Using genetic algorithms for the optimization of mechanisms

This work examines the possibility of using genetic algorithms for the optimization of the loads transmitted in mechanisms. The variables of design are the relative positions of the various connections, considered in a comparative manner. The minimization of the loads transmitted in the connections is achieved by optimizing the respective positions of those lead to less expensive solutions for bearings and sections of beams. The examples show that using this stochastic method is an efficient way to minimize loads in mechanisms.     

两级分销网络选址——配送问题的模型及算法

为合理设计企业分销网络,提出两级分销网络选址一配送问题,研究在由工厂、配送中心和客户组成的且只销售一种产品的两级分销网络中,从若干候选点里确定已知个数的配送中心的位置以及工厂到配送中心和配送中心到客户的配送方案,使得在满足工厂和配送中心能力限制、客户产品需求、配送中心最小转运量和最小配送量的前提下,包括配送中心选址固定费用、从工厂到配送中心和配送中心到客户的配送费用在内的总费用最小,构建该问题的混合整数线性规划模型,并设计0-1和优先权混合编码的遗传算法。通过对1个演示算例和1组包括2～10个工厂、5-100个配送中心候选点、20-400个客户、6～20个需建设配送中心数的随机生成算例的计算分析,表明了所提方法的可行性和有效性。     

Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria

In textile industries, production facilities are established as multi-stage production flow shop facilities, where a production stage may be made up of parallel machines. This known as a flexible or hybrid flow shop environment. This paper considers the problem of scheduling n independent jobs in such an environment. In addition, we also consider the general case in which parallel machines at each stage may be unrelated. Each job is processed in ordered operations on a machine at each stage. Its release date and due date are given. The preemption of jobs is not permitted. We consider both sequence- and machine-dependent setup times. The problem is to determine a schedule that minimizes a convex combination of makespan and the number of tardy jobs. A 0–1 mixed integer program of the problem is formulated. Since this problem is NP-hard in the strong sense, we develop heuristic algorithms to solve it approximately. Firstly, several basic dispatching rules and well-known constructive heuristics for flow shop makespan scheduling problems are generalized to the problem under consideration. We sketch how, from a job sequence, a complete schedule for the flexible flow shop problem with unrelated parallel machines can be constructed. To improve the solutions, polynomial heuristic improvement methods based on shift moves of jobs are applied. Then, genetic algorithms are suggested. We discuss the components of these algorithms and test their parameters. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.     

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making

Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.     

基于层次分析法的配电网建设项目投资决策评价方法

分析配电网建设投资决策影响因素,选定相关技术经济指标建立了高压配电网建设项目投资决策评价指标体系;利用层次分析法开展综合投资决策分析,以确定最优投资方案。以具体算例验证了评价模型及评价方法的实用性。     

基于遗传神经网络的压铸机座板结构优化设计

在对压铸机合模机构进行结构设计时,利用神经网络的非线性映射能力,通过少量样本的有限元分析结果,训练出表述结构参数间函数关系的神经网络模型,然后利用遗传算法的全局寻优性找到神经网络模型表述的目标函数的最优结构参数,从而解决结构优化设计的瓶颈和智能问题,利用这种优化设计策略,设计了压铸机合模机构座板,结果表明了该方法的高效性。     

Performance Measurement in a Supply Chain

The supply chain is an important element in logistics development for all industries. It can improve efficiency and effectiveness of not only product transfer, but also information sharing between the complex hierarchy of all the tiers. There is no systematic grouping of the different performance measures in the existing literatures. This paper presents the formulisation of both quantitative and qualitative performance measurements for easy representation and understanding. Apart from the common criteria such as cost and quality, five other performance measurements are defined: resource utilisation; flexibility; visibility; trust; and innovativeness. In particular, new definitions are developed for visibility, trust, and innovativeness. Details of choices of these performance measurements are listed and suggested solutions are given, with the hope that a full picture of supply chain performance measurements is developed. In addition, a multi-attribute decision-making technique, an analytic hierarchy process (AHP), is used to make decisions based on the priority of performance measures. This paper outlines the application and particularly the pairwise comparison which helps to identify easily the importance of different performance measurements. An example from the electronic industry is used to demonstrate the AHP technique. ID="A1"Correspondance and offprint requests to: Dr F. T. S. Chan, Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong. E-mail: ftschan@hkucc.hku.hk     

A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach

A supply chain (SC) distribution network design model is developed in this paper. The goal of the model is to select the optimum numbers, locations and capacity levels of plants and warehouses to deliver products to retailers at the least cost while satisfying desired service level to retailers. A maximal covering approach is used in statement of the service level. The model distinguishes itself from other models in this field in the modeling approach used. Because of somewhat imprecise nature of retailers’ demands and decision makers’ (DM) aspiration levels for the goals, a fuzzy modeling approach is used. Additionally, a novel and generic interactive fuzzy goal programming (IFGP)-based solution approach is proposed to determine the preferred compromise solution. To explore the viability of the proposed model and the solution approach, computational experiments are performed on realistic scale case problems.     

基于改进遗传算法的工艺路线优化

工艺路线优化是实现计算机辅助工艺设计的关键技术之一,遗传算法是一种能较好解决工艺路线优化的方法。介绍了遗传算法及其在工艺路线优化中的应用,分析了目前流行的遗传算法应用于工艺路线优化中存在的问题,提出了改进的遗传算法,并给出了相应实例予以证明,较好解决了工艺路线优化问题。     

遗传算法在函数优化问题中的应用研究

系统地论述了遗传算法在函数优化问题中的应用，提出了基于遗传算法的函数优化问题的通用框架，通过实例的分析及计算，得出较为理想的结果。     

Predicting stress distribution in cold-formed material with genetic programming

In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.     

