基于改进遗传算法的航材装载优化

目的 通过对现行航材装载问题进行分析,优化提高工作中航材的装载效率,为部队航材智能装载提供思路和方法.方法 确定初始可理想化条件和装载过程中需满足的约束条件,以航材的摆放顺序和旋转方式为基因值,利用装载方案所需装载箱数量和重心偏移度为适应度函数去评估解的优劣;采取精英选择策略去指导进化方向,子代的产生选择相对偏随机秘钥交叉及顺序变异,避免选择变异过程中可能产生基因值的冲突,并用最小二乘法对航材储运装载算法输入参数进行拟合,找到平衡时间和装载箱使用数量的最佳输入值.结果 通过与实际人工装载进行比对,将装载箱平均使用率从78.1％提升至84.45％,并完成了对航材的分类.结论 该方法具有优化航材装载方案的能力,能够提升装载工作效率,对部队航材智能装载发展具有重要意义.     

基于理想点的DEA交叉效率评价研究

针对信息无法充分利用的缺陷,提出了一种基于理想点的DEA交叉效率评价方法。首先,分别求出各决策单元的交叉效率值;其次,利用所有信息,求出集结交叉效率值的基于数据驱动的客观权重系数;最后,对集结后的交叉效率值进行排序。结果显示:基于理想点的DEA交叉效率评价方法,得到的评价结果较为客观、公平,较好地解决了决策单元的交叉效率评价问题。     

航空公司飞机航线可更换单元两级库存控制方法

航空公司以维修基地和中央仓库两级库存保障飞机的航线可更换单元需求,同时规定高保障率以提供高航空服务质量,提出了保障率约束下的两级仓库最佳备件数量问题。本文首先通过航线可更换单元的失效数据以极大似然方法估计需求率,然后提出所有仓库总备件成本最小化为目标,备件综合保障率为约束的两级库存控制模型,最后采用边际分析法进行模型求解完成维修基地和中央仓库两级备件量的配置优化,实现航材资金占用量的最小化。通过实例验证表明采用此方法能够有效压缩库存规模,降低库存成本。     

基于储运单元的航材携行组配研究

目的 为有效提高航材保障应急准备和战时快速保障能力,探索基于储运单元的航材携行组配新模式,解决组配航材需求大与库存条件有限的矛盾问题。方法 科学设计模块化航材携行组配方案,以组配航材需求最少为目标,分别构建了绝对约束和宽松约束条件下的组配模块配置优化模型,并通过Matlab软件实现模型求解自动化。同时,引入模块配置允许偏差约束条件,以灵敏度分析为方法手段,合理调整组配方案中与库存条件不相适应的部分。结果 算例分析表明,模块化航材携行组配方案能够使组配规模和器材需求降低58.9%,可行性大幅提高,文中构建的模块配置优化模型既能够严格按照航材携行标准精确组配,又能够根据现实状况灵活应变,具有较强的实用性、适应性。结论 航材携行组配模块配置优化模型能够支持组配方案的实施,对航材携行组配模式研究的推进、提高航材保障机动性,具有重要的现实意义。     

基于多目标蝗虫算法的航材分配模型

目的 为了进一步优化航材库存结构,解决多目标航材分配问题,提高航材保障工作决策的效率.方法 建立基于费用和分配满意度的多目标航材分配模型,运用先进的群体智能算法——蝗虫算法求解.结果 算例分析表明,在3种求解算法中,蝗虫算法所求出来的解,既使得航材分配过程中所需成本最低,又保证了航材股满意度处于较高水平.同时,将算法运行10次,蝗虫算法的求解时间平均值和方差分别为4.01 ms和11.5 ms,明显优于传统的群智能算法粒子群和NSGA-Ⅱ算法的求解效率.结论 蝗虫算法能够有效地解决多目标航材分配问题,对于优化航材库存,平衡航材数量具有重要的现实意义.     

