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.     

钢铁一体化生产多目标合同计划建模与算法

为了实现热装比最大等多个优化目标,将炼钢-连铸-热轧一体化生产过程,抽象为炼钢与热轧两大加工阶段,建立了一体化生产多目标合同计划模型.以板坯热装比最大、交货提前/拖期率最小和组炉余材最少为优化目标,综合考虑了炼钢产能、热轧产能、最小主体材产量、以及钢种、板坯和成品规格等约束条件.通过变异目标空间中的重合个体,以及在每一代增加若干个新个体的方法,对非支配排序遗传算法NSGA-Ⅱ (non-dominated sorting genetic algorithm)进行了改进,提高了种群的多样性.不同规模计划问题的计算结果表明了所建立模型和对NSGA-Ⅱ算法的改进是有效的.     

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MES中的生产计划管理模式

介绍了宝钢四层架构的计算机系统生产计划与调度管理功能,重点分析了钢铁企业制造执行系统MES中常见的两种计划管理模式,并结合目前两种计划管理模式实际运行情况,分析了两种计划管理模式的特点。计划钢卷顺序一致,有利于计划一体化的需求,适用于物流复杂,对加工顺序要求高的管理系统。MES,PCS计划顺序不一致模式电文处理逻辑简单,ISPS-MES-PCS管理界面清晰,适用于物流简单,对生产顺序要求不高的管理系统。两种计划管理模式,对流程型工业特别是钢铁企业MES系统开发具有一定的借鉴意义。     

钢铁企业生产资源平衡计划系统分析与设计

针对市场需求剧烈变动环境下,钢铁企业如何利用有限产能、平衡资源分配、优化产品组合的问题,提出了通过生产资源平衡计划系统来解决的方法.在系统功能需求分析的基础上,通过数据流图、实体关系图、用例图及时序图构建钢铁生产资源平衡计划系统模型,详细描述系统的设计目标、业务流程及子模块协作机制.该系统以基于数学优化和智能计算的优化计算引擎为核心,综合考虑盈利指标、市场需求、生产能力等因素进行资源平衡,为生产与销售提供决策支持.     

可延迟供货的冷轧生产库存问题之建模与优化

本文针对可延迟供货的冷轧生产系统,建立了以最小化库存成本、拖期惩罚和启动成本为目标的多阶段生产库存模型,模型中充分考虑了工序不允许停机的情况以及计划与调度之间的一致性问题.同时开发了基于变量分离的有效拉格朗日松弛求解算法,并使用120个基于实际生产数据的算例进行了仿真实验,计算结果显示该算法能够在合理的时间内得到高质量的解.     

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.

     

基于RSAPSO算法的网络计划综合优化

为了提高电力工程企业的经济效益,在综合考虑成本、质量和进度的基础上,提出了工期-收益-质量多目标优化模型.粒子群优化算法是基于群体智能理论的算法.该算法利用生物群体内个体的合作与竞争等复杂性行为产生群体智能,并为工程优化问题提供高效的解决方法.但是粒子群优化算法同样存在一些问题,针对这些问题提出了一种新算法,即基于速度松弛策略的模拟退火粒子群算法(RSAPSO).运用RSAPSO算法对多目标优化模型进行求解,最后通过工程实例验证模型和算法的有效性.     

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。     

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 multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

This paper investigates one of the key decision-making problems referring to the integrated production planning (IPP) for the steelmaking continuous casting-hot rolling (SCC-HR) process in the steel industry. The complexities of the practical IPP problem are mainly reflected in three aspects: large-scale decision variables; multiple objectives and interval-valued uncertain parameters. To deal with the difficulty of large-scale decision variables, we introduce a new concept named “order-set” for modeling. In addition, considering the multiple objectives and uncertainties of the given IPP problem, we construct a multi-objective optimization model with interval-valued objective functions to optimize the throughput of each process, the hot charge ratio of slabs, the utilization rate of tundishes and the additional cost of technical operations. Furthermore, we propose a novel approach based on a modified interval multi-objective optimization evolutionary algorithm (MI-MOEA) to solve the problem. The proposed model and algorithm were tested with daily production data from an iron and steel company in China. Computational experiments demonstrate that the proposed method generates quite effective and practical solutions within a short time. Based on the IPP model and MI-MOEA, an IPP system has been developed and implemented in the company.     

基于多目标分阶段的跑道调度计划算法*

针对多条跑道环境下离港飞机调度问题,提出了一种基于多目标、两阶段算法。算法第一阶段以飞机重量类型为主要分解参数,生成离港飞机序列。该参数在跑道调度计划问题上比其他参数更具影响力和稳定性。算法第二阶段从离港飞机队列池中选取可用序列,将特殊航班指配到目标类型序列中,生成优化的飞行航班时刻表。实验表明,采用两阶段跑道调度计划算法进行多跑道离港飞机调度比采用先来先服务算法调度在跑道总吞吐量上有明显改善,能有效降低机场航班延误,提高跑道运营效率。     

A new decision making structure for managing arriving orders in MTO environments

In order to improve the quality of decision about orders incoming to make to order (MTO) company, an effective evaluation approach is essential. So, in this paper a comprehensive decision making structure is presented for acceptance or rejection of incoming orders. The aim of the proposed structure is to manage the arriving orders so that the MTO system just proceeds to produce those arriving orders which are feasible and profitable for the system. The proposed structure composed of three phases. At the first phase, arriving orders are prioritized into high and low priority orders, considering characteristics of order and customer and utilizing technique for order performance by similarity to ideal solution (TOPSIS). At the second phase, rough-cut capacity was calculated for each order regarding priority level and so, acceptance or rejection decision is taken based on it. Finally, at the third phase, the previous phase accepted orders are evaluated based on their due dates and material arrival times and final decisions for orders are made. At the end, the effectiveness of the proposed structure is demonstrated through a case study.     

钢铁企业年度生产计划的研究

针对多分厂、多机型的钢铁企业年度生产计划问题,建立一个以提高经济效益为目的,包含生产工艺、资源和能力等约束的多层递阶优化数学模型,并研究了模型的算法．该算法首先从产品结构的角度将多层问题转换为两层模型,然后对此两层模型利用遗传算法和二阶段法进行求解．仿真结果表明了该模型和算法的有效性和可行性．     

A modified particle swarm optimization for aggregate production planning

Particle swarm optimization (PSO) originated from bird flocking models. It has become a popular research field with many successful applications. In this paper, we present a scheme of an aggregate production planning (APP) from a manufacturer of gardening equipment. It is formulated as an integer linear programming model and optimized by PSO. During the course of optimizing the problem, we discovered that PSO had limited ability and unsatisfactory performance, especially a large constrained integral APP problem with plenty of equality constraints. In order to enhance its performance and alleviate the deficiencies to the problem solving, a modified PSO (MPSO) is proposed, which introduces the idea of sub-particles, a particular coding principle, and a modified operation procedure of particles to the update rules to regulate the search processes for a particle swarm. In the computational study, some instances of the APP problems are experimented and analyzed to evaluate the performance of the MPSO with standard PSO (SPSO) and genetic algorithm (GA). The experimental results demonstrate that the MPSO variant provides particular qualities in the aspects of accuracy, reliability, and convergence speed than SPSO and GA.     

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.