柔性作业车间生产计划与调度集成优化求解策略

针对柔性作业车间(Flexible job-shop, FJS)生产计划(Production planning, PP)与调度紧密衔接的特点, 建立了生产计划与调度集成优化模型. 模型综合考虑了安全库存、需求损失及工件加工路线柔性等方面因素. 提出了一种基于拉格朗日松弛(Lagrangian relaxation, LR)的分解算法, 将原问题分解为计划子问题与调度子问题. 针对松弛的生产计划子问题, 提出一种新的费用结构, 以保证生产计划决策与实际情况相符, 并设计了一种变量固定—松弛策略与滚动时域组合算法进行求解. 对于调度子问题中的加工路线柔性问题, 提出了一种新的机器选择策略. 通过数值实验验证了模型与算法的有效性.     

炼油企业全厂生产计划系统设计与开发

采用面向对象方法,使用统一建模语言进行炼油企业生产计划系统的需求分析和系统设计。首先描述了生产计划系统的功能需求,包括数据管理、模型管理、模型发生器和过程分析等,并且采用序列图初步分析了系统核心功能生产计划的实现过程。在此基础上,进行系统的数据模型设计,包括公用数据模型,装置数据模型和物流数据模型。上述数据模型描述了炼油企业生产流程的静态结构和生产计划活动的信息。最后开发了软件OpTechPlan,并且在中国北方某炼油企业得到了初步的应用。     

A review of function blocks for process planning and control of manufacturing equipment

Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

An overview of function block enabled adaptive process planning for machining

Small- and medium-sized enterprises (SMEs) in job-shop machining are experiencing more shop-floor uncertainties today than ever before, due to multi-tier outsourcing, customised product demands and shortened product lifecycle. In a fluctuating shop floor environment, a process plan generated in advance is often found unsuitable or unusable to the targeted resources, resulting both in wasted effort spent in early process planning and in productivity drop when idle machines have to wait for operations to be re-planned. Consequently, an adaptive process planning approach is in demand. Targeting shop-floor uncertainty, the objective of this research is to develop a novel adaptive process planning method that can generate process plans at runtime to unplanned changes. This paper, in particular, presents an overview of adaptive process planning research and a new methodology, including two-layer system architecture, generic supervisory planning, machine-specific operation planning, and adaptive setup planning. Particularly, function blocks are introduced as a core enabling technology to bridge the gap between computer systems and CNC systems for adaptive machining.     

Integration of reclamation and tailings management in oil sands surface mine planning

The processing of oil sands generates large volumes of slurry, known as tailings, that is impounded in tailings ponds. Oil sands operators are committed to develop reclamation plans to ensure that the mine site is restored to a natural or economically usable landscape. Since most of the material that is needed for capping of the tailings pond is produced in mining operation, it is reasonable to include material requirement for reclamation as part of mine planning. In this paper, an integrated long-term mine planning model is proposed that includes tailings capacity and reclamation material requirements. A mixed integer linear programming (MILP) model is developed to test the performance of the proposed model. The MILP model is coded in Matlab^®. It is verified by carrying out a case study on an actual oil sands dataset, and has resulted in an integer solution within a 2% gap to the optimality. The resulted production schedule meets the capacity constraint of the tailings facility and guarantees the production of the required reclamation material.     

基于仿真的生产计划与调度系统集成

针对航天制造企业和产品生产特点,描述了一个生产计划、仿真和调度集成系统.分析了该类型企业在生产管理上存在的主要问题,描述了集成系统的框架及主要组件的功能,以及集成系统的运行机制.该系统利用仿真技术将企业的生产计划与作业调度紧密集成,实现了计划部门与生产部门在信息和流程上的紧密关联.最后通过实例验证了该集成系统的可行性与有效性.     

