Showing papers on "Goal programming published in 2012"
Book•
30 Oct 2012
TL;DR: This book presents a meta-anatomy of multi-Criteria Problem Structuring and Analysis in a Value Theory Framework and discusses the use of Artificial Intelligence in MCDM, as well as several other approaches to multi-criteria programming.
Abstract: Foreword. Preface. About the Authors. 1. Decision-Aiding Today: What Should We Expect B. Roy. 2. Theory of Vectormaximization: Various Concepts of Efficient Solutions J. Jahn. 3. Duality in Multi-objective Optimization H. Nakayama. 4. Preference Relations and MCDM C.A. Bana e Costa, J.-C. Vansnick. 5. Normative and Descriptive Aspects of Decision Making O.I. Larichev. 6. Meta Decision Problems in Multiple Criteria Decision Making T. Hanne. 7. Sensitivity Analysis in MCDM T. Tanino. 8. Goal Programming S.M. Lee, D.L. Olson. 9. Reference Point Approaches A.P. Wierzbicki. 10. Concepts of Interactive Programming T.J. Stewart. 11. Outranking Approach P. Vincke. 12. Multi-Criteria Problem Structuring and Analysis in a Value Theory Framework V. Belton. 13. Fundamentals of Interior Multiple Objective Linear Programming Algorithms A. Arbel. 14. The Use of Rough Sets and Fuzzy Sets in MCDM S. Greco, et al. 15. Use of Artificial Intelligence in MCDM P. Perny, J.-C. Pomerol. 16. Evolutionary Algorithms and Simulated Annealing for MCDM A.J. Chipperfield, et al. Index.
236 citations
TL;DR: This paper demonstrates that the BFGA is effective by offering the possibility of searching over tens of thousands of plans for trade-off sets of non-dominated plans, and presents an application of the model to the Tongzhou Newtown in Beijing, China.
202 citations
TL;DR: This paper presents a multi-objective optimization framework for matching product architecture strategy to supply chain design, and incorporates the compatibility between the supply chain partners into the model to ensure the long term viability of thesupply chain.
119 citations
TL;DR: Various solution approaches for multiobjective stochastic problems where random variables can be in both objectives and constraints parameters are surveyed and classify and evaluate such transformations with regards to the many proposed concepts of efficiency are proposed.
105 citations
TL;DR: In this paper, an inverse problem is formulated as a mixed integer linear programming problem such that coefficients of the objectives are jointly estimated along with the goal arrival times to the activities, and a unique invariant common prior is used to regularize the estimation method.
Abstract: A parameter estimation method is proposed for calibrating the household activity pattern problem so that it can be used as a disaggregate, activity-based analog of the traffic assignment problem for activity-based travel forecasting. Inverse optimization is proposed for estimating parameters of the household activity pattern problem such that the observed behavior is optimal, the patterns can be replicated, and the distribution of the parameters is consistent. In order to fit the model to both the sequencing of activities and the arrival times to those activities, an inverse problem is formulated as a mixed integer linear programming problem such that coefficients of the objectives are jointly estimated along with the goal arrival times to the activities. The formulation is designed to be structurally similar to the equivalent problems defined by Ahuja and Orlin and can be solved exactly with a cutting plane algorithm. The concept of a unique invariant common prior is used to regularize the estimation method, and proven to converge using the Method of Successive Averages. The inverse model is tested on sample households from the 2001 California Household Travel Survey and results indicate a significant improvement over the standard inverse problem in the literature as well as baseline prescriptive models that do not make use of sample data for calibration. Although, not unexpectedly, the estimated optimization model by itself is a relatively poor forecasting model, it may be used in determining responses of a population to spatio-temporal scenarios where revealed preference data is absent.
102 citations
TL;DR: In this paper, a decision framework based on a novel formulation of the integrated analytical hierarchy process (AHP) is proposed, which not only guarantees accurate selection of an ideal VSM tool, but also aids the decision maker to arrive at the optimum implementation sequence of a chosen set of VSM tools to identify and reduce all wastes present in the system, thereby maximising organisational performance in the shortest timeframe.
Abstract: Since the development of the original value stream mapping (VSM) by Taichi Ohno at Toyota, a number of authors have suggested several additional VSM tools to understand and improve the value stream through waste reduction. A single best VSM tool, though effective in dealing with a certain waste type, becomes redundant as other wastes take centre stage and/or organisational priorities change. To overcome this, a decision framework based on a novel formulation of the integrated analytical hierarchy process (AHP) – pre-emptive goal programming (PGP) has been proposed. This framework not only guarantees accurate selection of an ideal VSM tool, based on the current organisation's priorities, but also aids the decision maker to arrive at the optimum implementation sequence of a chosen set of VSM tools to identify and reduce all wastes present in the system, thereby maximising organisational performance in the shortest timeframe.
