Showing papers on "Goal programming published in 2013"

Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys

Abstract: The roots of Multiple Criteria Decision Making and Multiple Criteria Optimization were laid by Pareto at the end of the 19th century, and since then the discipline has prospered and grown, especially during the last three decades. Today, many decision support systems incorporate methods to deal with conflicting objectives. The foundation for such systems is a mathematical theory of optimization under multiple objectives. Since its beginnings, there have been a vast number of books, journal issues, papers and conferences that have brought the field to its present state. Despite this vast body of literature, there is no reliable guide to provide an access to this knowledge. Over the years, many literature surveys and bibliographies have been published. With the ever rapidly increasing rate of publications in the area and the development of subfields, these were mostly devoted to particular aspects of multicriteria optimization: Multiobjective Integer Programming, Multi-objective Combinatorial Optimization, Vector Optimization, Multiobjective Evolutionary Methods, Applications of MCDM, MCDM Software, Goal Programming. Hence the need for a comprehensive overview of the literature in multicriteria optimization that could serve as a state of the art survey and guide to the vast amount of publications. Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys is precisely this book. Experts in various areas of multicriteria optimization have contributed to the volume. The chapters in this book roughly follow a thread from most general to more specific. Some of them are about particular types of problems (Theory of Vector Optimization, Nonlinear Multiobjective Programming, Fuzzy Multiobjective Programming, Multiobjective Combinatorial Optimization, Multicriteria Scheduling Problems), while the others are focused on multi-objective methodologies (Goal Programming, Interactive Methods, Evolutionary Algorithms, Data Envelopment Analysis). All contributing authors invested great effort to produce comprehensive overviews and bibliographies and to have references that are as precise as possible.

A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection

Abstract: In this paper, a new hybrid fuzzy multiple criteria group decision making (FMCGDM) approach has been proposed for sustainable project selection. First, a comprehensive framework, including economic, social, and environmental effects of an investment, strategic alliance, organizational readiness, and risk of investment has been proposed for sustainable project selection. As the relative importance of the criteria of the proposed framework are hard to find through several conflictive preferences of a group of Decision Makers (DMs) so, a goal programming (GP) has been supplied to this aim considering multiplicative and fuzzy preference relation. Then, a fuzzy TOPSIS method has been developed to assess the fitness of investment chances. It is based on Preference Ratio (PR), which is known as an efficient ranking method for fuzzy numbers, and a fuzzy distance measurement. The properties of proposed hybrid approach make it robust for modeling real case of uncertain group decision making problems. The FMCGDM has been developed through a linkage between Lingo 11.0, MS-Excel 12.0, and Visual Basic 6.0. The proposed hybrid approach has been applied in a real case study called Iranian financial and credit institute for sustainable project selection.

An integrative supplier selection model using Taguchi loss function, TOPSIS and multi criteria goal programming

Abstract: Some of the key factors affecting the selection of supplier are price, quality, delivery, satisfaction, and warranty degree. The present paper is an extension of previous related work to select the appropriate supplier. This paper deals with an integrative approach considering Taguchi's loss function, Technique for Order preference by similarity to ideal solution (TOPSIS) and Multi criteria goal programming. The model is split up into three phases. In the first phase, the quality losses are identified using Taguchi's loss function. In the second phase, suitable factors are identified with different weights from TOPSIS and in the third phase, a goal programming model is developed to identify the best performing supplier with the weights and the loss associates. The purpose of this paper is to integrate different criteria levels to select relatively better performing supplier. A case is also presented and finally a comparison with data envelopment analysis (DEA) is discussed.

An Overview of Optimization Formulations for Multiresponse Surface Problems

Abstract: Many industrial applications involve more than one quality characteristic. For example, in robust parameter design, the quality characteristics include the process mean and process variance. Such applications lead to multiresponse surface problems in which it is necessary to determine optimal operating conditions according to some specified optimization criterion involving the quality characteristics. The purpose of this article is to address this problem from a multiobjective decision-making framework. The foremost approaches in multiresponse optimization are categorized and integrated. Guidelines are presented to help select appropriate formulations. Moreover, the applicability and computational aspects of the methods in various decision-making contexts are discussed. Numerical examples are also provided. Copyright © 2012 John Wiley & Sons, Ltd.

Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China

Abstract: A Coarse-Grained Parallel Genetic Algorithm (CGPGA) is utilized to search for near-optimal solutions for land use allocation optimization problems in the context of multiple objectives and constraints. Plans are obtained based on the trade-off among three spatial objectives including ecological benefit, accessibility and compatibility. The Multi-objective Optimization of Land Use model integrates these objectives with the fitness function assessed by reference point method (goal programming). The CGPGA, as the first coupling in land use allocation optimization problems, is tested through the experiments with one processor, two processors and four processors to pursue near-optimal land use allocation scenarios and the comparison to these experiments based on Generic Genetic Algorithm (GGA), which clearly shows the robustness of the model we proposed as well as its better performance. Furthermore, the successful convergent (near-convergent) case study utilizing the CGPGA in Tongzhou Newtown, Beijing, China evinces the capability and potential of CGPGA in solving land use allocation optimization problems with better efficiency and effectiveness than GGA.

On the location selection problem using analytic hierarchy process and multi-choice goal programming

Abstract: Location selection is a crucial decision in cost/benefit analysis of restaurants, coffee shops and others. However, it is difficult to be solved because there are many conflicting multiple goals in the problem of location selection. In order to solve the problem, this study integrates analytic hierarchy process AHP and multi-choice goal programming MCGP as a decision aid to obtain an appropriate house from many alternative locations that better suit the preferences of renters under their needs. This study obtains weights from AHP and implements it upon each goal using MCGP for the location selection problem. According to the function of multi-aspiration provided by MCGP, decision makers can set multi-aspiration for each location goal to rank the candidate locations. Compared to the unaided selection processes, the integrated approach of AHP and MCGP is a better scientific and efficient method than traditional methods in finding a suitable location for buying or renting a house for business, especially under multiple qualitative and quantitative criteria within a shorter evaluation time. In addition, a real case is provided to demonstrate the usefulness of the proposed method. The results show that the proposed method is able to provide better quality decision than normal manual methods.

Fuzzy goal programming technique to solve multiobjective transportation problems with some non-linear membership functions

Abstract: The linear multiobjective transportation problem is a special type of vector minimum problem in which constraints are all equality type and the objectives are conicting in nature. This paper presents an application of fuzzy goal programming to the linear multiobjective transportation problem. In this paper, we use a special type of nonlinear (hyperbolic and exponential) membership functions to solve multiobjective transportation problem. It gives an optimal compromise solution. The obtained result has been compared with the solution obtained by using a linear membership function. To illustrate the methodology some numerical examples are presented.

A decision support system for procurement risk management in the presence of spot market

Abstract: In the presence of spot market, this paper presents a decision support system to model risks for procurement processes and to design a robust purchasing plan, including supplier selection and order allocation. Taking advantages of contract supplier and spot market, the buyer can better meet business requirements in this dynamic business environment. However, there are limitations of existing methods for modeling multiple correlated risks to support decision makers for allocating orders among multiple suppliers in the presence of spot market. Therefore, Monte Carlo simulation algorithm termed as Expected Profit-Supply at Risk (A-EPSaR) is proposed to quantify each supplier's risk so as to let decision maker realize the trade-off between profit and risk. The goal programming model helps to allocate orders among the supplier pool and the contract-spot allocation model can assign orders between the spot market and the supplier pool, respectively. The significance of this paper is to propose a novel decision support framework which helps the buyer to make optimal and robust procurement decision including supplier selection and order allocation among multiple supplier sources in the existence of correlated demand, yield and spot price uncertainties. A case study is used to illustrate the performance of the proposed framework and the proposed methods show the promising result.

A differential evolution algorithm to solve multi-skilled project portfolio scheduling problems

Abstract: A multiskilled project portfolio scheduling problem (MSPPSP) is an extension of a multiobjective multimode resource-constrained project portfolio scheduling problem that is generally propounded to schedule a set of projects performed by human skills in an organization The main idea of the MSPPSP is to consider resources that are called staff members to perform projects’ activities in different required skills Since the required staff members have various skills, different combinations of skills are applied to accomplish the project These definitions cause to encounter a huge number of modes while performing activities of a project In this paper, a novel goal programming model for the multiobjective MSPPSP with precedence constraints that aim at finding a minimum deviation from the expected time to complete each project and assignment of resources is presented To solve such a hard problem, an efficient metaheuristic algorithm based on differential evolution (DE) is developed To evaluate the efficiency of the proposed DE algorithm, the results are compared to the results of the tabu search algorithm and the optimal results The comparison confirms the effectiveness of the DE algorithm Finally, regarding the size of organizations in terms of staff members, the maximum number of the determined structure projects, which is performable with minimum delay from aspiration times, is examined

