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.

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.

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.

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.

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.

Prediction Model of Data Envelopment Analysis with Undesirable Outputs

Data envelopment analysis (DEA) has become a standard non parametric approach to productivity analysis, especially to relative efficiency analysis of decision making units (DMUs). Extended to the prediction field, it can solve the prediction problem with multiple inputs and outputs which can not be solved easily by the regression analysis method.But the traditional DEA models can not solve the problem with undesirable outputs,so in this paper the inherent relationship between goal programming and the DEA method based on the relationship between multiple goal programming and goal programming is explored,and a mixed DEA model which can make all factors of inputs and undesirable outputs decrease in different proportions is built.And at the same time,all the factors of desirable outputs increase in different proportions.

A multi-objective model for cordon-based congestion pricing schemes with nonlinear distance tolls

Congestion pricing is an important component of urban intelligent transport system.The efficiency,equity and the environmental impacts associated with road pricing schemes are key issues that should be considered before such schemes are implemented.This paper focuses on the cordon-based pricing with distance tolls,where the tolls are determined by a nonlinear function of a vehicles' travel distance within a cordon,termed as toll charge function.The optimal tolls can give rise to:1) higher total social benefits,2) better levels of equity,and 3) reduced environmental impacts(e.g.,less emission).Firstly,a deterministic equilibrium(DUE) model with elastic demand is presented to evaluate any given toll charge function.The distance tolls are non-additive,thus a modified path-based gradient projection algorithm is developed to solve the DUE model.Then,to quantitatively measure the equity level of each toll charge function,the Gini coefficient is adopted to measure the equity level of the flows in the entire transport network based on equilibrium flows.The total emission level is used to reflect the impacts of distance tolls on the environment.With these two indexes/measurements for the efficiency,equity and environmental issues as well as the DUE model,a multi-objective bi-level programming model is then developed to determine optimal distance tolls.The multi-objective model is converted to a single level model using the goal programming.A genetic algorithm(GA) is adopted to determine solutions.Finally,a numerical example is presented to verify the methodology.

A Decision Model for Selecting Participants in Supply Chain

In order to satisfy the rapid changing requirements of customers, enterprises must cooperate with each other to form supply chain. The first and the most important stage in the forming of supply chain is the selection of participants. The article proposes a two staged decision model to select partners. The first stage is the inter company comparison in each business process to select high efficiency candidate based on inside variables. The next stage is to analyse the combination of different candidates in order to select the most perfect partners according to a goal programming model.

Economical Analysis and Optimization of Recovery Processing Policies about Discarded Product

On the foundation of analyzing the closed loop logistics chain of product with multi-lifecycle, the connotation of environmental value is set forth, recurring to such conceptions as supply chain and value chain. The plotting rules about disassembly tree are discussed in detail. The reachable matrix R of components’ disas- sembly is introduced into distinguishing if disassembly is needed, in combination with disassembly-deciding vector X. Furthermore, the arithmetic of disassembly cost is put forward. And the cost-benefits of components’ reusing, materials’ recycling, safety disposing are dissertated based on the activity-based costing. Then the 0-1 goal-programming model on product recovery processing is established, with components’ demotion calculated. In addition, taking the PC’s recovery processing for example, we put it into application.

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.

THE ROLLING DEVELOPMENT MODEL OF ECOLOGICAL ECONOMY COMBINED WATER CONSERVANCY CONSTRUCTION WITH SCHISTOSOMIASIS CONTROL

On the basis of classification of geographical properties a rolling develop-ment goal programming model is formulated in correspondence with the ecopatterngroup in Four-Lake Area. In this model, the control of schistosomiasis and strengthen-ing of the drainage-storage function are both regarded as ecological measures duringthe process of multi-year(r_0) ecological economic rolling development to make theecoagricultural system in this area ioto a stably operating, ordered, and good cyclicsystem under the support of some controllable factors.

COAL QUALITY CONTROL

Coal storing and loading have much more influence on coal quality. In the paper, a goal programming model has been constructed to determine the ideal quantity extracting from stockpile and silos and a quality control model is inferred under the guidance of maximum theory of dispersed number and practice methods are given to meet production demand, with which a coal mine has achieved a better tech-economic result.

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.

Sequential Ordering Algorithm for Mass Integration: The Case of Direct Recycling

In the last three decades much effort has been devoted in process integration as a way to improve economic and environmental performance of chemical processes. Although the established frameworks have undergone constant refinement toward formulating and solving complicated process integration problems, less attention has been drawn to the problem of sequential applications of mass integration. This work addresses this problem by proposing an algorithm for optimal ordering of the process sinks in direct recycling problems, which is compatible with the typical mass integration formulation. The order consists in selecting the optimal sink at a specific integration step given the selection of the previous steps and the remaining process sources. Such order is identified through a succession of preemptive goal programming problems, namely of optimization problems characterized by more objectives at different priority levels. Indeed, the target for each sink is obtained by maximizing the total flow recycled from the available process sources to this sink and then minimizing the use of pure sources, starting from the purest one;the hierarchy is respected through a succession of linear optimization problems with a single objective function. While the conditional optimality of the algorithm holds always, a thorough statistical analysis including structured to random scenarios of process sources and process sinks shows how frequently the sequential ordering algorithm is outperformed with respect to the total recycled amount by a different selection of process sinks with the same cardinality. Two more case studies proving the usefulness of ordering the process sinks are illustrated. Extensions of the algorithm are also identified to cover more aspects of the process integration framework.