An Evolutionary Firefly Algorithm, Goal Programming Optimization Approach for Setting the Osmotic Dehydration Parameters of Papaya

An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediate-to-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.

Optimizing the Asset and Liability Management of Telecommunication Company using Goal Programming Model

Since the telecommunications companies experience great competition,high churn rate,data traffic issues during the Covid-19 pandemic and the upgrade to 5G connectivity,the finance management of a telecommunications company should be analyzed to study the volatility and returns in the sector.This paper aims to develop a goal programming model to examine the asset and liability management of a telecommunication company,namely Telekom Malaysia Berhad(TM)in Malaysia.The result of this study shows that TM has achieved all the goals in maximizing assets,equities,profits,earnings and optimum management item while minimizing liabilities over the period of study from 2015 to 2019.Potential improvements on these goals have also been identified through this study.This paper has also contributed to the studies in financial management since past studies have not been done on asset and liability management in telecommunications companies which is rapidly growing and expanding even while the world is suffering from economy crisis during this pandemic.

Goal Programming for Investment Portfolio and Its Application

To solve the problem of investment portfolio with single goal of maximal NPV, a 0- 1 programming model was proposed and proved effective; and to solve that concerning more elements of a project such as risk level and social benefit, a goal programming model is then introduced. The latter is a linear programming model adopting slack variable called deviation variable to turn inequation constraint into equation constraint, introducing a priority factor to denote different importance of the goals. A case study has demonstrated that this goal programming model can give different results according to different priority requirement of each objective.

Relating Optimization Problems to Systems of Inequalities and Equalities

In quantitative decision analysis, an analyst applies mathematical models to make decisions. Frequently these models involve an optimization problem to determine the values of the decision variables, a system S of possibly non- linear inequalities and equalities to restrict these variables, or both. In this note, we relate a general nonlinear programming problem to such a system S in such a way as to provide a solution of either by solving the other—with certain limitations. We first start with S and generalize phase 1 of the two-phase simplex method to either solve S or establish that a solution does not exist. A conclusion is reached by trying to solve S by minimizing a sum of artificial variables subject to the system S as constraints. Using examples, we illustrate how this approach can give the core of a cooperative game and an equilibrium for a noncooperative game, as well as solve both linear and nonlinear goal programming problems. Similarly, we start with a general nonlinear programming problem and present an algorithm to solve it as a series of systems S by generalizing the “sliding objective function method” for two-dimensional linear programming. An example is presented to illustrate the geometrical nature of this approach.

Predictive Mathematical and Statistical Modeling of the Dynamic Poverty Problem in Burundi: Case of an Innovative Economic Optimization System

The mathematical and statistical modeling of the problem of poverty is a major challenge given Burundi’s economic development. Innovative economic optimization systems are widely needed to face the problem of the dynamic of the poverty in Burundi. The Burundian economy shows an inflation rate of -1.5% in 2018 for the Gross Domestic Product growth real rate of 2.8% in 2016. In this research, the aim is to find a model that contributes to solving the problem of poverty in Burundi. The results of this research fill the knowledge gap in the modeling and optimization of the Burundian economic system. The aim of this model is to solve an optimization problem combining the variables of production, consumption, budget, human resources and available raw materials. Scientific modeling and optimal solving of the poverty problem show the tools for measuring poverty rate and determining various countries’ poverty levels when considering advanced knowledge. In addition, investigating the aspects of poverty will properly orient development aid to developing countries and thus, achieve their objectives of growth and the fight against poverty. This paper provides a new and innovative framework for global scientific research regarding the multiple facets of this problem. An estimate of the poverty rate allows good progress with the theory and optimization methods in measuring the poverty rate and achieving sustainable development goals. By comparing the annual food production and the required annual consumption, there is an imbalance between different types of food. Proteins, minerals and vitamins produced in Burundi are sufficient when considering their consumption as required by the entire Burundian population. This positive contribution for the latter comes from the fact that some cows, goats, fishes, ···, slaughtered in Burundi come from neighboring countries. Real production remains in deficit. The lipids, acids, calcium, fibers and carbohydrates produced in Burundi are insufficient for consumption. This negative contribution proves a Burundian food deficit. It is a decision-making indicator for the design and updating of agricultural policy and implementation programs as well as projects. Investment and economic growth are only possible when food security is mastered. The capital allocated to food investment must be revised upwards. Demographic control is also a relevant indicator to push forward Burundi among the emerging countries in 2040. Meanwhile, better understanding of the determinants of poverty by taking cultural and organizational aspects into account guides managers for poverty reduction projects and programs.