Joint optimization of spare parts inventory and maintenance policies using genetic algorithms

In general, the maintenance and spare parts inventory policies are treated either separately or sequentially in industry. However, since the stock level of spare parts is often dependent on the maintenance policies, it is a better practice to deal with these problems simultaneously. In this study, a simulation optimization approach using genetic algorithms (GAs) has been proposed for the joint optimization of preventive maintenance (PM) and spare provisioning policies of a manufacturing system operating in the automotive sector. A factorial experiment was carried out to identify the best values for the GA parameters, including the probabilities of crossover and mutation, the population size, and the number of generations. The computational experiments showed that the parameter settings given by the proposed approach achieves a significant cost reduction while increasing the throughput of the manufacturing system.     

基于遗传神经网络的电火花加工效果的预测

针对电火花加工非线性及复杂性的特点,提出了基于遗传神经网络的电火花加工效果的预测模型.遗传神经网络(GA-BP)是针对BP算法易陷入局部极小、收敛速度慢的缺点,根据遗传算法具有很强的全局搜身能力的特点,将二者结合起来形成一种训练神经网络的混合算法.通过该预测模型对一定加工条件下的加工速度和工件表面粗糙度进行预测,预测结果与实际实验结果有较好的一致性,说明遗传神经网络对电火花加工效果的预测是有效的.     

模糊评价在汽车覆盖件冲压工艺方案优选中的应用

考虑影响汽车覆盖件冲压工艺方案评价的因素复杂多样,建立了汽车覆盖件冲压工艺方案评价模型。设计了6项评价指标并根据层次分析法得出了各项指标的权重,最后应用模糊评价法对各种方案进行了评价,得出了最优方案,从而提高了方案优选工作的科学性、智能性。     

Using genetic algorithms on facilities layout problems

This article utilises a scheme to solve both equal and unequal area department problems in facilities layout by using genetic algorithms (GAs) for achieving the minimal total layout cost (TLC). With regards the equal area department problems, the objective function is mainly developed according to the measurement of the material flow factor cost (MFFC). However, the objective function for unequal area department problems in this study is a multiple objective function involving MFFC, shape ratio factor, and area utilisation factor to reach minimal TLC. In addition, a rule-based expert system is implemented to create space-filling curves for connecting each unequal area department to be continuously placed without discontinuity (partition). There is no gap between each unequal area department. The experimental results show that the proposed approach achieves lower TLC compared with existing algorithms. Under the practical limitations, the proposed approach in this study is much more feasible for dealing with the facilities layout problems in the real world.     

求解FJSP的混合遗传一蚁群算法

为更有效地求解柔性作业车间调度问题,综合考虑其中的机器分配与工序排序问题,建立了相关析取图模型,提出一种混合遗传一蚁群算法。该算法首先通过遗传算法获取问题的较优解,据此给出蚁群算法的信息素初始分布;之后充分利用蚁群算法的正反馈性进行求解,采用精英策略对蚁群的信息素进行局部更新;最后借鉴遗传算法交叉算子的邻域搜索特性扩大蚁群算法解的搜索空间,从而改善解的质量。通过3个经典算例的实验仿真,以及与其他算法的比较,验证了所提算法的可行性与有效性。     

CAPP系统中基于AHP的排序优先级研究

CAPP是CAD/CAM集成的桥梁和纽带,由于工艺决策问题约束复杂、随机性强,排序准则的确定需要根据具体的加工环境和加工对象来制定。为了便于工艺路线的自动生成,本文采用AHP(层次规划法),一种数学决策模型,进行排序优先级的确定。首先介绍了AHP模型及其构造算法,然后,以回转体零件在数控加工中心上的工步排序方案决策为例,用Visual Ba-sic编制程序,验证了基于AHP的柔性工艺优先排序决策方法的可行性。     

A hybrid heuristic algorithm for the multistage supply chain network problem

In recent years, many developments in logistics were connected to the need for information in an efficient supply chain flow. The supply chain is often represented as a network called a supply chain network (SCN) that is comprised of nodes that represent facilities (suppliers, plants, distribution centers and customers). Arcs connect these nodes along with the production flow. A multistage SCN (MSCN) is a sequence of multiple SCN stages. The flow can only be transferred between two consecutive stages. The MSCN problem involves the choice of facilities (plants and distribution centers) to be opened and the distribution network design must satisfy the demand with minimum cost. In this paper, a revised mathematical model is first proposed to correct the fatal error appearing in the existing models. An efficient hybrid heuristic algorithm (HHA) was developed by combining a greedy method (GM), the linear programming technique (LP) and three local search methods (LSMs) (always used in solving the scheduling problem). The pair-wise exchange procedure (XP), the insert procedure (IP) and the remove procedure (RP) to solve the MSCN problem. Preliminary computational experiments demonstrate the efficiency and performance of the proposed HHA.     

基于AHP与模糊多目标规划的新产品组合研究

分析了新产品组合方法对产品开发的重要作用，提出了基于AHP与模糊目标规划的新产品组合方法，将人的经验判断与客观分析相结合，经过实例计算，取得了满意的结果。