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.

     

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.􀁱     

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。     

钢厂合同计划的模型与算法

通过对钢厂生产过程的分析,建立了合同计划编制的整数规划模型,并提出用基于可重复自然数编码和三变异算子的遗传算法对模型进行求解。以热轧厂合同计划编制为例进行实验,结果表明,此模型符合生产实际,获得的结果优于人机交互系统,求解算法比较有效。     

钢铁企业按订单生产模式下合同池优化

基于对钢铁企业按订单生产模式下合同月计划编制策略、约束条件和优化目标的研究,建立了合同月计划优化模型.模型以最大化编入计划的加权合同重量为优化目标,综合考虑了设备能力和合同月计划中各种产品规格、品种比例限制等约束.采用惩罚函数法将约束最优化模型转换为无约束最优化模型.设计了求解模型的向导局域搜索算法.以某钢铁企业的实际合同月计划问题作为实例,对模型和算法进行了系统地验证,计算结果表明模型和算法在优化效果和运算效率方面均优于该企业现有合同计划系统.     

基于MTO管理系统的钢厂合同计划方法

构造了钢铁企业的ＭＴＯ管理系统,建立了钢铁企业合同计划编制的整数规划模型,并提出用基于可重复自然数编码和三变异算子的遗传算法对模型进行求解。以热轧厂合同计划编制为例进行实验,结果表明模型符合生产实际,获得的结果优于人机交互系统,求解算法是有效的。     

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.     

新产品开发多项目协同计划与资源冲突的动态再匹配实现方法*

在有供应商参与的大批量制造企业多项目协同计划的执行过程中,可能会出现的资源颗粒或项目任务的变更,导致广义设计资源颗粒与多项目计划任务匹配出现的冲突,使原协同计划无法继续进行。针对这一问题,提出了在原协同计划执行过程中进行多项目计划与广义设计资源的动态再匹配的思想,建立了各种变更情况下的动态再匹配的模型及算法,以保证已启动的新产品开发多项目计划能顺利执行。用一实例对所建模型和动态再匹配算法进行了验证,证明所提出的方法是可行的。     

易变质产品的生产计划与作业排序集成优化研究

讨论了一类针对易变质产品生产批量计划与作业排序的集成优化问题,以最小化库存成本、变质成本、缺货成本、加班成本之和作为目标函数并建立了混合整数规划模型,采用协同进化遗传算法进行求解,即通过迁移算子把协同进化算法和遗传算法有机联系起来,加强算法的寻优能力和收敛性能,最后通过仿真实验,分析自身进化结果,同时与遗传算法对比结果,验证了算法的性能。     

汽车装配线生产计划与调度的集成优化方法

为提高汽车装配线的生产效率,优化资源配置,研究了汽车装配线生产计划和调度的集成优化问题,给出了该问题的混合整数规划模型.利用分枝定界算法和单纯型法求得问题的粗生产计划.通过将模拟退火算法和快速调度仿真相结合,探讨了一种新的启发式算法.然后基于已求得的粗生产计划,针对三种不同寻优组合论述了该算法的实现.将该算法应用于实际算例,仿真结果表明该算法对求解此类问题有着很好的效果.     

Multi-level inventory matching and order planning under the hybrid Make-To-Order/Make-To-Stock production environment for steel plants via Particle Swarm Optimization

This paper proposes a nonlinear integer programming model which co-optimizes the multi-level inventory matching and order planning for steel plants while combining Make-To-Order and Make-To-Stock policies. The model considers order planning and inventory matching of both finished and unfinished products. It combines multiple objectives, i.e., cost of earliness/tardiness penalty, tardiness penalty within delivery time window, production cost, inventory matching cost, and order cancelation penalty. This paper also proposes an improved Particle Swarm Optimization (PSO) method, where strategies to repair infeasible solutions and inventory-rematching scheme are introduced. Parameters of PSO and the rematching scheme are also analyzed. Three sets of real data from a steel manufacturing company are used to perform computational experiments for PSO, local search, and improved PSO. Numerical results show the validity of the model and efficacy of the improved PSO method.     

A distributed hierarchical genetic algorithm for efficient optimization and pattern matching

In this paper we propose a new approach in genetic algorithm called distributed hierarchical genetic algorithm (DHGA) for optimization and pattern matching. It is eventually a hybrid technique combining the advantages of both distributed and hierarchical processes in exploring the search space. The search is initially distributed over the space and then in each subspace the algorithm works in a hierarchical way. The entire space is essentially partitioned into a number of subspaces depending on the dimensionality of the space. This is done in order to spread the search process more evenly over the whole space. In each subspace the genetic algorithm is employed for searching and the search process advances from one hypercube to a neighboring hypercube hierarchically depending on the convergence status of the population and the solution obtained so far. The dimension of the hypercube and the resolution of the search space are altered with iterations. Thus the search process passes through variable resolution (coarse-to-fine) search space. Both analytical and empirical studies have been carried out to evaluate the performance between DHGA and distributed conventional GA (DCGA) for different function optimization problems. Further, the performance of the algorithms is demonstrated on problems like pattern matching and object matching with edge map.     

