Multiplicity and complexity issues in contemporary production scheduling

High multiplicity scheduling problems arise naturally in contemporary production settings where manufacturers combine economies of scale with high product variety. Despite their frequent occurrence in practice, the complexity of high multiplicity problems – as opposed to classical, single multiplicity problems – is in many cases not well understood. In this paper, we discuss various concepts and results that enable a better understanding of the nature and complexity of high multiplicity scheduling problems. The paper extends the framework presented in Brauner et al. [ Journal of Combinatorial Optimization (2005 ) Vol. 9, pp. 313–323] for single machine, non-preemptive high multiplicity scheduling problems, to more general classes of problems.     

基于遗传算法(GA)的配送路径优化问题研究

文章在建立配送车辆路径优化问题数学模型的基础上，构造了遗传算法来求解该问题，并在算法中引入了自然选择、交叉操作、变异操作等思想：实验结果表明，此算法可以有效求得车辆路径问题的优化解或近似优化解．是求解车辆路径问题的一个较好的方案。     

基于遗传算法的多周期确定性IRP问题研究

本文主要通过在分析多周期确定性条件的基础上建立的数学模型,研究多周期确定性客户需求下的IRP问题,并利用遗传算法对模型进行求解.同时以小群体规模、指定交叉率和变异率为实例,进行数值模拟实例分析.与利用经济批量采购公式求解的客户补货量进行了对比分析,遗传算法可以有效地解决了IRP中最优补货量的问题.     

一种不确定环境下供应链的模型与算法

供应链管理作为一种新型的企业关系管理模式在现代市场竞争中为企业生存与发展提供了一种工具。本文针对不确定环境下供应链的优化进行了研究。给出了一个供应链的仿真模型及其优化算法。     

Measuring the impact of R&;D on Productivity from a Econometric Time Series Perspective

In this paper we argue that the standard sequential reduction approach to modelling dynamic relationships may be sub-optimal when long lag lengths are required and especially when the intermediate lags may be less important. A flexible model search approach is adopted using the insights of Bayesian Model probabilities, and new information criteria based on forecasting performance. This approach is facilitated by exploiting Genetic Algorithms. Using data on U.K. and U.S. agriculture the bivariate time series relationship between R&D expenditure and productivity is analysed. Long lags are found in the relationship between R&D expenditures and productivity in the U.K. and in the U.S. which remain undiscovered when using the orthodox approach. This finding is of particular importance in the debate on the optimal level of public R&D funding.JEL Classification: C22, C51, Q16     

基于遗传算法的客运站到发线优化安排

合理安排列车占用到发线计划是编制阶段计划的关键。建立的客运站到发线运用模型是非线性的整数规划模型,直接求解较为困难,为此采用遗传算法通过交叉变异等操作,构建车站到发线运用的染色体结构和适应度函数,并进行遗传算子设计,可取得较优的结果。以成都站为例,运用模型计算生成到发线运用计划,说明其结果优化效果明显。     

基于遗传算法的包装废弃物回收逆向供应链网络构建研究

随着人们对生态环保和经济可持续发展的进一步认识,面对可再利用包装废弃物资源的浪费和对环境造成的污染,越来越多的人们意识到包装废弃物回收的重要性。文章分析了国内包装废弃物回收的现状,提出了包装废弃物回收的逆向供应链网络模型,利用遗传算法,对模型进行了优化求解,并给出了算例。     

基于多台自动导引运输车的自动分拣系统

为了提高仓储系统中物流分拣的效率,设计了一套基于多台自动导引运输车（AGV）的自动分拣系统。首先将整个系统分解为硬件部分和软件部分,硬件部分主要包含服务器、多台AGV和机械手等等,软件部分的设计主要包含智能调度算法设计、最短路径算法设计和多AGV之间的避障算法设计等等;之后,通过逐步完成上述子模块的设计工作,即可完成整个系统的设计。经过在工厂一段时间的试运行,实践结果表明,该套系统的设计能够完成多台AGV的调度,并较大地提高了物流分拣的效率。     

A novel mathematical model for a multi-period,multi-product optimal ordering problem considering expiry dates in a FEFO system

One of the main challenges of retail units is to determine the order quantities of different types of products, each with a specific expiry date, so that the system cost including shortage cost is minimized. We study a new multi-product multi-period replenishment problem for a First Expired-First Out (FEFO) based warehouse management system. The proposed nonlinear model is first converted to a linear one and then solved by applying two evolutionary algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model for perishable items and comparing the results shows the efficiency of the proposed metaheuristics as well.     

Marketing Intelligent Systems for consumer behaviour modelling by a descriptive induction approach based on Genetic Fuzzy Systems

In its introduction this paper discusses why marketing professionals do not make satisfactory use of the marketing models posed by academics in their studies. The main body of this research is characterised by the proposal of a brand new and complete methodology for knowledge discovery in databases (KDD), to be applied in marketing causal modelling and with utilities to be used as a marketing management decision support tool. Such methodology is based on Genetic Fuzzy Systems, a specific hybridization of artificial intelligence methods, highly suited to the research problem we face. The use of KDD methodologies based on intelligent systems like this can be considered as an avant-garde evolution, exponent nowadays of the so-called knowledge-based Marketing Management Support Systems; we name them as Marketing Intelligent Systems. The most important questions to the KDD process-i.e. pre-processing; machine learning and post-processing-are discussed in depth and solved. After its theoretical presentation, we empirically experiment with it, using a consumer behaviour model of reference. In this part of the paper, we try to offer an overall perspective of how it works. The valuation of its performance and utility is very positive.