Optimal Solution of Multi-Choice Mathematical Programming Problem Using a New Technique

The study deals with the multi-choice mathematical programming problem, where the right hand side of the constraints is multi-choice in nature. However, the problem of multi-choice linear programming cannot be solved directly by standard linear or nonlinear programming techniques. The aim of this paper is to transform such problems to a standard mathematical linear programming problem. For each constraint, exactly one parameter value is selected out of a multiple number of parameter values. This process of selection can be established in different ways. In this paper, we present a new simple technique enabling us to handle such problem as a mixed integer linear programming problem and consequently solve them by using standard linear programming software. Our main aim depends on inserting a specific number of binary variables and using them to construct a linear combination which gives just one parameter among the multiple choice values for each choice of the values of the binary variables. A numerical example is presented to illustrate our analysis.

Linear goal programming approach to obtaining the weights of intuitionistic fuzzy ordered weighted averaging operator

The multiple attribute decision making problems are studied, in which the information about attribute weights is partly known and the attribute values take the form of intuitionistic fuzzy numbers. The operational laws of intuitionistic fuzzy numbers are introduced, and the score function and accuracy function are presented to compare the intuitionistic fuzzy numbers. The intuitionistic fuzzy ordered weighted averaging （IFOWA） operator which is an extension of the well-known ordered weighted averaging （OWA） operator is investigated to aggregate the intuitionistic fuzzy information. In order to determine the weights of intuitionistic fuzzy ordered weighted averaging operator, a linear goal programming procedure is proposed for learning the weights from data. Finally, an example is illustrated to verify the effectiveness and practicability of the developed method.

Goal programming approach for sustainable forest management(case study in Iranian Caspian forests)

We used a goal programming technique to determine the optimal harvest volume for the Iranian Caspian forest. We collected data including volume, growth, wood price at forest roadside, and variable harvesting costs. The allometric method was used to quantify seques- trated carbon. Regression analysis was used to derive growth models. Expected mean price was estimated using wood price and variable harvesting costs. Questionnaire was used to determine the constraints and the equation coefficients of the goal programming model. The optimal volume was determined using the goal programming method according to multipurpose forest management. LINGO software was used for analysis. Results indicated that the optimum volumes of species were 250.25 m3.ha-1 for beech, 59 m3.ha-1 for hornbeam, 73 m3.ha-1 for oak, 41 m3.ha-1 for alder, and 32 m3.ha-1 for other species. The total optimum volume is 455.25 m3.ha-1.

HEURISTIC MODELING FOR A DYNAMIC AND GOAL PROGRAMMING IN PRODUCTION PLANNING OF CONTINUOUS MANUFACTURING SYSTEMS

At the first sight it seems that advanced operation research is not used enough in continuous production systems as comparison with mass production, batch production and job shop systems, but really in a comprehensive evaluation the advanced operation research techniques can be used in continuous production systems in developing countries very widely, because of initial inadequate plant layout, stage by stage development of production lines, the purchase of second hand machineries from various countries, plurality of customers. A case of production system planning is proposed for a chemical company in which the above mentioned conditions are almost presented. The goals and constraints in this issue are as follows： （1） Minimizing deviation of customer＇s requirements. （2） Maximizing the profit. （3） Minimizing the frequencies of changes in formula production. （4） Minimizing the inventory of final products. （5） Balancing the production sections with regard to rate in production. （6） Limitation in inventory of raw material. The present situation is in such a way that various techniques such as goal programming, linear programming and dynamic programming can be used. But dynamic production programming issues are divided into two categories, at first one with limitation in production capacity and another with unlimited production capacity. For the first category, a systematic and acceptable solution has not been presented yet. Therefore an innovative method is used to convert the dynamic situation to a zero- one model. At last this issue is changed to a goal programming model with non-linear limitations with the use of GRG algorithm and that＇s how it is solved.

A Glorious Literature on Linear Goal Programming Algorithms

In the last several years, there has been a marked improvement in the development of new algorithms for solving Linear Goal programming (LGP). This paper presents a survey of current methods for LGP.

