MTO 管理模式下钢铁企业生产合同计划建模与优化

基于对钢铁企业MTO管理模式下合同计划的编制策略、约束条件和优化目标的研究，建立了合同计划优化模型，模型综合考虑了拖期惩罚费用、设备能力均衡利用和库存成本等优化目标，并采用加权法将多目标优化模型转换为单目标优化模型，针对模型的特点设计了求解模型的特殊PSO算法，以某钢铁企业的实际合同计划问题作为实例，在算法不同参数组合下进行了系统的测试，实验结果表明模型和算法是令人满意的。     

热轧带钢轧制批量计划优化模型及算法

基于奖金收集车辆路径问题模型建立了热轧带钢生产批量计划多目标优化模型．模型综合考虑了生产工艺约束、用户合同需求以及综合生产指标优化等因素．利用加权函数法将多目标优化模型转换为单目标优化模型,针对模型特点设计了蚁群优化求解算法,算法中嵌入了单向插入和2-opt局部搜索过程．引用某钢铁企业热轧生产轧制批量计划编制的实际问题对模型和算法进行了验证,结果表明模型和算法的优化效果和时间效率是令人满意的．     

基于遗传搜索算法热轧合同编制优化仿真研究

为解决钢铁企业多品种、小批量的热轧合同编制优化问题,针对规模大、约束复杂难以建模及求解等难点,以半旬为基本时间单位,在考虑各钢种炼钢能力、轧制能力等约束条件的基础上,建立以合同的提前期、拖期惩罚最小,各工序产能利用均衡,相邻排产合同的工艺约束惩罚费用最小以及各半旬的炼钢余材最少为优化目标的0-1非线性整数规划模型.由于所建模型具有多旅行商问题结构的特征及模型中约束条件复杂、数据规模较大,采用分段整数编码和启发式修复策略的遗传搜索算法进行求解.通过对实际生产数据进行仿真,验证了所提模型和算法的有效性,为科学合理地编制热轧合同计划提供了有效的解决方法.     

基于PCTSP的热轧单元计划模型与算法

根据钢铁企业热轧产品生产工艺约束条件,将热轧生产轧制单元计划模型归结为奖金收集旅行商问题,设计了蚁群最优化算法对模型进行求解.引用某钢铁企业热轧生产轧制单元计划编制的实际问题对模型和算法进行了验证,并与遗传算法的求解结果进行了对比.实验结果表明模型和算法的优化效果和时间效率都是令人满意的.该模型和算法经过改进后可应用到包含多个轧制单元计划的轧制批量计划优化问题中.     

考虑生产路径柔性的钢铁合同计划编制研究

在钢铁企业MTO管理模式下,通过对生产过程和质量设计的分析,提出了一种考虑生产路径柔性的合同计划编制新方法,建立了以工序能力均衡为目标的周合同计划编制模型,以此模型为基础研究质量设计生产路径柔性对合同计划工序负荷能力影响.针对模型特点,设计了改进变邻域搜索的模型求解算法,算法采用模拟退火的Metropolis准则,防止算法陷入局部最优.应用钢厂实际数据进行数字运算,结果表明,合理选择质量设计生产路径可以大幅度提高合同计划工序负荷率,验证了所提方法合理、有效.     

钢铁企业中库存匹配与生产计划联合优化模型与算法

针对钢铁企业在MTO与MTS混合生产组织方式下存在的库存匹配与生产计划问题,按照集成 化管理思想,将两项工作综合考虑,建立了以合同的违约惩罚、生产准备费用、库存匹配费用总额最小化为目标的联合优化模型.结合问题的特点,构造了具有启发式修复策略的改进遗传算法.通过实例仿真证明了模型与算法的有效性和可行性.􀁱     

钢铁企业合同计划与余材匹配的集成优化方法

钢铁企业的合同计划和余材匹配的集成优化是解决钢铁企业面向订单生产的关键技术.由于该问题复杂,涉及因素多,求解难度大,对此提出一个带有提前拖期惩罚的联合计划优化的数学模型,并提出一种嵌有"优先适合启发式"的遗传算法.该方法利用背包问题的求解思路改进了染色体的性能,从而加快了遗传算法的求解速度.将该模型及算法应用于实际钢铁企业的计划编排中,取得了满意的效果.     