84 citations
TL;DR: An index called ''SRI-Attractiveness'' is presented that summarizes the ''social, environmental and ethical performance'' of each SRI-fund for a particular investor and uses Fuzzy Multi-Criteria Decision-Making techniques.
76 citations
TL;DR: The results show that CBM is preferred when the risk possessed by an equipment is very high while CM is preferred in those cases where risk is low and cost is the main consideration, but in cases where both cost and risk are somewhat equally important, TBM is the preferred option.
Abstract: The study deals with the problem of maintenance policy selection for an industrial unit. Maintenance policy selection is a multiple criteria decision making problem. Criteria considered here are 'risk of equipment failure' and the 'cost of maintenance'. The maintenance policies considered are Corrective Maintenance (CM), Time Based Maintenance (TBM), Condition Based Maintenance (CBM) and Shutdown Maintenance (SM). For modeling, fuzzy analytic network process (FANP) has been employed. Chang's extended analysis has been applied to deal with the fuzzy variables and the preferred maintenance policy alternative is found out using FANP analysis. The methodology was applied to a unit of a chemical plant and the suitable maintenance policy was found out for each of the 13 equipment of the unit. The results were compared to the earlier study using Analytic Hierarchal Process and Goal Programming (Arunraj and Maiti, 2010) vis-a-vis the existing practices. The results show that CBM is preferred when the risk possessed by an equipment is very high while CM is preferred in those cases where risk is low and cost is the main consideration. But in cases where both cost and risk are somewhat equally important, TBM is the preferred option.
74 citations
Book•
18 Jan 2012
TL;DR: This book presents a meta-modelling framework for estimating the costs and benefits of various approaches to linear programming, and some of the models developed so far have shown promising results.
Abstract: Part 1 LINEAR PROGRAMMING. Chapter 1 INTRODUCTORY CONCEPTS AND THE GRAPHICAL APPROACH TO LINEAR PROGRAMMING. Chapter 2 THE SIMPLEX METHOD TO SOLVING LINEAR PROGRAMMING PROBLEMS. Chapter 3 SENSITIVITY ANALYSIS USING THE SIMPLEX METHOD AND DUALITY. Chapter 4 FARM-LEVEL LINEAR PROGRAMMING MODELS. Chapter 5 TRANSPORTATION AND ASSIGNMENT MODELS FOR FOOD AND AGRICULTURAL MARKETS. Chapter 6 NATURAL RESOURCE AND ENVIRONMENTAL ECONOMICS APPLICATIONS OF LINEAR PROGRAMMING. Part 2 RELAXING THE ASSUMPTIONS OF LINEAR PROGRAMMING. Chapter 7 INTEGER AND BINARY PROGRAMMING. Chapter 8 OPTIMIZATION OF NONLINEAR FUNCTIONS. Chapter 9 GLOBAL APPROACHES TO NONLINEAR OPTIMIZATION. Chapter 10 RISK PROGRAMMING MODELS. Chapter 11 PRICE ENDOGENOUS MATHEMATICAL PROGRAMMING MODELS. Chapter 12 GOAL PROGRAMMING. Chapter 13 DYNAMIC PROGRAMMING. Index.
70 citations
TL;DR: In this paper, a goal programming model based on a multi-source multi-sink network was developed to locate five renewable energy plants for electric generation in five places located in the autonomous region of Cantabria, in the north of Spain.
Abstract: The capacity expansion planning problem of the renewable energy industry involves decisions regarding the optimal mix of different plant types, locations where each plant should be built, and capacity expansion decisions over the planning horizon for each plant. The aim of this paper is to develop a goal programming model, based on a multi-source multi-sink network, in order to locate five renewable energy plants for electric generation in five places located in the autonomous region of Cantabria, in the north of Spain. As different types of plants can be placed in each location, the goal is to locate one plant in each place, maximizing the number of plants that are matched with comparable locations, in a way that the total deviations from goals are minimized.
68 citations
TL;DR: A goal programming (GP) approach is adopted to solve the SMONDP models and three stochastic GP models with different philosophies are provided to model planners' NDP decision under demand uncertainty, i.e., the expected value GP models, chance-constrained GP model, and dependent-chance GP model.