A fuzzy grey goal programming approach for aggregate production planning

Abstract: Aggregate planning is a medium-range capacity planning that suggests the production strategies in order to meet the forecasted demand considering the capacity constraints. As the period of planning length increases, the uncertainty of information will grow. Regarding this point, in this paper, a multi-objective model is proposed for aggregate planning problem in which the parameters of the model are expressed in the form of grey numbers. The suggested grey multi-objective model is solved based on a goal programming problem with fuzzy aspiration levels. The model is applied in a real-world problem, and its results are illustrated. The obtained results give a range for decision variables, and decision makers can handle the inevitable uncertainty of information by using these ranges.

Goal programming in forest management: customising models for the decision-maker's preferences

Abstract: Within a multi-criteria perspective goal programming (GP) is the most widely used approach for addressing forest management problems of a continuous nature. A key element of a GP model is the achievement function. Each type of achievement function is supported by a very precise structure of decision-makers' preferences. However, in many of the GP applications reported in the forest management literature, the achievement function of the GP model is chosen without justifying the reasons for its election. However, the right election of a GP achievement function is a crucial matter if we want the GP model to capture the essential features of the forest management reality analysed. In coherence with these ideas, this article aims: (1) to provide a precise preferential interpretation of the different GP achievement functions and (2) to provide some insight and guidelines about how to choose the most suitable GP achievement function for a precise forest resource management problem. Hopefully the ideas p...

A cardinality constrained stochastic goal programming model with satisfaction functions for venture capital investment decision making

Abstract: Venture capital has proven to be an essential resource for economic growth, especially in some technological clusters. The focus is on the way the venture capitalist makes the investment decision and the portfolio selection. The aim of this paper is to formulate the venture capital investment problem through the Goal Programming model where the Financial Decision-Maker’s preferences will be explicitly incorporated through the concept of satisfaction functions. The proposed model will be illustrated by using data from an Italian venture capital fund.

Stock index tracking by Pareto efficient genetic algorithm

Abstract: This paper proposes a heuristic searching approach on construction of a tracking portfolio, which is able to get the average market return and can even outperform some hedge funds that are managed actively. The tracking portfolio is expected to replicate the performance of a benchmark index return with a part of its component stocks while reducing the cost of transaction by limiting the number of rebalancing and unnecessary investment on less influential component stocks. The mathematical model being proposed is based on a hybrid genetic algorithm with a self-adaptive evolving mechanism. In order to enhance the model efficiency, we optimize the original genetic algorithm by applying Pareto efficiency as utility measure and goal programming for the inevitable conflicts of multiple objectives/interests. The proposed approach provides a comprehensive solution to index tracking problem by considering as many practical issues as possible. The constructed portfolio has a satisfactory performance on experiments based on CSI300, FTSE100 and HSI data. The proposed formulation of index tracking is therefore believed to be a good alternative to many current techniques.

TOPSIS Based Approach to Scoring Negotiating Offers in Negotiation Support Systems

Abstract: In this paper we analyze the possibility of applying the technique for order preferences by similarity to ideal solution (TOPSIS) to building the scoring system for negotiating offers. TOPSIS is a multiple criteria decision making method that is based on measuring distances between alternatives under consideration and two bipolar reference alternatives, a positive and negative ideal. Thus the criteria used for the evaluation of alternatives should be described using strong scales. However, in the negotiation, the issues are very often described qualitatively, which results in ordinal or even nominal variables that must be taken into consideration in offers’ evaluation process. What is more, TOPSIS may be applied to solving the discrete decision problems while the negotiation space may be defined by the means of continuous variables too. In this paper we try to modify the TOPSIS algorithm to make it applicable to negotiation support and, moreover, discuss the following methodological issues: using TOPSIS for a negotiation problem with continuous negotiation space; selecting the distance measure for adequate representation of negotiator’s preferences and measuring distances for qualitative issues. Finally, we propose a simple additional mechanism that allows for building the TOPSIS-based scoring system for negotiating offers and does not involve negotiators in time consuming and tiresome preference elicitation process. This mechanism requires from negotiators to construct examples of offers that represent some categories of quality and then by using a goal programming approach it infers all the parameters required by the TOPSIS algorithm. We also show a simple prototype software tool that applies the TOPSIS modified algorithm and may be used in electronic negotiation support.