三参数区间交叉效率DEA评价方法

基于交叉效率概念,遵循自评与互评相结合的原则,提出三参数区间交叉效率DEA评价方法。运用不同的赋权策略,获得用最优效率值、最劣效率值和最可能效率值描述的决策单元三参数区间交叉效率值。该效率值能更全面合理地反映决策单元效率值可能的取值情况和实际评价问题的特点,利用三角模糊数期望值排序方法可以对该效率值进行比较排序。算例说明该方法具有可行性和有效性。     

考虑排产约束的汽车零部件出库订单排序优化北大核心CSSCI

针对TPL仓库面向汽车零部件企业的产前物料拣选和配送作业环节,研究了JIT生产模式下配送送达时间限定和生产线缓冲库容量有限条件下的出库作业拣选订单排序的优化模型及算法。在分析现有拣选订单排序模式的基础上,提出了融合现有模式优点的新作业模式,建立了该模式下拣选订单排序优化模型,并针对这一整数组合优化模型,设计了一套基于贪心理论的算法对其进行求解。通过实际案例分析表明,该模型及算法可以达到减少拣选作业空余时间、提高作业效率、降低作业成本的目的。     

基于超效率DEA的钢铁工业绿色规模经济

为实施可持续发展战略,适应构建和谐社会的要求,提出绿色规模经济的概念,并阐述了其必要性,建立了钢铁工业绿色规模经济的指标体系,使用数据包络分析(DEA)方法来测度我国钢铁企业绿色规模经济有效性,选择超效率DEA模型解决多个DEA有效情况下的排序问题,同时提出了钢铁企业相应的改进措施和发展方向.     

考虑排产约束的汽车零部件出库订单排序优化

针对TPL仓库面向汽车零部件企业的产前物料拣选和配送作业环节,研究了JIT生产模式下配送送达时间限定和生产线缓冲库容量有限条件下的出库作业拣选订单排序的优化模型及算法。在分析现有拣选订单排序模式的基础上,提出了融合现有模式优点的新作业模式,建立了该模式下拣选订单排序优化模型,并针对这一整数组合优化模型,设计了一套基于贪心理论的算法对其进行求解。通过实际案例分析表明,该模型及算法可以达到减少拣选作业空余时间、提高作业效率、降低作业成本的目的。     

基于外包络点的最小外接圆误差快速评定方法北大核心CSCD

对大尺寸轴类零件的高精密圆度测量时会生成大样本测量点集,而基于大样本点集的快速最小外接圆(MCC)误差评估已成为急需解决的工程问题。圆度测量产生的点集一般为有序评估点集,其最小外接圆可由该点集的外包络点确定。为提高最小外接圆计算效率,先利用包络向量搜寻外包络点,再进一步将外包络点压缩至常数范围,进而基于压缩后的外包络点快速求取最小外接圆及误差。分别利用文献数据、仿真数据和实测数据验证基于外包络点的最小外接圆误差快速评定方法的通用性、高效性和实用性。基于外包络点的最小外接圆误差快速评定方法具有实现简单、评定速度快的特点,可用于改进现有圆度仪的最小外接圆计算方法。     

DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection

Data envelopment analysis (DEA) has been extended to cross-efficiency evaluation to provide better discrimination and ranking of decision-making units (DMUs). However, the non-uniqueness of optimal weights in the traditional DEA models (CCR and BCC models) has reduced the usefulness of the DEA cross-efficiency evaluation method. To solve this problem, we introduce the concept of the satisfaction degree of a DMU towards a set of optimal weights for another DMU. Then, a new DEA cross-efficiency evaluation approach, which contains a maxmin model and two algorithms, is proposed based on the satisfaction degrees of the DMUs. Our maxmin model and algorithm 1 can obtain for each DMU an optimal set of weights that maximises the least satisfaction degrees among all the other DMUs. Further, our algorithm 2 can then be used to guarantee the uniqueness of the optimal weights for each DMU. Finally, our approach is applied to a real-world case study of technology selection.     

Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection

A practical common weight multi-criteria decision-making (MCDM) methodology with an improved discriminating power for technology selection is introduced. The proposed MCDM methodology enables the evaluation of the relative efficiency of decision-making units (DMUs) with respect to multiple outputs and a single exact input. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed MCDM framework with that obtained by the cross-efficiency analysis, which is a well-known data envelopment analysis-based methodology. The results indicate that the proposed methodology enables further ranking of data envelopment analysis-efficient DMUs with a notable saving in computations compared with cross-efficiency analysis. Finally, the proposed MCDM framework is extended to incorporate ordinal as well as exact outputs, and an application is presented to illustrate the methodology.     

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

Material handling improvement in warehouses by parts clustering

A major part of warehouse operations is related to the collection of parts from the warehouse which is called the Order Picking Problem. To improve order picking operations, the total travel distance and generally picking time must be reduced. In this paper, a two-level approach is proposed that determines the locations of parts in the warehouse. The first step clusters parts into part families. Four different clustering methods based on principal component analysis, singular value decomposition and Two-Step Cluster Component are applied. In the second step, four different heuristics are proposed to determine the locations of parts. In addition to the minimisation of travel distance, we also consider the minimisation of the total congestion in aisles due to multiple workers. The proposed algorithms also consider the interactions between part families to minimise intergroup movements. As a result of the implementation, we achieved more than 40% reduction in material handling compared to the current set-up of the warehouse. The applied algorithms can easily be modified to be used for warehouses with different configurations. The algorithms utilised in this case study can be helpful to researchers to become familiar with new heuristics, as well as practitioners to design improved warehouses.     