卷烟企业制丝车间基于仿真的生产计划与调度系统集成的研究

本文首先分析了卷烟厂制造执行系统的特点以及发展现状,描述了一个生产计划、仿真和调度集成系统。描述了集成系统的框架及主要组件的功能,以及集成系统的运行机制。该系统利用仿真技术将企业的生产计划与作业调度紧密集成,实现了计划部门与生产部门在信息和流程上的紧密关联。     

Intelligent setup planning in manufacturing by neural networks based approach

Setup planning is considered the most significant but also difficult activity in Computer Aided Process Planning (CAPP), and has a strong impact on manufacturability, product quality and production cost. Indeed, setup planning activity deserves much attention in CAPP. The setup planning in manufacturing consists mainly of three steps, namely, setup generation, operation sequence, and setup sequence. In this paper, the Kohonen self-organizing neural networks and Hopfield networks are adopted to solve such problems in setup planning efficiently. Kohonen self-organizing neural networks are utilized, according to the nature of the different steps in setup planning, to generate setups in terms of the constraints of fixtures/jigs, approach directions, feature precedence relationships, and tolerance relationships. The operation sequence problem and the setup sequence problem are mapped onto the traveling salesman problem, and are solved by Hopfield neural networks. This paper actually provides a complete research basis to solve the setup planning problem in CAPP, and also develops the most efficient neural networks based approaches to solve the setup planning problem in manufacturing. Indeed, the results of the proposed approaches work towards the optimal solution to the intelligent setup planning in manufacturing.     

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.     

基于粒子群算法的轧制批量计划问题研究

将轧制批量计划编制问题归结为车辆路径问题,采用粒子群算法对模型求解,设计了轧制批量计划问题的编码方案,阐明了算法的具体实现过程。计算结果表明,利用粒子群算法解决热轧批量计划问题是有效和可行的。     

炼油企业生产计划与重点装置工艺条件集成优化

传统的生产计划优化由于不考虑过程装置的操作优化,从而无法保证企业生产计划层与过程操作层的全局最优.为了在获得炼油企业最优生产计划的同时,确保计划优化中重点装置的操作条件可以实现,本文建立了集成装置工艺条件的炼油企业生产计划优化模型.该模型引入常减压装置侧线产品切割点温度、催化裂化装置转化率等过程工艺条件,基于物料质量平衡、产品质量指标约束等关系,进行厂级生产计划建模与求解,确定可达的装置操作条件.应用案例中重点通过与传统常减压装置侧线收率固定的生产计划方案比较,证明在满足可达的操作条件下,集成装置工艺条件操作范围的生产计划优化模型,可以实现更高的全厂利润与更优的装置收率分布,同时优化结果对炼厂实际生产更具有指导意义.     

Assembly features in modeling and planning

In recent years, features have been introduced in modeling and planning for manufacturing of parts. Such features combine geometric and functional information. Here it is shown that the feature concept is also useful in assembly modeling and planning. For modeling and planning of both single parts and assemblies, an integrated object-oriented product model is introduced. For specific assembly-related information, assembly features are used. Handling features contain information for handling components, connection features information on connections between components. A prototype modeling environment has been developed. The product model has been successfully verified within several analyses and planning modules, in particular stability analyses, grip planning, motion planning and assembly sequence planning. Altogether, feature-based product models for assembly can considerably help in both assembly modeling and planning, on the one hand by integrating single-part and assembly modeling, and on the other hand by integrating modeling and planning.     

一种改进的综合生产计划动态规划优化方法

提出了一种改进的综合生产计划动态规划优化方法。以1999年甘应爱主编的《运筹学》第227~230页给出的一类综合生产计划问题为研究对象,深入分析了原综合生产计划问题、数学优化模型、动态规划求解过程、计算方法存在的不足并提出了相应的改进措施。通过案例分析验证了所提方法的有效性。     

Multi-choice multi-objective mathematical programming model for integrated production planning: a case study

This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of a steel plant. The aim of the integrated production planning problem is to integrate the planning sub-functions into a single planning operation. The sub-functions are formulated by considering the capacity of different units of the plant, cost of raw materials from various territories, demands of customers in different geographical locations, time constraint for delivery the products, production cost and production rate at different stages of production process. Departure cost is also considered in the formulation of mathematical programming model. Some of the parameters are decided from a set of possible choices, therefore such parameters are considered as multi-choice type. Multi-choice mathematical programming problem cannot be solved directly. Therefore an equivalent multi-objective mathematical programming model is established in order to find the optimal solution of the problem. Computation of the mathematical programming model is performed with the practical production data of a plant to study the methodology.     

