单机带调整时间加权成套订单数问题遗传算法研究

通过大量的企业调研,提出了一类新的目标排序问题--单机带调整时间加权成套订单数排序问题:n个工件来自m个订单,分属B个不同类别,不同类之间的工件连续加工有调整时间,各工件有自己的交货期,一个订单中所有工件均按期完工则该订单成套完工,目标为加权成套延迟订单数最小.提出两类问题并且通过数学模型进行表述,设计相应的遗传算法,仿真结果表明该算法是可行而有效的.     

不确定环境下的单机成套订单鲁棒计划

讨论了不确定环境下订单数量可变的单机成套计划的优化问题,利用对偶变换给出了该问题的鲁棒整数规划模型,并设计了相应的遗传算法。算法对约束条件难点的处理采用了四种不同的方法即死亡惩罚、罚函数法、修补方法和解码方法,以检验算法的性能。最后进行了数值仿真实验,以比较不同算法的有效性。     

面向成套订单的生产与配送协调的排序研究

在工件体积和运输车辆容量的双重约束条件下,建立了以最大化成套订单数和最小化工件总配送时间的多目标规划模型,使用多目标排序寻找"约束解"的方法结合遗传算法求解此模型.最后通过算例分析,给出多目标规划模型及其综合算法在FLOW SHOP生产作业环境中的应用.计算结果表明,应用此模型和算法能够满足最大化成套订单数的要求,同时节省总的工件配送时间,有潜在的应用价值.     

关于导热反问题的一种遗传算法

导热反问题是一个不适定问题,它的求解往往是一项很复杂的计算任务．为了简化计算任务,缩短计算时间,文章提出一种解决导热反问题的遗传算法,该算法在遗传算法的基础上,针对导热反问题的不适定性特点,巧妙地优化遗传种群,较好地解决导热反问题。     

反相容工件系统的加权完工时间和的重新排序问题

重新排序问题是指在原始工件已经安排好的情形下,新到的工件集与原始工件集一起重新再排序,这是实际工作中常见一类优化问题。本文考虑了单机上当工件加工时间与权重反相容时,在最大错位量约束下的加权完工时间和最小化的重新排序问题。对于提出的四个问题,即在最大序列错位、最大时间错位、总序列错位和总时间错位约束下的加权完工时间和重新排序,基于问题的结构性质,运用动态规划方法分别给出了这些问题的多项式时间或拟多项式时间算法。     

改进遗传算法求解多时间约束的柔性作业车间调度问题

将加工时间、调整时间和移动时间分别作为独立时间因素考虑到柔性作业车间调度模型中,建立以最大完工时间最小、总调整时间最小、总移动时间最小为目标的考虑多时间约束的柔性作业车间调度模型,并提出改进的遗传算法求解该模型。通过测试标准数据集,并对比其他文献算法,验证了改进的遗传算法的可行性和有效性。     

多部件设备维修问题研究及遗传算法求解

合理组织设备维修，缩短设备维修时间是生产运营管理的一个重要环节。本文探讨了多部件设备维修的有效组织问题。考虑到维修工人对拆分后设备的不同部分具有不同的维修效率，提出了一种基于维修时间优化的决策方法，将其与经典指派方法进行了比较；进一步地，对这一NP-hard问题设计了遗传算法，通过数值例子讨论了其求解有效性。     

带时间窗车辆路径问题的并行遗传算法

采用并行遗传算法研究了带时间窗限制的车辆路径问题.通过设计并行算法和交叉、变异等算子提高了算法的计算效率和性能.通过计算若干benchmark问题,验证了模型的有效性.     

低压成套开关设备型式试验的问题及改进方法

低压成套开关设备安全性能的良好与否与低压电器的正常运行息息相关,基于此,本文在对低压成套开关柜型式试验过程中可能发生的问题进行研究的基础上,分析了可能导致问题的各类原因,并对如何改进做出提出了一些方法并作出了一些设想。     

遗传算法求解TSP问题的改进

针对遗传算法解决TSP问题的特点,构造出遗传算法需要的初始种群.运用相同算法对改进前后的两种种群进行实验,得出改进方法具有增强遗传算法效率的作用.     

Bi-objective mathematical model and improved algorithm for optimisation of welding shop scheduling problem

This paper addresses a bi-objective welding shop scheduling problem (BWSSP) aiming to minimise the total tardiness and the machine interaction effect. The BWSSP is a special flow-shop scheduling problem (FSP) which is characterised by the fact that more than one machine can process on one job at a certain stage. This study analyses the operation of a structural metal manufacturing plant, and includes various aspects such as job sequence, machine-number-dependent processing time, lifting up time, lifting down time and different delivery time. A novel mixed-integer programming model (MIPM) is established, which can be used to minimise the delayed delivery time and the total machine interaction effect. One machine interaction effect formula is given in this paper. In order to solve this BWSSP, an appropriate non-dominated sorting Genetic Algorithm III (NSGAIII), embedded with a restarted strategy (RNSGAIII), is proposed. The restarted strategy, which can increase the diversity of the solutions, will be triggered with a restart probability. Following the iterative process, an effective strategy is applied to reduce the interaction effect penalty, on the premise that the makespan will remain unchanged. Total five algorithms, namely NSGAII, NSGAIII, harmony search algorithm (HSA), strength Pareto evolutionary algorithm (SPEA2), and RNSGAIII are utilised to solve this engineering problem. Numerical simulations show that the improved RNSGAIII outperforms the other methods, and the Pareto solution distribution and diversity, in particular, are significantly improved.     