A Mathematical Optimization Model for Locating Telecenters

Telecommuting is a Transportation Demand Management strategy to partially or completely replace the daily commute with telecommunication technologies. Research has revealed that telecommuting can be effectively done from special places provided for this purpose called telecenters. In telecenter-based telecommuting, trip lengths are shortened due to change in the location of work places. Thus suitable locations of telecenters play an important role in increasing the beneficial impacts of telecommuting in the transportation systems. In this research, a mathematical optimization model for finding optimal location and capacity of telecenters is proposed. This model is a bi-objective linear program, and a Fuzzy Goal Programming method with a preemptive structure is used to solve it. Telecommuting demand is classified into three groups of telecommuters and a priority structure that assigns the higher priority class to the closer telecenters is also incorporated into the model. The proposed model is implemented in a case study of finding optimal location of telecenters for government employees in Tehran (capital of Iran) metropolitan area. The base model is solved and its sensitivity to different parameters has been analyzed based on which, an optimal model is selected. The solution of this model is an optimal pattern for distribution of telecommuting capacities and yields the most system-wide benefits from implementation of telecommuting.

Harvest optimization for sustainable agriculture:The case of tea harvest scheduling

To ensure sustainability in agriculture,many optimization problems need to be solved.An important one of them is harvest scheduling problem.In this study,the harvest scheduling problem for the tea is discussed.The tea harvest problem includes the creating a harvest schedule by considering the farmers'quotas under the purchase location and factory capacity.Tea harvesting is carried out in cooperation with the farmer-factory.Factory man-agement is interested in using its resources.So,the factory capacity,purchase location capacities and number of expeditions should be considered during the harvesting process.When the farmer's side is examined,it is seen that the real professions of farmers are different.On harvest days,farmers often cannot attend to their primary professions.Considering the harvest day preferences of farmers in creating the harvest schedule are of great importance for sustainability in agriculture.Two different mathematical models are proposed to solve this problem.The first model minimizes the number of weekly expeditions of factory vehicles within the factor and purchase location capacity restrictions.The second model minimizes the number of expeditions and aims to comply with the preferences of the farmers as much as possible.A sample application was performed in a region with 12 purchase locations,988 farmers,and 3392 decares of tea fields.The results show that the compli-ance rate of farmers to harvesting preferences could be increased from 52%to 97%,and this situation did not affect the number of expeditions of the factory.This result shows that considering the farmers'preferences on the harvest day will have no negative impact on the factory.On the contrary,it was concluded that this situation increases sustainability and encouragement in agriculture.Furthermore,the results show that models are effective for solving the problem.

考虑净负荷不确定性的配电网协同调度

如何实现可再生能源的最大化消纳同时保证配电网的安全经济运行成为亟须解决的问题。首先,将节点上的可再生能源出力和负荷聚合为净负荷并以区间描述其不确定性,同时将系统中调节能力在节点上的映射以端点可优化的区间描述,以调节能力区间和净负荷扰动区间的公共部分反映该节点净负荷扰动的可接纳域。然后,基于优先目标规划,构建了以最大化净负荷扰动可接纳域、最小化配电网运行总成本、最小化电压偏差为目标的考虑净负荷不确定性的3层优化模型,进而得到协同考虑新能源最大化消纳、运行成本合理、节点电压波动最小化的优化方案,保证了配电网运行的安全性、可靠性与经济性。最后,基于改进的IEEE 33节点配网算例,验证了该模型和方法的有效性。

基于数据增强的导弹延寿目标确立方法研究

在导弹贮存延寿工程的立项论证阶段,需要先统计分析每个导弹贮存期内环境剖面数据、故障数据等以分析导弹的贮存故障率变化趋势,进而科学确立延寿目标。针对高可靠性导弹故障数据样本少、环境数据的变化存在非线性、传统方法预测贮存可靠度精度不高等问题,提出SelfSVR算法对导弹贮存故障率进行预测。通过贝叶斯优化方法自适应计算出Mixup生成的样本个数、SVR算法的超参数核函数系数和核函数惩罚系数。基于Mixup增强算法构建新的训练样本。使用SVR算法对导弹贮存故障率进行非线性预测。通过示例验证,Self-SVR算法在预测非线性变化的贮存故障率方面具有良好的拟合效果,在R2系数、EVS和MSE三个指标上优于对比算法。在消融实验中证明了贝叶斯优化和Mixup增强算法对Self-SVR算法均具有优化作用。