基于英式拍卖的RMFS货位指派研究

目的 构建英式拍卖模型，以待指派商品品项(SKU)群的最低周转率为媒介，通过逐步提升最低周转率来实现待指派SKU与待指派区域货位数量的匹配。方法 针对移动机器人拣货系统(RMFS)中的货位指派，提出基于英式拍卖机制的货位指派方法，提升仓库拣货效率。结果 与随机指派相比，在不同仓库规模、订单规模、订单偏度的RMFS中采用英式拍卖货位指派机制，机器人行走路程下降比率在大型仓库中达30.17%，中型仓库的下降比率为27.31%，小型仓库的下降比率为24.13%。结论 采用英式拍卖机制在RFMS中进行货位指派可大幅度提高工作效率。     

Methods and applications of DEA cross-efficiency: Review and future perspectives

The field of engineering management usually involves evaluation issues, such as program selection, team performance evaluation, technology selection, and supplier evaluation. The traditional self-evaluation data envelopment analysis (DEA) method usually exaggerates the effects of several inputs or outputs of the evaluated decision-making unit (DMU), resulting in unrealistic results. To address this problem, scholars have proposed the cross-efficiency evaluation (CREE) method. Compared with the DEA method, CREE can rank DMUs more completely by using reasonable weights. With the extensive application of this technique, several problems, such as non-unique weights and non-Pareto optimal results, have arisen in CREE methods. Therefore, the improvement of CREE has attracted the attention of many scholars. This paper reviews the theory and applications of CREE, including the non-uniqueness problem, the aggregation of cross-efficiency data, and applications in engineering management. It also discusses the directions for future research on CREE.     

Supplier evaluation and selection: an augmented DEA approach

Evaluating and selecting suppliers is an essential part of effectively managing today's dynamic and global supply chains. In this paper, we propose a supplier evaluation and selection methodology based on an extension of data envelopment analysis (DEA) that can evaluate suppliers in an efficient manner. Through the incorporations of a range of virtual standards, the proposed methodology termed augmented DEA, has enhanced discriminatory power over basic DEA models to rank suppliers. In addition, weight constraints are introduced to reduce the possibility of having inappropriate input and output factor weights. We demonstrate the application of augmented DEA with comparison experiments and find that the augmented DEA model has advantages over the basic DEA model as well as the cross-efficiency and super-efficiency models. Finally, we present a case application with data obtained from a communication and aviation electronics company to demonstrate the applicability and use of augmented DEA.     

DEA cross-efficiency aggregation method based upon Shannon entropy

Existing approaches for DEA cross-efficiency evaluation are mainly focused on the calculation of cross-efficiency matrix but pay little attention to the aggregation of the efficiencies in the cross-efficiency matrix. The most widely used approach is to aggregate the efficiencies in each row or column in the cross-efficiency matrix with equal weights into an average cross-efficiency score for each Decision Making Unit (DMU) and view it as the overall performance measurement of the DMU. This paper focuses on the aggregation process of the efficiencies in the cross-efficiency matrix and proposes the use of Shannon entropy for cross-efficiency aggregation. In the study, we propose an entropy model to generate a set of weights for aggregating and determining the ultimate cross-efficiency instead of the traditional average cross-efficiency. We prove that the set of weight is a unique global optimal solution which can reflect the goodness of this method. Finally, two examples of a flexible manufacturing system and 27 industrial robots are illustrated to examine the validity of the proposed method.     

Designing public storage warehouses with high demand for revenue maximisation

The design of public storage warehouses needs to fit market segments to increase the average revenue in an environment of high demand. This paper presents a revenue model integrated with queuing and price-demand theories to solve the design and pricing problem for public storage warehouses. We consider two demand cases in the model, which are exponential demand and piecewise linear demand. We also develop a solution based on dynamic programming techniques to solve the problem. Using data from a warehouse, we conduct numerical experiments. Results show that our approach can improve the expected revenue of public storage warehouses with high demand by 16.6% on average. We further conduct sensitivity analysis on price, and investigate the relation between revenue and price.     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。