A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.

A Review of Quantitative Analysis (QA) in Production Planning Decisions Using the Linear Programming Model

The purpose of this paper was to examine the role of quantitative analysis in production planning decisions. This draws from the observed imperatives of quantitative analysis in business decisions and its capacity for predictability and enhanced decision making given the increasingly complex nature of the business environment. The paper therefore addressed the historical evolution of quantitative technique as an efficient and effective decision-making tool. The content of the paper addressed commonly applied quantitative technique in manufacturing firms today which is, linear programming and its subsequent impact on production planning decisions. The results based on a congruence of views revealed that the “best-fit” application of quantitative analysis models and tools can untangle the complexities of production and planning decision making process in order to achieve the organizational goal. This is, as literature also showed that there is obviously no consensus or integrated model that is capable of solving all managerial problem, different models such as the linear programming model have however been developed to cater for different problems as they arise. The workability or suitability of quantitative analysis is actually premised on its appropriate application. The paper recommends the application of quantitative analysis using linear programming in solving various resource allocation related issues in the primary production planning function of manufacturing firms.

Optimal Batching Plan of Deoxidation Alloying based on Principal Component Analysis and Linear Programming

As the market competition of steel mills is severe,deoxidization alloying is an important link in the metallurgical process.To solve this problem,principal component regression analysis is adopted to reduce the dimension of influencing factors,and a reasonable and reliable prediction model of element yield is established.Based on the constraint conditions such as target cost function constraint,yield constraint and non-negative constraint,linear programming is adopted to design the lowest cost batting scheme that meets the national standards and production requirements.The research results provide a reliable optimization model for the deoxidization and alloying process of steel mills,which is of positive significance for improving the market competitiveness of steel mills,reducing waste discharge and protecting the environment.

Diagnosis and Resolution of Infeasibility in the Constraint Method for Solving Multi Objective Linear Programming Problems

In this paper we discuss about infeasibility diagnosis and infeasibility resolution, when the constraint method is used for solving multi objective linear programming problems. We propose an algorithm for resolution of infeasibility, which is a combination of interactive, weighting and constraint methods.Numerical examples are provided to illustrate the techniques developed.

A Linear Programming Model for Seabed Oil-Gas Pipe Network

This paper analyzes the pipe network system of oil-gas collection and transportation for offshore oilfield development. A '0-1' integer linear programming model is constructed to optimize the investment of seabed pipe network. The mathematical model is solved by the spanning tree method of graph theory and network analysis. All spanning trees of a network graph compose all the feasible solutions of the mathematical model. The optimal solution of the model is the spanning tree with the minimum cost among all spanning trees. This method can be used to optimize the seabed pipe network system and give a minimum cost plan for the development of offshore marginal oilfield groups.

Shakedown Analysis of 3-D Structures Using the Boundary Element Method Based on the Static Theorem

The static shakedown theorem was reformulated for the boundary element method (BEM) rather than the finite element method with Melan's theorem, then used to develop a numerical solution procedure for shakedown analysis. The self-equilibrium stress field was constructed by a linear combination of several basis self-equilibrium stress fields with undetermined parameters. These basis self-equilibrium stress fields were expressed as elastic responses of the body to imposed permanent strains obtained using a 3-D BEM elastic-plastic incremental analysis. The lower bound for the shakedown load was obtained from a series of nonlinear mathematical programming problems solved using the Complex method. Numerical examples verified the precision of the present method.

基于大数据的煤炭企业决策模型研究与应用

煤矿的安全生产运营离不开大数据的支持,为利用大数据分析技术和业务模型,给经营决策提供支持,从而实现效益最大化。文章结合当前大型煤炭能源企业在发展大数据方面所具有的信息化基础,探讨研究大数据在企业经营智能决策中的应用,根据国能BT煤炭部门实际生产经营数据,创建了生产优化模型、月度自产煤供应量及外购煤采购量计算模型和决策模型,并将模型部署至服务器,应用于煤矿WL和煤矿LJH,结果表明模型表现良好,并通过对煤矿实际数据的实证分析,验证了该模型的有效性。

Statistical Approach to Basketball Players’Skill Level

In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.

基于混合整数线性规划的分组密码安全性分析

分组密码算法分析中需评估其抵抗差分和线性攻击的能力,而这一能力往往是通过算法的最小差分/线性活跃S盒个数体现的。论文给出基于混合整数线性规划的方法自动分析活跃S盒的原理及一般过程,并应用在I型广义Feistel结构的最小活跃S盒分析中,详细介绍了具体算法分析及应用的过程,该方法通用强、简单易行,可以提高分组密码算法分析的效率。

基于Bootstrap方法和灰色关联分析法的企业原材料订购方案与运输策略

基于企业生产经营的现实情况,文章使用Bootstrap及灰色关联分析等方法对企业过去的原材料订购情况、供应商供应情况和转运商运输情况的相关数据进行了处理,并对供应商的供货特征和转运商的运输损耗特征进行了量化分析。其中,建立并应用了一种评价供应商重要性的评估体系,在不同条件下给出了企业最优订购方案和运输方案,分析了企业如何通过技术改造来提高产能的潜力。

基于蚁群算法的无人机协同任务规划研究

无人机搜寻具有机动性强、定位精度高的优势。本文结合实际情况,模拟红蓝两方的无人机协同海域规划问题。一是协同搜索问题,利用BFS算法先将热力图可视化,根据蚁群算法将红方无人机最优搜索路线规划出来,再利用K-means聚类算法将多架无人机分配到多个区域,实现无人机协同搜索。二是全区域覆盖问题,通过计算区域圆度是否大于0.86判断是用螺旋法还是割草法搜寻方式,实现全区域覆盖。根据不同的搜寻模式对热点区域进行分割处理,建立蓝方搜寻船的机动模型。