基于多重选择目标规划(MCGP)模型的商业银行资产负债管理优化

在大数据时代,多目标决策模型不仅有助于金融行业的数字化转型,也能提高行业风险控制能力。商业银行资产负债管理以安全性、流动性和盈利性的均衡为主要目标,是银行风险管理的重要方面和银行经营的灵魂。针对资产负债管理中存在的多个相互冲突的目标以及各目标值的不确定性,建立基于多重选择目标规划的商业银行资产负债管理优化模型进行实证研究。研究结果表明,相较于传统的目标规划方法,多重选择目标规划模型具有更高的灵活性,可以避免目标值的低估,实现更理想的结果;运用多重选择目标规划模型可有效优化商业银行资产负债结构,更好地平衡风险与收益,同时满足管理层的期望。

双碳目标下考虑源网荷储协同优化运行的新型电力系统发电规划

该文建立了双碳目标下考虑源网荷储协同优化运行的新型电力系统发电规划数学模型,综合考虑各种发电技术、储能以及需求侧响应的情况下实现投资和运行的总成本最低,并将双碳目标、可再生能源渗透率、弃风弃光率这些政策要求转化为约束条件融合到发电规划模型中。通过算例分析分别求解以火电、核电、风电光伏和风光储为主4种发电规划情形,验证了模型的有效性。最后,在双碳目标下建立以风光储为主体的新型电力系统,通过考虑源网荷储协同优化运行实现了规划时期该新型电力系统的发电规划,并求解出对应规划时期储能的最优配置方案。此外,通过该模型对不同渗透率下储能配置与弃风弃光率的量化分析发现,对于省级电力系统,弃风率和弃光率分别为5%和3%时,为了保证系统安全稳定优化运行,风电和光伏的渗透率最好都控制在20%~25%以内,同时储能装机容量配置为0.24~0.3倍风电装机容量加光伏装机容量。

几种常用优化算法在饲料配方中应用的比较分析

为探讨在饲料配方设计时如何选择合适的优化算法,文章比较了线性规划、目标规划和模糊规划等算法在鲤鱼、猪、蛋鸡配合饲料配方设计中的优化结果。结果表明:三种优化算法的结果存在显著差异。模糊规划算法在三种配合饲料配方设计中均不能满足所有营养物质水平,且计算时间最长,其优点是配方成本最低;线性规划算法和目标规划算法在三种饲料配方中均能满足所有营养物质水平,但是在配方成本和计算时间方面存在差异。在配方成本方面,目标规划算法在三种优化结果均为最高成本。在优化过程用时方面,除鲤鱼饲料配方中线性规划算法用时最短外,另外两种饲料配方中均是目标规划算法用时最短。同时,结果还表明线性规划算法和模糊规划算法会随着配方中原料和营养物质约束条件的增多而导致计算时间大幅度增加。

渤海超大位移井水平段裸眼延伸极限预测及影响因素分析

为解决渤海复杂地质条件下浅层超大位移井到底能打多远的问题,文中综合考虑岩屑、井筒环境、水化效应对井壁稳定性的影响,建立了海上超大位移井水平段裸眼延伸极限预测模型,分析了不同因素对水平段裸眼延伸极限的影响,并进行了因素敏感性和钻井方案优化措施分析。结果表明:考虑岩屑影响的水平段裸眼延伸极限明显小于不考虑岩屑影响的延伸极限;水平段裸眼延伸极限随破裂压力当量密度、钻井液稠度系数、钻杆转速、温度增量的增加而增大,随钻井液密度、钻井液流性指数、机械钻速、固相(岩屑)密度、固相体积分数、压力增量、水化应力的增加而减小,随钻井液排量的增大先增大后减小。敏感性分析结果表明,破裂压力当量密度、钻井液流性指数、钻井液稠度系数对延伸极限的影响较大。通过优化各参数取值,可改善钻井液性能,提高井筒清洁能力,合理控制钻进参数,从而延长超大位移井水平段裸眼延伸极限,为平台位置优选、井网布置、复杂结构井开发方式选择提供帮助。