Modeling rolling batch planning as vehicle routing problem with time windows

This paper considers the rolling batch planning problem of grouping and sequencing a given set of slabs into several rolling units in iron and steel industry. The existing mathematical methods often used for the problem are traveling salesman problem (TSP) and vehicle routing problem (VRP), but these methods are not precise, because the position limitation of some slabs in a rolling unit scheduling is not considered. Therefore we suggest a new model, vehicle routing problem with time window (VRPTW) to describe the rolling batch planning problem, in which the position limitation of slabs are quantified as the time constraints. Several solution methods including the genetic algorithm are presented for solving the problem and the computational results show that the genetic algorithm is superior to other methods.In this paper, the vehicle routing problem with time window (VRPTW) of combinational optimization is used to analyze and model the rolling batch planning problem. Genetic algorithm and heuristic are used to solve the problem. Simulation results based on the actual production data show that this model is precise and the genetic algorithm based method is very promising.     

An expert system for process planning

Fully integrated manufacturing systems, the ultimate goal of today's production engineering research, cannot be realized without automated process planning systems. A knowledge-based expert system incorporating heuristic algorithms as well as analytical and empirical models and which can thus provide a partial replacement for human expertise is the only key to automated process planning.This paper presents an attempt to develop an expert system for automatic process planning using artificial intelligence. In this system, a structural database was incorporated with a knowledgebase for the execution of rules. The programs developed are user-friendly and highly interactive. They allow dialog between the system and the user and require only that the operator answer questions in a familiar workshop language.     

Integrating workers’ differences into workforce planning

In today’s competitive market, manufacturers need to work hard towards improving their production system performance in order to satisfy customer demands. In such a situation, most companies develop production systems that can help in quality improvement, cost reduction and throughput time reduction. In this research, we consider a workforce planning (WP) model including some human aspects such as skills, training, and workers’ personalities and motivation. A multi-objective non-linear programming model is developed in order to minimize the hiring, firing, training and overtime costs and minimize the number of fired most productive workers. The purpose is to determine the number of workers for each worker type, the number of workers trained, and the number of overtime hours. Moreover, a decision support system (DSS) based on the proposed model is introduced using the Excel-LINGO software interfacing feature. The results indicate that the proposed model can provide a promising workforce planning approach to easily apply it in practice.     

Efficient solution of the 3G network planning problem

The 3G universal mobile telecommunications system (UMTS) planning problem is combinatorially explosive and difficult to solve optimally, though solution methods exist for its three main subproblems (cell, access network, and core network planning). We previously formulated the entire problem as a single integrated mixed-integer linear program (MIP) and showed that only small instances of this global planning problem can be solved to optimality by a commercial MIP solver within a reasonable amount of time ( St-Hilaire, Chamberland, & Pierre, 2006). Heuristic methods are needed for larger instances. This paper provides the first complete formulation for the heuristic sequential method ( St-Hilaire, Chamberland, & Pierre, 2005) that re-partitions the global formulation into the three conventional subproblems and solves them in sequence using a MIP solver. This greatly improves the solution time, but at the expense of solution quality. We also develop a new hybrid heuristic that uses the results of the sequential method to generate constraints that provide tighter bounds for the global planning problem with the goal of reaching the provable optimum solution much more quickly. We empirically evaluate the speed and solution accuracy of four solution methods: (i) the direct MIP solution of the global planning problem, (ii) a local search heuristic applied to the global planning problem, (iii) the sequential method and (iv) the new hybrid method. The results show that the sequential method provides very good results in a fraction of the time needed for the direct MIP solution of the global problem, and that optimum results can be provided by the hybrid heuristic in greatly reduced time.