本和染缸切换成本也有明显优化。实验结果表明所提算法具有很好的调度能力。     

基于订单更改的间歇多产品厂调度在线调整研究

针对间歇化工厂产品市场变化快的特点，提出了基于订单更改的多产品厂调度在线调整模型：该模型根据生产进度对产品进行分类，并结合原调度采取相应的调整策略，得到满意的调整方案。模型基于原调度，不需增加太多变量，故在建模和实际应用中易于实现。文中将该模型用于无限中间储罐的间歇多产品厂调度在线调整实例，并通过调度调整方案对订单更改时刻的灵敏度分析找出了目标函数的影响因素。     

基于遗传禁忌算法的多目的间歇过程调度问题求解

由于组合爆炸特性,多目的厂的调度问题很难求解大规模甚至中等规模的问题,本文采用一种新的随机性优化技术一基于禁忌技术的遗传算法点(Tabu-genetic algorithm,TGA)来对该问题进行求解,引入新的选择策略和变异方法．并以零等待的多目的间歇过程调度为实例,计算表明同已有的方法相比,该方法求解效率高、收敛速度快、使用简单方便,可有效的克服计算负荷和求解质量之间的冲突,是一种求解多目的厂间歇过程调度问题的有效算法。     

A hybrid scheduling approach for a two-stage flexible flow shop with batch processing machines

In this paper, we discuss a flexible flow shop scheduling problem with batch processing machines at each stage and with jobs that have unequal ready times. Scheduling problems of this type can be found in semiconductor wafer fabrication facilities (wafer fabs). We are interested in minimizing the total weighted tardiness of the jobs. We present a mixed integer programming formulation. The batch scheduling problem is NP-hard. Therefore, an iterative stage-based decomposition approach is proposed that is hybridized with neighborhood search techniques. The decomposition scheme provides internal due dates and ready times for the jobs on the first and second stage, respectively. Each of the resulting parallel machine batch scheduling problems is solved by variable neighborhood search in each iteration. Based on the schedules of the subproblems, the internal due dates and ready times are updated. We present the results of designed computational experiments that also consider the number of machines assigned to each stage as a design factor. It turns out that the proposed hybrid approach outperforms an iterative decomposition scheme where a fairly simple heuristic based on time window decomposition and the apparent tardiness cost dispatching rule is used to solve the subproblems. Recommendations for the design of the two stages with respect to the number of parallel machines on each stage are given.