求解一类无关并行机调度的遗传迭代贪心算法

以最小化最大完工时间为优化目标,建立带工单加工约束和序相关设置时间无关并行机调度问题的混合整数规划模型;考虑现实生产对求解算法在质量、收敛速度和鲁棒性等方面的较高要求,构建一种混合遗传-迭代贪心算法。在遗传变异操作中嵌入一种迭代贪心策略的破坏和构建机制,用于提高算法的种群多样性;引入基于破坏与构建操作设计而成的快速局部搜索算法来增强算法的局部开发能力;基于实际生产数据的相关特征随机生成了一系列计算案例,并通过实验说明所提新型混合算法相较于传统混合算法的优越性。     

Single machine scheduling problem with stochastic sequence-dependent setup times

In this study, we consider stochastic single machine scheduling problem. We assume that setup times are both sequence dependent and uncertain while processing times and due dates are deterministic. In the literature, most of the studies consider the uncertainty on processing times or due dates. However, in the real-world applications (i.e. plastic moulding industry, appliance assembly, etc.), it is common to see varying setup times due to labour or setup tools availability. In order to cover this fact in machine scheduling, we set our objective as to minimise the total expected tardiness under uncertain sequence-dependent setup times. For the solution of this NP-hard problem, several heuristics and some dynamic programming algorithms have been developed. However, none of these approaches provide an exact solution for the problem. In this study, a two-stage stochastic-programming method is utilised for the optimal solution of the problem. In addition, a Genetic Algorithm approach is proposed to solve the large-size problems approximately. Finally, the results of the stochastic approach are compared with the deterministic one to demonstrate the value of the stochastic solution.     

Stochastic customer order scheduling with setup times to minimize expected cycle time

Short cycle time of customer orders is crucial for companies to achieve mass customization and quick response. However, the complicated and stochastic environment, especially the exist of setup times, makes it extremely challenging to optimize the efficiency of a system. In this study, stochastic customer orders are scheduled to minimize their expect cycle time with the consideration of setup times. Customer orders arrive dynamically, and each order requires multiple product types with random workloads. These workloads will be assigned to a set of unrelated parallel machines to be processed. Particularly, for each machine, setup times are required whenever there is a product type changeover, and the lengthes are both machine- and product type-dependent. This paper intends to minimize the long-run expected order cycle time by proper policies including workload allocation and type sequencing. The impacts of product type sequence and workload variance are evaluated through theoretical study and several analytical properties are developed. With the help of these properties, three scheduling algorithms are proposed, and a lower bound is derived to evaluate the proposed algorithms. Computational experiment is conducted to demonstrate the effectiveness of the lower bound and the algorithms under various circumstances, and several important managerial insights are also provided.     

基于遗传算法的高校教室调度问题

对高校教室调度问题进行研究,能使教室和课程达到一种合理的优化配置。类似问题的研究在制造业或生产系统中都占有非常重要的地位。研究首先采用三元组方式,将教室调度问题描述为一类具有机器适用限制的并行多机问题,以最小化Cmax（即集中时间提高教室利用效率）和滞后时间总和为调度目标,建立了对应的数学模型。根据问题的特性,设计了包含分派规则和遗传算法的启发式调度算法求解该类问题。研究结合问题特性在编码,个体适应度函数,交叉及变异等方面进行了设计。以实例分析验证了所设计算法的可行性和有效性。     

考虑急件到达的单机鲁棒调度方法

实际生产过程中经常会有急件到达。由于急件的优先级最高,其到达容易扰乱初始调度,使实际调度性能恶化,影响调度目标的实现。针对以总拖期为目标且带有释放时间的单机调度问题,研究了在有急件到达情况下的鲁棒调度方法,以降低急件对实际调度性能的影响。鉴于该调度问题是NP hard问题,根据工件释放时间和交货期的关系构造“金字塔”结构,获得该调度问题的占优性质。根据这些占优性质和急件到达特点,研究急件到达情景下的占优规则,据此求解急件到达情景下的占优调度集合,作为鲁棒调度的备选调度方案集合。提出了应对急件到达的鲁棒调度算法。给出仿真算例验证了算法的有效性,算例表明本文给出的鲁棒调度方法能有效避免急件到达造成实际调度性能的恶化。      

《基于多目标遗传算法的消声器优化设计》

四端网络法结合遗传算法设计消声器。以四端子网络为理论基础建立消声器的插入损失、传递损失、噪声降低的模型,模拟计算出消声器的消声特性响应曲线。应用多目标遗传算法对得到的消声器的噪声特性进行优化,结果使消声器的消声性能得到明显的改善,可以缩短设计周期,提高效率。