Design of steel roofs subjected to drifted snow using genetic optimization

Formed metal deck roofs play an important role in steel building construction. There are many design variables in deck design to make it important for designers to put the right combination together to achieve a better design. In this paper, a procedure for the design of steel roofs subjected to non-uniform loads such as drifted snow is presented. The presented approach uses genetic algorithms to achieve optimal or near-optimal designs. The advantages of the model stem from two main points: (1) it searches for the optimum or near-optimum number of purlins for the roof; and (2) it determines the near-optimum spacing between purlins to minimize the structure weight. Details of model development are described and example applications are presented to demonstrate the model capabilities.     

原材料价格波动下钢铁生产库存多期优化模型

考虑钢铁企业钢材生产受钢材需求和生产变动成本波动的双重影响,在预测基础上,建立钢材生产库存多期动态优化模型。由于模型涉及多种钢材及多个时段,属于大规模问题,求解困难,且为了避免粒子群算法陷入局部最优,提出采用模拟退火与粒子群组合智能算法对模型进行求解。最后通过钢铁企业L的案例,结果表明算法具有较强的收敛性和适用性,模型可用于解决钢铁企业多期生产实际问题。     

Multidisciplinary and multiple operating points shape optimization of three-dimensional compressor blades

The recent progress in simulation technologies in several fields such as computational fluid dynamics, structures, thermal analysis, and unsteady flow combined with the emergence of improved optimization algorithms makes it now possible to develop and use automatic optimization software and methodologies to perform complex multidisciplinary shape optimization process. In the present applications, the MAX optimization software developed at CENAERO is used to perform the optimization. This software allows performing derivative free optimization with very few calls to the computer intensive simulation software. The method employed in this paper combines the use of a genetic algorithm (with real coding of the variables) to an approximate (or meta) model to accelerate significantly the optimization process. The performance of this optimization methodology is illustrated on the optimization of three-dimensional turbomachinery blades for multiple operating points and multidisciplinary objectives and constraints. The NASA rotor 67 geometry is used to demonstrate the capabilities of the method. The aim is to find the optimal shape for three different operating conditions: one at a near peak efficiency point, one at choked mass flow, and one near the stall flow. The three points are analyzed at the same blade rotational speed but with different mass flows. A finite element structural mechanics software is used to compute the static and dynamic mechanical responses of the blade. A Navier–Stokes solver is used to calculate the aerodynamic performance. High performance computers (HPC) are used in this application. Cenaero’s HPC infrastructure contains a Linux cluster with 170 3.06 GHz Xeon processors. The optimization algorithm is parallelized using MPI.     

Path planning of a mobile robot by optimization and reinforcement learning

At AROB5, we proposed a solution to the path planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term, and a collision term. While the results of our simulation showed the effectiveness of our approach, the values of the weights in the objective function were not given by any theoretical method. This article presents a theoretical method using reinforcement learning for adjusting the weight parameters. We applied Williams' learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. We verified the learning rule by some experiments. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001     

Proposal and validation of an optimization method using Monte Carlo method for multi-objective functions

Solving optimization problems is essential for many engineering applications and research tools. In a previous report, we proposed a new optimization method, MOST (Monte Carlo Stochastic Optimization), using the Monte Carlo method, and applied it to benchmark problems for optimization methods, and optimization of neural network machine learning. While the proposed method MOST was a single objective, this study is an extension of MOST so that it can be applied to multi-objective functions for the purpose of improving generality. As the verification, it was applied to the optimization problem with the restriction condition first, and it was also applied to the benchmark problem for the multi-objective optimization technique verification, and the validity was confirmed. For comparison, the calculation by genetic algorithm was also carried out, and it was confirmed that MOST was excellent in calculation accuracy and calculation time.     

Integration of process planning and scheduling—A modified genetic algorithm-based approach

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, a new integration model and a modified genetic algorithm-based approach have been developed to facilitate the integration and optimization of the two functions. In the model, process planning and scheduling functions are carried out simultaneously. In order to improve the optimized performance of the modified genetic algorithm-based approach, more efficient genetic representations and operator schemes have been developed. Experimental studies have been conducted and the comparisons have been made between this approach and others to indicate the superiority and adaptability of this method. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling.     

一类Job- shop 车间生产计划和调度的集成优化

讨论一类Job—shop车间的生产计划和调度的集成优化问题，给出了该问题的非线性混合整数规划模型，并采用混合遗传算法进行求解。该模型利用调度约束来细化生产计划，以保证得到可行的调度解。在混合算法中，利用启发式规则来改善初始解集，并采用分段编码策略将计划和调度解映射为染色体。算例研究表明，该算法对求解该类问题具有很好的效果。