Abstract: The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implementation, only a good solution that meets the goals of different stakeholders is required. In view of this, we adopt a goal programming (GP) approach to solve the SMONDP models. The GP approach explicitly considers the user-defined goals and priority structure among the multiple objectives in the NDP decision process. Considering different modeling purposes, we provide three stochastic GP models with different philosophies to model planners' NDP decision under demand uncertainty, i.e., the expected value GP model, chance-constrained GP model, and dependent-chance GP model. Meanwhile, a unified simulation-based genetic algorithm (SGA) solution procedure is developed to solve all three stochastic GP models. Numerical examples are also presented to illustrate the practicability of the GP approach in solving the SMONDP models as well as the robustness of the SGA solution procedure.
TL;DR: This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain.
Abstract: This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.
TL;DR: A novel methodology that employs a goal programming technique and genetic algorithm for formulation and evaluation of a multi-objective function, respectively, for optimal planning of distributed generator units in the distribution system is proposed.
Abstract: This article proposes a novel methodology that employs a goal programming technique and genetic algorithm for formulation and evaluation of a multi-objective function, respectively, for optimal planning of distributed generator units in the distribution system. The multi-objective function consists of various performance indices that govern the optimal operation of a distribution system with distributed generator units. The proposed method aims to greatly diminish the dependence in existing methods on the global preference information of the distribution system planner by means of simplicity in problem formulation utilizing a goal programming technique. The capacity of the distribution system to accept distributed generator integration is evaluated such that with the placement of every additional distributed generator unit, the value of multi-objective function reduces without any violation in the system operating constraints. The effectiveness of the proposed method is tested using various distr...
TL;DR: A hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints, can achieve a trade off between the computational time and the solution quality to enable better decision-making.
Abstract: We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.
TL;DR: In this article, a goal programming model based on an environmental/input-output linear programming model is developed and applied to the Spanish economy, combining relations between economic, energy, social and environmental effects, providing valuable information for policy-makers in order to define and examine the different goals that must be implemented to reach sustainability.
TL;DR: A decision support system (DSS) which can accommodate evaluation models to optimise multimodal transportation routing within Greater Mekong sub-region countries (GMS) is developed and the Zero-One Goal Programming (ZOGP) is presented.
TL;DR: Two original models for ranking of efficient units in data envelopment analysis based on multiple criteria decision making techniques—goal programming and analytic hierarchy process are presented.
Abstract: Data envelopment analysis models usually split decision making units into two basic groups, efficient and inefficient. Efficiency score of inefficient units allows their ranking but efficient units cannot be ranked directly because of their maximum efficiency. That is why there are formulated several models for ranking of efficient units. The paper presents two original models for ranking of efficient units in data envelopment analysis—they are based on multiple criteria decision making techniques—goal programming and analytic hierarchy process. The first model uses goal programming methodology and minimizes either the sum of undesirable deviations or maximal undesirable deviation from the efficient frontier. The second approach is analytic hierarchy process model for ranking of efficient units. The two presented models are compared with several super-efficiency models and other approaches for ranking decision making units in DEA models including definitions based on distances from optimistic and pessimistic envelopes and cross efficiency evaluation models. The results of the analysis by all presented models are illustrated on a real data set—evaluation of 194 bank branches of one of the Czech commercial banks.
TL;DR: The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains.
Abstract: Supported by a network of researchers and practitioners, the goal programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision-making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision-making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains.
TL;DR: This paper develops models for selecting portfolios for conventional and socially responsible investment (SRI) mutual funds according to the preferences of the SRI investor, and the proposed fuzzy goal programming (FGP) model is applied to a database of UK mutual funds.
Abstract: This paper develops models for selecting portfolios for conventional and socially responsible investment (SRI) mutual funds according to the preferences of the SRI investor. This involves constructing an investment portfolio that takes into account both financial and social, environmental and ethical (SEE) criteria. The optimal portfolio selection problem is solved when the expected returns of the assets as well as the periodic returns are not precisely known. Instead, incomplete information on the parameters of the model is modeled by fuzzy numbers, which include the 'true' values and are consistent with the Decision Maker's beliefs on assets' performance. In this paper, the financial criteria taken into account are the expected return and the difference between the returns of the portfolio and a pre-specified benchmark index i.e. a strategy of tracking error (TE) is followed. Moreover, we assume that the investor's preferences about SEE features of the portfolio are imprecisely known. In order to model these flexible preferences we propose to use fuzzy decision making. The multidimensional nature of the problem leads us to work with techniques of multiple criteria decision making (MCDM), namely goal programming (GP), and the incomplete information is handled by a fuzzy robust approach. The proposed fuzzy goal programming (FGP) model is applied to a database of UK mutual funds.
Posted Content•
TL;DR: In this article, the authors highlight the main methodological developments of the stochastic goal programming (GP) model and present an overview of its applications in several domains, and present a survey of the main applications of the GP model.