Optimum blending of iron ore from Choghart stockpiles by using goal programming

Iron and steel industry is an important sector of Iran's economy.Choghart iron ore mine is an important iron ore producer of Iran steel industry.Phosphorous contained in the iron ore concentrates of Choghart mine has a detrimental effect on the steel making process,whereby this causes cracks to form in the refractory lining of blast furnaces.In the past,about 1.43 Mt of low-grade and 4.53 Mt of high-phosphorous materials had been transported to low grade and high phosphorous stockpiles,respectively,for future beneficiation.As a result of the progressive depletion of high-grade ore and establishment of beneficiation plant in Choghart,exploitation of these two stockpiles in this mine became an important issue.In this work,a linear goal programming(GP) model was developed in order to determine the optimum iron ore blend in terms of quality from low grade and high phosphorous stockpiles of Choghart mine.The model was solved by the SOLVER V.9 program.Results show that feeding with acceptable quality(w(Fe)≥50% and w(P)≤1.2%,mass fraction) materials can be blended from stockpiles that satisfy the needs of the Choghart processing line.

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.

A Weighted Goal Programming Model for the DASH Diet Problem: Comparison with the Linear Programming DASH Diet Model

A Linear Programming DASH diet model for persons with hypertension has previously been formulated and daily minimum cost diet plans that satisfy the DASH diets’ tolerable intake level of the nutrients for 1500 mg a day Sodium level and different daily calorie levels were obtained using sample foods from the DASH diet eating plan chart. But the limitation in the use of linear programming model in selecting diet plans to meet specific nutritional requirements which normally results in the oversupply of certain nutrients was evident in the linear programming DASH diet plan obtained as the nutrient level of the diet plans obtained had wide deviations of from the DASH diets’ tolerable upper and lower intake level for the given calorie and sodium levels. Hence the need for a model that gives diet plans with minimized nutrients’ level deviations from the DASH diets’ tolerable intake level for different daily calorie and sodium level at desired cost. A weighted Goal Programming DASH diet model that minimizes the daily cost of the DASH eating plan as well as deviations of the diets’ nutrients content from the DASH diet’s tolerable intake levels is hereby presented in this work. The formulated weighted goal programming DASH diet model is further illustrated using chosen sample foods from the DASH food chart as used in the work on the linear programming DASH diet model for a 1500 mg sodium level and 2000 calories a day diet plan as well as for 1800, 2200, 2400, 2600, 2800 and 3000 daily calorie levels. A comparison of the DASH nutrients’ composition of the weighted Goal Programming DASH diet plans and those of the linear programming DASH diet plans were carried out at this sodium level and the different daily calorie levels. It was evident from the results of the comparison that the weighted goal programming DASH diet plans has minimized deviations from the DASH diet’s tolerable intake levels than those of the linear programming DASH diet plans.

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.

SURFACE MINE PLANNING OPTIMIZATION BY GOAL PROGRAMMING

This paper introduced an approach to surface mine planning optimization-Goal Programming.The multiobjective[0-1] model has been built and the software has been developed.The method has been applied to a huge surface coal mine,the result of which shows that it is effective and feasible.

Goal Programming for Investment Portfolio and Its Application

To solve the problem of investment portfolio with single goal of maximal NPV,10-1 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 improtance of the goals.A case study has demonstrated that this goal programming model can give different results according to different priorty requirement of each objective.

Modeling of Catfish Farm Using Lexicographic Linear Goal Programming

In this paper, a fish farm was modeled using the Lexicographic linear goal programming approach due to incommensurability in objectives. The study considered the fish farming plan with two sizes of catfish from stocking to harvesting at four-month intervals. The multi-objective goals developed are required raw materials feed, water, light (resource utilization), sales revenue, profit realized, labor utilization, production costs, and pond utilization. The developed model was tested using related data collected from the farm records with the use of TORA 2007 software. The compromised solution from the results showed that the developed model is an efficient tool for decision-making process in the fish farm business organization.

A Note on Standard Goal Programming with Fuzzy Hierarchies: A Sequential Approach

In the paper [Standard goal programming with fuzzy hierarchies: a sequential approach, Soft Computing, First online: 22 March 2015], it has been assumed that the normalized deviations should lie between zero and one. In some cases, this assumption may not be valid. Therefore, additional constraints must be incorporated into the model to ensure that the normalized deviations should not exceed one. This modification is illustrated by the given numerical example.

