基于GP-PS的分布式加工与装配多级车间调度规则自动设计方法

分布式加工与装配多级制造系统由多个用于加工零件的作业车间和用于装配产品的一般流水车间组成。动态到达的订单涉及多层产品结构,零件需齐备之后才可装配。该类多级车间的管控涉及订单分配、加工和装配任务调度联合决策问题,其关键在于实现两级生产的精准化协同目的。针对分布式加工与装配多级车间调度问题,提出了一种基于GP-PS的分布式加工与装配多级车间调度规则自动设计方法。首先,以最小化订单拖期率为目标,建立了订单分配、加工和装配任务调度联合决策的数学模型;然后,提出了一种改进型遗传规划算法,用以集成进化多级调度规则,设计了一类种群优化机制来避免算法陷入局部收敛,同时嵌入了并行仿真技术,有效减少了训练时间;最后,进行了仿真实验,对改进型遗传算法的性能进行了验证。研究结果表明:人工规则组、标准遗传规划算法及改进型遗传算法得到的订单拖期率分别为6.44%、5.65%、2.67%。基于并行仿真优化的改进型GP算法较数十个优选的人工规则组及标准GP算法生成的最优规则组,能取得更明显的综合性能优势。使用该算法针对DPAMW调度问题自动设计一体化调度的多级规则是可行的、有效的。

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。

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.

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.

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.

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.

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.

益胃消浊汤联合GP方案治疗中晚期肺癌的临床观察

目的 研究益胃消浊汤联合GP方案治疗中晚期肺癌的临床效果。方法 以90例肺癌患者为研究对象,采用随机数字表法将患者随机分为对照组与观察组,每组各45例。观察组患者采用GP化疗方案联合益胃消浊汤进行治疗,对照组患者采用GP化疗方案,两组患者治疗期间均采用中医护理方案,治疗8周。观察两组患者中医证候积分,生命质量,自我管理能力,护理满意度之间的差异。结果 经过治疗后,两组患者的中医证候积分均显著下降,且观察组患者的中医证候积分高于对照组,经过治疗后,两组患者的生命质量各项指标显著升高,且观察组患者的生命质量各项指标显著高于对照组,两组患者自我管理评分均有所提升,且观察组患者治疗干预后自我管理能力评分高于对照组(P<0.05),观察组患者的满意度(97.78%)明显高于对照组(84.44%),两组患者满意度之间的差异存在统计学差异(P<0.05)。结论 益胃消浊汤联合GP方案治疗中晚期肺癌,患者的中医证候积分显著改善,患者的生命质量和自控能力大幅提升。

STOCHASTIC GENERALIZED GOAL PROGRAMMING

This thesis presents the combination of the stochastic programming and generalized goal programming. We puts forward several generalized goal programming models with stochastic parameter--stochastic generalized goal programming. Furthermore, we probe into the theory. and algorithm of these models. At last, this method was applied to an example of an industrial problem.

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.

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.

计及源荷低碳性与CCGP的两阶段优化调度

在低碳经济背景下解决火电机组碳排放高和源荷预测误差导致的全局信息不匹配问题,提出计及源荷低碳性与机会约束目标规划(CCGP)的两阶段调度模型。首先,规划阶段兼顾源荷低碳经济性与安全性,建立需求响应后计及碳交易和源荷不确定的调度模型。其次,调整阶段计及预测信息不匹配影响和激励型负荷,以出力调整偏差最小优化实时出力。最后,通过确定性等价理论对模型转化,在改进IEEE 30节点系统中验证模型。结果表明:所提模型有效降低了系统碳排放量与运行成本,提高了风电消纳能力及评估备用容量缺额需求。

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.

Reformative Financial Risk Management Approach： A Multistage Decision Support System with the Assistance of Fuzzy Goal Programming and Expertons Method

The potential demand on financial risk management has being increased considerably by the reason of Basel 11 regulations and instabilities in economy. In recent years, financial institutions and companies have been struggled for building up intensive financial risk management tools due to Basel II guidance on establishing financial self-assessment systems. In this respect, decision support system has a significant role on effectuating intensive financial risk management roadmap. In this study, a reformative financial risk management system is presented with the combination of determining financial risks with their importance, calculating risk scores and making suggestions based on detected risk scores by applying corrective actions. First, financial risk factors and indicators of these risk variables are selected and weights of these variables are specified by using fuzzy goal programming. After that, total risk scores are calculated and amendatory financial activities are appeared by means of expertons method which also provides possibilities of the alternative decisions. To illustrate the performance of integrated and multistage decision support system, a survey is applied on the end users.

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.

GP Algorithm-Based Fourier Transform Infrared Spectrum Trend Term Removal Model

Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.

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。