考虑生产路径柔性的钢铁合同计划编制研究

在钢铁企业MTO管理模式下,通过对生产过程和质量设计的分析,提出了一种考虑生产路径柔性的合同计划编制新方法,建立了以工序能力均衡为目标的周合同计划编制模型,以此模型为基础研究质量设计生产路径柔性对合同计划工序负荷能力影响。针对模型特点,设计了改进变邻域搜索的模型求解算法,算法采用模拟退火的Metropolis准则,防止算法陷入局部最优。应用铜厂实际数据进行数字运算,结果表明,合理选择质量设计生产路径可以大幅度提高合同计划工序负荷率,验证了所提方法合理、有效．     

钢铁企业APS优化引擎的软件模型研究及应用*

分析了钢铁企业APS理论和应用研究现状,总结了钢铁生产计划体系中的优化问题和求解方法,在此基础上,提出了钢铁企业APS优化引擎的软件模型ISMA。模型以通用性、独立性、可重用性和可扩展性为目标,分为接口层、求解层、模型层和算法层,阐述了各层的分工和协作方式。最后以钢轧一体化批量计划编制问题为例,说明了模型的应用方法。     

钢铁生产中的可用能力承诺模型与算法

针对钢铁企业订单排产中的可用能力承诺问题,建立了以对合同交货期的提前和拖期惩罚最小化为目标,考虑钢铁生产中工序间最大和最小间隔等特殊约束的数学模型;针对所建立的模型,结合问题本身的特点设计了基于遗传算法的求解方法,并通过仿真实验验证了该算法的可行性和有效性.     

基于PER-APF算法的无人驾驶汽车换道轨迹规划

针对传统人工势场算法在解决无人驾驶汽车换道轨迹规划过程中存在的不足,提出一种基于势能重构人工势场 (Potential Energy Reconstruction- Artificial Potential Field, PER-APF) 的无人驾驶汽车换道轨迹规划算法。首先,建立了具有斥力区分的道路边界约束条件和多约束换道轨迹规划模型,通过判断障碍车辆与道路边沿的距离来保证换道过程的安全性与有效性;其次,提出了基于势能重构的改进APF算法,通过构建虚拟区域以及重构物理势能力场,有效的解决了目标不可达以及局部最优问题。仿真结果表明,所设计的PER-APF算法能够快速有效地为无人驾驶汽车规划一条安全合理的换道轨迹。     

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.     

基于群智能-一致性理论的无人机编队全过程飞行航迹规划方法

针对无人机编队执行任务全过程飞行规划问题,提出一种基于多步粒子群优化的无人机编队航迹规划算法.首先,对无人机和执行任务策略进行建模,将编队执行任务全过程划分为编队成形、执行任务、返航、解散和无人机降落5个阶段,设计不同阶段的飞行策略;其次,针对不同的终端约束条件,设计多类多层优化指标,提出多步粒子群算法,并引入模型预测控制滚动优化航路点,得到适用于不同阶段的能严格满足约束条件的航路规划方法;然后,建立旋转坐标系,将航路点信息转换为编队控制律中的理想航向和高度信息,得到能通过航路点的编队控制算法;最后,利用编队控制算法去执行航路规划方法给出的航路点,生成航迹,得到编队航迹规划算法.仿真结果表明,所提规划方法比传统方法更适用于编队飞行,能为编队规划执行任务全过程的平滑航迹,具有良好的通用性.     

Robust distributed model predictive control

This paper presents a formulation for distributed model predictive control (DMPC) of systems with coupled constraints. The approach divides the single large planning optimization into smaller sub-problems, each planning only for the controls of a particular subsystem. Relevant plan data is communicated between sub-problems to ensure that all decisions satisfy the coupled constraints. The new algorithm guarantees that all optimizations remain feasible, that the coupled constraints will be satisfied, and that each subsystem will converge to its target, despite the action of unknown but bounded disturbances. Simulation results are presented showing that the new algorithm offers significant reductions in computation time for only a small degradation in performance in comparison with centralized MPC.     