考虑功率预测偏差和出力调节不确定性的风电集群功率分配策略

当因输电通道限制需对风电集群进行限电时,应考虑各风电场功率预测和调节能力的差异。考虑功率预测偏差和调节能力不确定性,构建机会约束规划和机会约束目标规划相结合的风电集群日前功率调度模型,并采用采样排序的方法将不确定变量转化为确定性变量对模型进行求解。对华北某地区风电集群进行案例分析,结果表明,在满足风电场间期望调度电量比例要求的基础上,相较于传统模型,所提模型能有效降低弃风率和系统负荷不平衡时的缺额电量。

考虑多种工业负荷参与的多时间尺度源荷储优化调度

为解决大规模新能源接入导致传统电网中常规机组调节压力不断增大的问题,提出了一种考虑储能和多种工业负荷参与的多时间尺度优化调度策略。该策略通过协调荷侧电解铝、水泥、钢铁、非生产性负荷与储能以及源侧常规机组调用计划,有效缓解电网调节压力。首先,分析储能和多种工业负荷的调节特性,建立风-光-荷-储的滚动调节框架。然后,针对源荷不确定性,采用多场景随机规划与模糊机会约束目标规划相结合的方法,建立日前和日内阶段以系统经济性最优为目标、实时调度阶段兼顾安全性和经济性的多时间尺度调度模型。最后,通过新能源充足与不足典型日算例可知,本文所提调度策略能够充分发挥可调控资源的调节能力,在两种典型日的总成本较日前最优调度分别降低了17.08%、44.13%,弃新能源量降低39%、失负荷量降低23%,有效提高了电网运行经济性和安全性。

社会组织项目运作中的目标置换问题及其治理

基于目标置换效应,深入分析社会组织的项目运作过程后发现,社会组织疲于完成项目的量化指标,服务对象的需求被考核指标和项目委托方的需求所置换,在项目运作中普遍存在形式置换内容的行为,体现出重形式、轻内容,重文档、轻服务,服务活动化与娱乐化,服务随意化与碎片化,服务对象同质化与重复化等目标置换特征。项目设计欠缺需求调研,目标设置偏重量化指标,在执行中缺乏双向沟通机制,外部监管采用重文档、重财务的评价方式,致使社会组织为迎合评估而弱化服务成效,因缺乏资源与人才而难以进行专业化发展,出现各种目标置换问题。要破解社会组织项目运作中的目标置换问题,就需要在项目发包前进行科学的事前评估,减少合同中严格的量化指标,加强双向沟通机制建设,强化服务成效评估,以及加强社会组织专业化服务建设。

新商科背景下“Python程序设计”的课程思政教学探索

新一代信息技术的蓬勃发展推动了数字经济的蓬勃发展,使人类社会迈入了数字经济时代,中国的商业模式和商业形态都发生了巨大的变化,开始由以工业经济为背景的传统商科向以数字经济为背景的新商科迈进,新商科注重学科间的交叉融合,使得跨学科综合型人才的需求量逐渐增大。“Python程序设计”作为新商科学生的公共基础课,具有知识点繁多、理论性和实践性较强等特点。在新商科背景下,将思政元素融入“Python程序设计”教学中,通过分析课程思政的德育目标,使学生在牢固掌握专业知识的同时,提升思想政治价值观念,形成勤奋好学的学习态度,以期为推动新商科人才核心素养的培育和发展提供改革路径及推进方法。

人工智能技术与电力系统融合发展研究

与人工智能的深度融合将是新型电力系统的主要特征。梳理了人工智能的关键技术体系,人工智能在电力系统中应用的重点技术领域、主要业务场景,以及人工智能与电力系统融合的典型应用范式,分析了人工智能研究成果与电力系统实际工程应用之间的差距,明确了企业经营、安全生产、客户服务、基础支撑四方面人工智能与电力系统融合的具体发展目标,提出了包括感知智能深化应用、认知智能发展实现、创造智能三阶段的人工智能与电力系统融合发展战略规划。

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。