Discriminating Among Relatively Efficient Units in Data Envelopment Analysis: A Comparison of Alternative Methods and Some Extensions

This paper concentrates on methods for comparing activity units found relatively efficient by data envelopment analysis (DEA). The use of the basic DEA models does not provide direct information regarding the performance of such units. The paper provides a systematic framework of alternative ways for ranking DEA-efficient units. The framework contains criteria derived as by-products of the basic DEA models and also criteria derived from complementary DEA analysis that needs to be carried out. The proposed framework is applied to rank a set of relatively efficient restaurants on the basis of their market efficiency.

The Application of Scenario Analysis in the Overall Planning of Land Use:A Case Study of Shangluo City in Shaanxi Province

The overall planning of land use is a complex process of joint action of social system, natural and economic conditions. On the basis of summarizing the existing researches, we select Shaanxi's Shangluo City, located in the Qinba mountainous area as the study object, to expound the concept and steps of scenario analysis based on land use change data, under the guidance of ecological safety and sustainable development theory. We design four different scenarios of land use planning program in Shangluo City during the period 2006-2020, and use grey linear programming model to analyze each scenario. The results show that the scenario analysis is feasible in the adjustment of land use structure in Shangluo City; operable in the determining of land use planning program on a macro-municipal scale.

Simulation Using Sensitivity Analysis of a Product Production Rate Optimization Model of a Plastic Industry

This study analyzes the sensitivity analysis using shadow price of plastic products. This is based on a research carried out to study optimization problem of BOPLAS, a plastic industry in Maiduguri, North eastern Nigeria. Simplex method of Linear programming is employed to formulate the equations which were solved by using costenbol software. Sensitivity analysis using shadow price reveals that the price of wash hand bowls is critical to the net benefit (profit) of the company.

基于混合整数线性规划的MORUS初始化阶段的差分分析

认证加密算法MORUS是凯撒(CAESAR)竞赛的优胜算法,抗差分分析性能是衡量认证加密算法安全性的重要指标之一。该文研究了MORUS算法初始化阶段的差分性质,首先给出了一个差分推导规则,可以快速获得一条概率较大的差分链。在此基础上利用混合整数线性规划(MILP)自动搜索技术求解更优的差分链。为了提高搜索速度,结合MORUS初始化阶段的结构特点给出了分而治之策略。根据ΔIV的重量、取值将MILP模型划分为多个子模型并证明了部分子模型的等价性,大大缩减了模型的求解时间,得到了MORUS初始化阶段1~6步状态更新的最优差分链。最后给出了简化版MORUS的差分-区分攻击,该文的结果较之前的工作有较大的提升。

Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)use.Crowd tracking using UAVs is among the most important services provided by a CMA.In this paper,we studied the periodic crowd-tracking(PCT)problem.It consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this purpose.The first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications context.This study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of abstraction.To closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs solver.Our main objective was to study the PCT problem fromboth theoretical and numerical viewpoints.To prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation purposes.The empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

分布式光储参与的直流受端近区配电网暂态电压控制方法

针对高比例分布式光伏接入下的直流受端近区配电网暂态电压控制问题,提出了一种基于灵敏度分析的分布式光伏和储能协同参与下的暂态电压控制方法。首先定义了直流闭锁后受端近区配电网的暂态电压稳定裕度、暂态脱网裕度和暂态可调裕度,然后分别提出了分布式光伏与储能所提供的无功支撑对所定义裕度的灵敏度矩阵计算方法。应用灵敏度方法,将非线性暂态优化问题近似为线性规划问题,从而协调光伏与储能共同参与暂态电压控制直至满足稳定要求。最后以安徽省电力系统为例验证算法的有效性。结果表明算法可协调各分布式光伏与储能为配电网提供暂态电压支撑,并能够防止直流闭锁后分布式光伏与储能脱网。

区域综合能源系统容量配置和调度策略优化方法研究

能源网络快速发展的背景下,综合能源系统可综合利用冷热电气等多种能源,显著提高综合能源利用率,是目前的研究热点。重点研究了区域综合能源系统容量配置和调度策略的最优化问题,提出一种优化方法。基于区域综合能源系统结构和能量转换原理,构建区域综合能源系统混合整数线性规划模型,应用求解器即可进行解算,同时可得到最优配置和调度策略结果。仿真结果表明,所提出的方法在实现区域综合能源系统经济、灵活、高效运行的同时,可以有效降低运行成本,促进能源供需平衡。

基于控制目标松弛的输电设备过载预防控制在线决策

基于有功灵敏度的线性规划模型可快速有效地进行预防控制策略求解,但是由直流潮流法得到的有功灵敏度忽略了电压和网损的影响,导致预防控制策略结果难以满足交流潮流校核计算精度要求。为此,文中提出一种基于控制目标松弛的输电设备过载预防控制在线决策方法。首先,根据过载安全裕度确定参与控制的关键设备及对应预想故障下的有功灵敏度;然后,通过聚类分析对有功调整措施的搜索空间进行降维;接着,建立过载预防控制在线决策的线性规划模型;最后,按预防控制的有功精度将关键设备的有功限额按档位进行松弛,根据交流潮流校核结果确定最优解的搜索方向,不断逼近最优解。通过实际电网的算例分析验证了所提方法的正确性和有效性。