This approach is sometimes known as sequential goal programming or preemptive goal programming as priorities cannot be. The method can be used to help decision makers make the bestappropriate policy. Solving a biobjective vehicle routing problem under uncertainty by a revised multichoice goal programming approach pages 283302 download pdf. Optimal solution of multichoice mathematical programming. Goal programming as a well known technique has been widely used for solving multi objective decision making problems. Goal programming, its application in management sectors. The advantage of this proposed method is that it allows decision makers to set multiple aspiration levels for the decision criteria. Global heritage dth dtt classifieds etail payments paytv print ecommerce listed internet global platform operator t e. Multichoice south africa group 2010 sustainability report.
Generally, the decision variable in transportation problem tp is considered as real variable, but here the decision variable in each node is chosen from a. Multichoice goal programming approach to solve multiobjective. Abstractstochastic programming is an art of modeling optimization problems in an environment, where randomness occurs. By examining the way the previous study is using utility function with mcgp, some drawbacks of such a. Therefore, to make more practical decisions, this paper is intended to propose an integrated technique for order preference by similarity to ideal solution topsis in fuzzy environment with multichoice goal programming mcgp to handle the supplier assessment and order allocation for a. An integrated maximizing consistency and multichoice goal.
Firstly, the hybrid decision information including crisp numbers, intervals, intuitionistic fuzzy numbers and. However, the problem cannot be solved by current goal programming gp techniques. The resources need to produce x and y are twofold, namely machine time for automatic processing and craftsman time for hand finishing. A numerical example of application is also presented. Solving fuzzy transportation problem using multichoice. But, one of the limitations of linear programming is that its objective.
This results in a linear programming problem where some of the constraints are crisp and the remaining fuzzy. Evaluation model for applying an elearning system in a. This paper explores the study of fuzzy transportation problem ftp using multichoice goalprogramming approach. List of organizational behaviour multiple choice questions with answers. Goal programming is one approach to dealing with problems of this kind. In order to solve this problem multichoice goal programming mcgp was proposed by chang 2007a. Then, in the model formulation, the objectives with multiplicity of aspiration levels are converted into the standard form of goals in goal programming approach to. Abstract this study proposes an idea of another way of using utility functions with multichoice goal programming mcgp models. Fuzzy multichoice goal programming for supplier selection. Solving multichoice linear goal programming problem with preemptive priorities.
Chang, multichoice goal programming, omega, the inter. For example, goal functions may be linear or nonlinear. The study deals with the multichoice mathematical programming problem, where the right hand side of the constraints is multichoice in nature. The aim of this paper is to transform such problems to a standard mathematical linear programming problem. Popular approaches for solving multiobjective problems in the literature can be categorized into two main groups.
Revised multichoice goal programming for multiperiod, multistage. Pdf solving multichoice linear goal programming problem. One half of the book is devoted to theoretical aspects, covering a broad range of multiobjective methods such as multiple linear programming, fuzzy goal programming, data envelopment. Revised multichoice goal programming sciencedirect.
Pdf solving multichoice linear goal programming problem with. In other words, goal programming is a powerful tool to tackle multiple and incompatible goals of an enterprise. This paper presents the study of a multichoice multiobjective transportation problem mcmotp when at least one of the objectives has multiple aspiration levels. Pdf multichoice goal programming with trapezoidal fuzzy. The proposed model is validated through a case study conducted in the city of amol. Goal programming formulation to deal with these two objectives in our example problem via gp, we need to introduce extra variables these variables deal wi th the deviation from the goal for each objective. To solve such a model, a revised multichoice goal programming rmcgp approach is then used with the purpose of finding a compromise solution.
Multichoice goal programming methodology is applied for both the cases. The explicit definition of goal programming was given by charnes and cooper 1961. The table below gives the number of minutes required for each item. In fact, the conflicts of resources and the incompleteness of available information make it almost impossible for dms to build a reliable mathematical model for representation of their preferences. The synergy amongst projects and the outsourcing option are also taken into account in order to provide a more realistic selection process. However, this may not happen in reality due to conflicts among the objectives. First, the in uencing criteria are derived with respect to higher priorities from the fuzzy dematel method under the balanced scorecard framework. This paper proposes a new framework to identify the optimal project portfolio. A revised multichoice goal programming method is applied to solve the multiobjective model.
Bilevel multiobjective production planning problem with. Weighted goal programming with weighted goal programming, the objective is to minimize w weighted sum of deviations from the goals. The methodology known as goal programming gp stems from the work of charnes. Five groups of the test examples are characterized by the number of goals n 8, 9. Multichoice group of companies 2009 sustainability report. Multichoice goal programming with trapezoidal fuzzy. Within the field of multiple criteria decision making, this volume covers the latest advances in multiple objective and goal programming as presented at the 2nd international conference on multiobjective programming and goal programming, torremolinos, spain, may 16 18, 1996.