Abstract: Supported by a network of researchers and practitioners, the goal programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision-making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision-making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains.
TL;DR: A novel formulation of fuzzy multi-choice goal programming (FMCGP) is presented that not only improves the applicability of goal programming in real world situations but also provides useful insight about the solution of a new class of problems.
TL;DR: This paper attempts to form CBVCMSs with DRC settings using a multi-objective mathematical model with a Goal Programming (GP) approach and proposes an algorithm to find optimum or near-to-optimum solutions.
TL;DR: A multiobjective stochastic program model to assign beds to hospital departments is proposed and a certainty equivalent program was derived based on a mixture between the chance constrained approach, the recourse approach and the goal programming approach.
Abstract: In this paper, we study hospital bed capacity management for a set of existing hospitals when the demand for beds is random. We propose a multiobjective stochastic program model to assign beds to hospital departments. We consider three objective functions to be minimized, which are the cost of creation and management of new beds and the number of nurses and physicians working in these hospitals, subject to demand satisfaction of three kinds of health-care specialities. A certainty equivalent program was derived based on a mixture between the chance constrained approach, the recourse approach and the goal programming approach. Empirical results using real data from 157 Tunisian national hospitals are reported.
TL;DR: In this article, an integrated genetic algorithm based grey goal programming (G3) approach is proposed to solve the part supplier selection problem, where both qualitative and quantitative factors at once in one model and to use the grey theory to cover the lack of inform...
Abstract: The problem of part supplier selection is a major concern for all manufacturers when seeking to enhance the products’ quality and productivity. The objective of this paper is to propose an integrated genetic algorithm based grey goal programming (G3) approach to solve the part supplier selection problem. The main factor in part supplier selection is the assembly relation of the parts so as to find the suitable suppliers combination for the parts of a product. We first identify the main factors affected on supplier selection. We then present a grey-based goal programming model to work as the fitness function to evaluate the suppliers with respect to the total deviation the factors have from the ideal values. Since the objective is to find the best solution, a genetic algorithm is used to solve this problem for faster and better evaluation. The novelty of this integrated approach is to apply both qualitative and quantitative factors at once in one model and to use the grey theory to cover the lack of inform...
TL;DR: Multi-response surfaces and their related stochastic nature have been modeled and optimized by Goal Programming in which the weights of response variables have been obtained through a Group Decision Making (GDM) process.
TL;DR: In this paper, an alternative multi-choice goal programming formulation based on the conic scalarizing function with three contributions is proposed, which reduces auxiliary constraints and additional variables, and the proposed model guarantees to obtain a properly efficient (in the sense of Benson) point.
Journal Article•
TL;DR: A goal programming model with utility functions to maximize the number of reading materials bought and the utility for each field’s user for bought materials is built and applied to a public university library.
Abstract: The funding of library is optimally utilized to achieve the need of users. We build a goal programming model with utility functions to maximize the number of reading materials bought and the utility for each field’s user for bought materials. The model is then applied to a public university library. The goal programming model illustrated an optimum solution for funding allocation with utility of each field’s user of the library.
TL;DR: This paper shows a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach, and provides sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change ofolerance values.
Abstract: In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers. A possible relaxation of the higher level decision is considered for avoiding decision deadlock due to the conflicting nature of objective functions. Then, fuzzy goal programming approach is used for achieving the highest degree of each of the membership goal by minimizing negative deviational variables. We also provide sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change of tolerance values with the help of a numerical example.
TL;DR: In this article, the authors employ the analytic network process (ANP) to model the interactions between eight SQM strategies and the three types of resources (human, organisational and technological) needed for effective strategy implementation, and then formulates a goal programming (GP) model to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources.
Abstract: Purpose – The purpose of this paper is to assist organizations in understanding the nature of quality management from a resource‐based perspective by investigating the relationship between strategies needed to drive quality enhancement, and resources being allocated to support effective strategy implementation. The resource‐based view of TQM elements led this case study research to deal with quality management from a strategic viewpoint, or what is known as Strategic Quality Management (SQM).Design/methodology/approach – The paper employs the analytic network process (ANP) to model the interactions between eight SQM strategies and the three types of resources (human, organisational and technological) needed for effective strategy implementation. The paper then formulates a goal programming (GP) model in order to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources. Using a case study approach, the hybrid ANP‐GP methodology is employed to illustrate the a...
TL;DR: This study deduces a new method, which is called the multi-coefficients goal programming, for group pricing discrimination problems, and an example is given to illustrate the correctness and usefulness of the proposed model.