Determining design requirements in QFD using fuzzy mixed-integer goal programming: application of a decision support system

Abstract: In product development, the identification of critical design requirements (DRs) is key to satisfying customer needs because it helps produce more successful products in a shorter time. Quality function deployment (QFD) is a tool used in product development to systematically determine the DRs so as to attain higher customer satisfaction. In the QFD process, the simultaneous optimisation of more than one conflicting objective is generally required. However, it is very difficult for decision makers to determine the goal value of each objective in imprecise and uncertain environments. In order to overcome this problem, the present study proposes a fuzzy mixed-integer goal programming model that determines a combination of optimal DR values. Different from the existing fuzzy goal programming models, the values of the DRs in the proposed model are taken as discrete. Finally, a new Decision Support System is developed. The new system integrates QFD and mathematical programming, enabling the design team to effec...

Portfolio Selection with Skewness: A Comparison of Methods and a Generalized One Fund Result

Abstract: This contribution compares existing and newly developed techniques for geometrically representing mean-variance-skewness portfolio frontiers based on the rather widely adapted methodology of polynomial goal programming (PGP) on the one hand and the more recent approach based on the shortage function on the other hand. Moreover, we explain the working of these different methodologies in detail and provide graphical illustrations in relation to the goal programming literature in operations research. Inspired by these illustrations, we prove two new results: a formal relation between both approaches and a generalization of the well-known one fund separation theorem from traditional mean-variance portfolio theory.

Heuristic dynamic programming with internal goal representation

Abstract: In this paper, we analyze an internal goal structure based on heuristic dynamic programming, named GrHDP, to tackle the 2-D maze navigation problem. Classical reinforcement learning approaches have been introduced to solve this problem in literature, yet no intermediate reward has been assigned before reaching the final goal. In this paper, we integrated one additional network, namely goal network, into the traditional heuristic dynamic programming (HDP) design to provide the internal reward/goal representation. The architecture of our proposed approach is presented, followed by the simulation of 2-D maze navigation (10*10) problem. For fair comparison, we conduct the same simulation environment settings for the traditional HDP approach. Simulation results show that our proposed GrHDP can obtain faster convergent speed with respect to the sum of square error, and also achieve lower error eventually.

A bi-objective network design model for multi-period, multi-product closed-loop supply chain

Abstract: Concerns over environmental degradation legislative requirements and growing business needs have fueled the growth of closed-loop supply chains (CLSCs). This paper addresses a bi-objective network design problem for multi-period, multi-product CLSC to minimize the total supply chain costs and to maximize the service efficiency of the warehouses and hybrid facilities. We develop a bi-objective mixed integer linear programming model to assist decisions in (1) location/operating decisions for warehouses, hybrid facilities and manufacturing facilities and (2) production and distribution of products between stages in the supply chain. Goal programming models and compromise programming techniques are used to solve the problem. An application of the model is demonstrated using a case study from the literature.

Dynamic Programming Approach for Exact Decision Rule Optimization

Abstract: This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository.

Random fuzzy multi-objective linear programming: Optimization of possibilistic value at risk (pVaR)

Abstract: This paper considers multiobjective linear programming problems (MOLPP) where random fuzzy variables are contained in objective functions and constraints. A new decision making model optimizing possibilistic value at risk (pVaR) is proposed by incorporating the concept of value at risk (VaR) into possibility theory. It is shown that the original MOLPPs involving random fuzzy variables are transformed into deterministic problems. An interactive algorithm is presented to derive a satisficing solution for a decision maker (DM) from among a set of Pareto optimal solutions. Each Pareto optimal solution that is a candidate of the satisficing solution is exactly obtained by using convex programming techniques. A simple numerical example is provided to show the applicability of the proposed methodology to real-world problems with multiple objectives in uncertain environments.