An implementation of improved harmony search algorithm for scenario-based transmission expansion planning

In this paper a new scenario-based framework is presented for transmission expansion planning (TEP) under normal and N–1 conditions. The proposed framework takes into account cost of network losses, cost of the transmission circuits and substations in the optimization process as objective functions, while considers short-term and also long-term constraints under normal and N–1 conditions as problem constraints. The proposed model is a non-convex optimization problem having a non-linear mixed-integer nature. A new improved harmony search algorithm (IHSA) is used in order to obtain the final optimal solution. The IHSA is a recently developed optimization algorithm which imitates the music improvisation process. In this process, the harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly planning methodology has been demonstrated on the well-known Garver’s 6-bus test system and a real life network of south Brazilian electric power grid in order to demonstrate the feasibility and capabilities of the proposed algorithm. The detailed results of the case studies are presented and thoroughly analyzed. The obtained TEP results illustrate the sufficiency and profitableness of the newly developed method in expansion planning when compared with other methods.     

基于改进蚁群算法的铁路乘务排班计划编制

为了提升铁路乘务排班计划编制的质量和效率,将乘务排班计划编制问题抽象为单基地、考虑中途休息的多旅行商问题（MTSP）,建立以排班周期最小、乘务交路间冗余接续时间分布最均衡为优化目标的单一循环乘务排班计划数学模型,并针对该模型提出了一种启发式修正蚁群算法。首先,构建满足时空约束的解空间,分别对乘务交路节点和接续路径设置信息素浓度;然后,确定基于修正的启发式信息,规定蚂蚁按乘务交路顺序依次出发,使蚂蚁遍历所有乘务交路;最后,从不同的乘务排班方案中选择最优的排班计划。以广深城际铁路为例对所提模型及算法进行验证,并与粒子群算法进行对比。实验结果表明：在相同的模型条件下,采用启发式修正蚁群算法编制的乘务排班计划平均月工时降低了8.5%,排班周期降低了9.4%,乘务人员超劳率为0。所提模型和算法能够压缩乘务排班周期,降低乘务成本,均衡工作量,避免乘务人员超劳。     

基于深度邻域搜索PSO 算法的装配序列优化问题

针对产品结构特征建立几何约束矩阵, 以最大化满足几何约束条件装配次数和最小化装配方向改变次数为目标, 研究产品装配序列优化问题. 利用值变换的粒子位置和速度更新规则, 基于具有随机性启发式算法产生初始种群, 提出一种带有深度邻域搜索改进策略的粒子群算法解决装配序列问题. 通过装配实例验证了所提出算法的性能并对装配序列质量进行了评价, 所得结果表明了该算法在解决装配序列优化问题上的有效性与稳定性.

     

Application of ant colony optimization algorithm in process planning optimization

One objective of process planning optimization is to cut down the total cost for machining process, and the ant colony optimization (ACO) algorithm is used for the optimization in this paper. Firstly, the process planning problem, considering the selection of machining resources, operations sequence optimization and the manufacturing constraints, is mapped to a weighted graph and is converted to a constraint-based traveling salesman problem. The operation sets for each manufacturing features are mapped to city groups, the costs for machining processes (including machine cost and tool cost) are converted to the weights of the cities; the costs for preparing processes (including machine changing, tool changing and set-up changing) are converted to the ‘distance’ between cities. Then, the mathematical model for process planning problem is constructed by considering the machining constraints and goal of optimization. The ACO algorithm has been employed to solve the proposed mathematical model. In order to ensure the feasibility of the process plans, the Constraint Matrix and State Matrix are used in this algorithm to show the state of the operations and the searching range of the candidate operations. Two prismatic parts are used to compare the ACO algorithm with tabu search, simulated annealing and genetic algorithm. The computing results show that the ACO algorithm performs well in process planning optimization than other three algorithms.