The book is dedicated to multiobjective methods in decision making. Student selection and assignment methodology based on. Integrated multichoice goal programming and multisegment goal programming for supplier selection considering imperfectquality and pricequantity discounts in a multiple sourcing environment. A new way using utility functions for multichoice goal. However, research based on fuzzy multiattribute decision making fmadm approach in ranking resilient suppliers in logistic 4. Multichoice goal programming multichoice goal programming chang, chingter 20070801 00. However, the major limitation of goal programming is that can only use aspiration levels with scalar value for solving multiple objective problems. This study proposes an analytic hierarchy process ahpmultichoice goal programming mcgp evaluation model as a decision aid to help decision makers obtain appropriate online it tools considering the time limitations of teachers and students. In fact, many decisions must be made in the face of competing interests in an atmosphere of confrontation.
An improved multichoice goal programming approach for. A year later, change proposed a revised multichoice goal programming model for goal programming problems with continuous aspiration level 5. By examining the way the previous study is using utility function. Analysis of a multiechelon supply chain problem using. Chairman multichoice nigeria adewunmi ogunsanya, tom adaba, former and first director general nbc and managing director, multichoice nigeria john ugbe. However, the problem of multichoice linear programming cannot be solved directly by standard linear or nonlinear programming techniques. The probability density function of extreme value distribution type i is as follows. Multichoice goal programming with utility functions.
Multichoice goal programming with utility functions multichoice goal programming with utility functions chang, chingter 20111201 00. Traditional madm approach fails to address the resilient supplier selection problem in logistic 4. A preemptive goal programming for allocating students into. The proposed approach was applied in an industrial engineering department. Goal programming is an extension of linear programming which handles multiobjective optimization where the individual objectives are often conflicting. A goal programming approach to multichoice multiobjective. Hossein yousefi, reza tavakkolimoghaddam, mahyar taheri bavil oliaei, mohammad mohammadi, ali mozaffari. Pdf revised multichoice goal programming for integrated supply. Optimisation of supplier selection with taguchi loss.
Ahp and multichoice goal programming integration for. Goal programming is one of the oldest multi criteria decision making techniques aiming at optimizing several goals and at the same time minimize the deviation for each of the objectives from the desired target. Solving a biobjective vehicle routing problem under. Supplier selection problem has gained extensive attention in the prior studies. A general transformation technique based on a binary variable has been used to transform the multichoices parameters of the problem into their equivalent deterministic form. Revised multichoice goal programming approach is applied to solve this mixed integer linear programming model. An introduction 2 firms often have more than one goal they may want to achieve several, sometimes contradictory, goals in linear and integer programming methods the objective function is measured in one dimension only it is. Following the idea of mcgp this study proposes a new concept of level achieving in the utility. The weights are the penalty weights for missing the goal. The multichoice goal programming allows the decision maker to set. Keywords goal programming, multichoice aspiration levels, ranking function. Multichoice 2009 sustainability report page 2 strategy and analysis multichoice enriches lives. In order to improve the utility of gp and solve the mcal problem, this paper proposes.
Multiplecriteria fuzzy group decisionmaking with multi. This paper deals with the modeling and optimization of a bilevel multiobjective production planning problem, where some of the coefficients of objective functions and parameters of constraints are multichoice. The result of our indepth investigations of the two main gp methods, lexicographic and weighted gp together with their distinct application areas is. Integrated multichoice goal programming and multisegment. Request pdf revised multichoice goal programming chang c. Introduce new changing cells, amount over and amount under, that will measure how much the current solution is over or under each goal. Multichoice goal programming approach to solve multiobjective probabilistic programming problem. Hakeemurrehman iqtmpu 1 ra o goal programming gp 2. The method can improve the practical utility of mcgp. To proceed we need to decide a numeric goal for each objective. In this study, an integrated approach of fuzzy multimoora and multichoice conic goal programming is proposed to consider criteria in choosing the best students and define the optimum assignments among the predefined programs to maximize both the total preference value and total ranking value. Multichoice goal programming approach to solve multi. Supplier selection model using taguchi loss function. Whether its through paytelevision, the internet, mobile phones or any other device on the digital horizon, our goal remains the same.
Afterwards, a utilitybased multichoice goal programming. Its our mission to brighten peoples lives with compelling digital media content. Two numerical examples are presented for illustrating the proposed problem. Project portfolio selection is an important problem for having an e cient and e ective project management. Revised multichoice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. Goal programming applications in financiill management 2 several classes of goal programming can be obtained, depending on the nature ofthe goal functions. In this study, an integrated approach based on the analytic hierarchy process ahp and multichoice goal programming mcgp model was proposed to construct an efficient course plan following the bologna process. Two cases are dealt with where the goal function assumes a set of choices and a range.
838 937 1541 189 556 102 228 1364 65 1334 1529 697 958 1224 56 1477 882 1472 685 872 931 439 919 686 1024 138 517 1400 48 254 889 542 1033 1066 1426 254 712 1203 79 997