Mergers and Acquisitions: An Efficiency Evaluation

This article sheds light on how synergies arise through mergers and acquisitions (M&A). Enterprises go through the process of Mergers and Acquisitions (M&A) with the goal of improving performance, increasing efficiency and obtaining business synergy. Prior literature suggests that synergies could arise due to taxes, market power or efficiency improvements. This study evaluates the efficiency of M&A in Brazil among publicly-traded companies. We used models with multiple objectives from Goal Programming and Data Envelopment Analysis (GPDEA), employing accounting indicators as input and output variables, and thus evaluated the emergence of synergy gains. These models allow us to analyze and classify the M&A according to the efficiency obtained in such processes. Some of the M&A cases analyzed were mistakenly considered efficient when used traditional models. And, as expected, the GPDEA was proved to be superior to classical models;however it was noticed that few of the cases investigated were proved to be effective. We presented a new application for multi-objective approach that can be used to assess mergers and acquisitions. The dualapplication of GPDEA provided a greater understanding of efficiency generation in synergy creation by means of M&A.

基于分区的含DG配电网实时无功优化

针对含DG配电网实时无功优化问题,提出了更适合该问题的目标函数,并将实时无功优化与配电网动态分区、短期无功调度相互配合来实现空间上和时间上的解耦。针对配电网动态分区问题,提出了支路切割枚举法,实现了快速动态分区。考虑系统区内无功储备不足等特殊情况,可能会导致系统电压在优化后仍有越限,为保证系统安全,启动第二次优化。通过对含DG的33节点配电系统算例和某实际线路进行验证,表明了该算法在求解实时无功优化问题时的正确性和有效性。

油田产量优化的目标规划模型

在现有开采条件下确定油田经济效益最大时的产量组合 (最佳经济产量 )的过程称为油田产量优化。分析了影响油田最佳经济产量的因素 ,将油田产量细分为自然产量、措施产量和新建产能产量 ,提出了自然油变动成本、措施油变动成本和新建产能油变动成本的计算方法。在此基础上 ,提出了多约束条件下以利润最大化为目标的油田产量优化模型。该模型的建立可为油田的中、短期开发规划提供定量的决策依据。

基于目标规划的履带可变形机器人结构参数设计及验证

机器人结构参数直接影响其对环境的适应能力,因此合理的结构参数设计至关重要。为更高效设计能适应障碍已知环境的机器人,该研究提出一种基于目标规划的机器人结构参数设计方法,以得到能适应该环境的结构参数最优的机器人,并开发样机进行试验验证。首先提出并设计履带可变形机器人模型,在分析机器人越障机理基础上,建立机器人能够跨越的台阶和沟壑障碍与其结构参数间的关系,并在此基础上建立履带可变形机器人的结构参数目标规划模型。利用遗传算法得到该目标规划问题的最优结构参数:履带轮半径60 mm,摆臂最大长度326 mm,机体长度290 mm,并利用Adams建立仿真模型验证了机器人对目标环境的适应性。样机试验表明机器人能够跨越160 mm高台阶和300 mm宽沟壑,证明了计算得到的结构参数的合理性,及基于目标规划的机器人结构参数设计方法的可行性。该研究可为机器人的结构参数设计提供参考。

基于QFD的产品多目标规划模型

基于带有模糊系数的模糊回归及模糊优化理论,提出了一种确定关联函数及自相关函数的数学方法,进而建立了基于质量功能展开的产品多目标规划模型·仿真研究表明,该模型适合于各种工程设计问题,尤其是在不确定的、模糊的条件下,能够有效地确定关联函数及自相关函数,帮助开发人员优化顾客需求的满意水平,优化工程特性目标值,在资源约束下使产品的顾客满意度最大·

单线实用货物列车运行图计算机编制系统

列车运行图的计算机编制，是实现铁路行车指挥自动化的基础。本文在研究单线区段列车运行图的优化与自动编制的基础上，把编图研究推进到由多区段组成的某段或整条线路，提出了用目标规划建立优化模型，用“滚动窗口搜索法”进行求解，使区域优化与整体优化有机地结合起来。从而使问题的解满足实用要求。

基于目标规划的网架重构路径优化方法

大停电后的网架重构需要在保证系统运行安全的约束下尽快恢复机组出力,合理的路径恢复顺序对提高重构效果意义重大。文中提出了一种基于目标规划的网架重构路径优化方法。该方法以重构时间和机组出力恢复程度作为评价重构效果的主要因素,在为二者确定合理目标值的基础上,建立了体现各目标相对重要性及综合目标偏差最小的目标规划模型。针对IEEE 30节点系统的应用结果说明,该方法能够利用不同优化目标间的不相容性,所获得的路径恢复顺序以目标值为基础,最大限度地体现了恢复决策的侧重点,对于指导恢复操作更具现实意义。