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Goal programming

About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.


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Journal ArticleDOI
TL;DR: This paper develops three stochastic goal programming formulations and highlights the usefulness of the approach on a small forest holding.
Abstract: Developing a forest management plan in a multicriteria perspective is traditionally accomplished utilizing simulation and optimization tools as a means to predict and optimize a variety of criteria...

32 citations

Journal ArticleDOI
TL;DR: This article proposes a linearisation strategy to formulate the FMOMBFP problem in which extra binary variable is not required, and achieves the highest membership value of each fuzzy goal defined for the fractional objective function.
Abstract: Fuzzy multiple objective fractional programming (FMOFP) is an important technique for solving many real-world problems involving the nature of vagueness, imprecision and/or random. Following the idea of binary behaviour of fuzzy programming (Chang 2007), there may exist a situation where a decision-maker would like to make a decision on FMOFP involving the achievement of fuzzy goals, in which some of them may meet the behaviour of fuzzy programming (i.e. level achieved) or the behaviour of binary programming (i.e. completely not achieved). This is turned into a fuzzy multiple objective mixed binary fractional programming (FMOMBFP) problem. However, to the best of our knowledge, this problem is not well formulated by mathematical programming. Therefore, this article proposes a linearisation strategy to formulate the FMOMBFP problem in which extra binary variable is not required. In addition, achieving the highest membership value of each fuzzy goal defined for the fractional objective function, the proposed method can alleviate the computational difficulties when solving the FMOMBFP problem. To demonstrate the usefulness of the proposed method, a real-world case is also included.

32 citations

Journal ArticleDOI
TL;DR: An additive-consistency- and consensus-based method for group decision making (GDM) with LPRs is developed and a consistency index is proposed for gauging the agreement degree among individual L PRs.
Abstract: Linguistic preference relations (LPRs) can indicate the decision makers (DMs)’ qualitative pairwise judgments regarding a set of alternatives in uncertain multicriteria decision-making problems. This paper examines several goal programming models for managing the additive consistency and consensus of LPRs and then develops an additive-consistency- and consensus-based method for group decision making (GDM) with LPRs. First, this paper offers a consistency index to quantify the consistency level for LPRs and define acceptable consistent LPRs. For an LPR that is unacceptably additive consistent, several additive-consistency-based programming models are developed to address the inconsistency and to establish an acceptably consistent LPR. Then, an additive-consistency-based procedure to generate the priority weight vector from the LPR is offered. An additive-consistency-based algorithm for decision making with an LPR is presented. Subsequently, considering the consensus in GDM, a consensus index is proposed for gauging the agreement degree among individual LPRs. Regarding individual LPRs that do not exhibit acceptably additive consistency or acceptable consensus, several goal programming models to derive new LPRs with acceptable consistency and consensus are provided. Afterward, the DMs’ weights are determined objectively, and individual LPRs are integrated into a collective LPR. An additive-consistency- and consensus-based GDM method with a group of LPRs is developed. Finally, two practical numerical examples are offered, and a comparative analysis is presented.

32 citations

Journal ArticleDOI
TL;DR: An example is given which demonstrates that using a decision theory analysis for the basic chance-constrained model of stochastic linear programming may lead to an apparent dilemma, namely, 0 > EVSI > EVPI.
Abstract: An example is given which demonstrates that using a decision theory analysis for the basic chance-constrained model of stochastic linear programming may lead to an apparent dilemma, namely, 0 > EVSI > EVPI. The problem is discussed and a resolution suggested.

32 citations

Journal ArticleDOI
TL;DR: In this article, a systematic way for allocating resources among different research and development (R&D) projects in a multiple objective environment is developed, where a Delphic Goal Programming (DGP) is proposed as follows: initially, a Delphi inquiry is conducted to identify the objectives to be considered in problem formulation, and successive rounds of Delphi are then utilized to prioritize these objectives, and to determine the relative weight and the aspiration level for each objective.
Abstract: A systematic way for allocating resources among different research and development (R&D) projects in a multiple objective environment is developed. For this purpose, the Delphic Goal Programming (DGP) is proposed as follows: initially, a Delphi inquiry is conducted to identify the objectives to be considered in problem formulation. Successive rounds of Delphi are then utilized to prioritize these objectives, and to determine the relative weight and the aspiration level for each objective. Finally, through Delphi inquiry, a portfolio of R&D projects to achieve these objectives is identified. The results of the Delphi inquiry are used to build a goal programming model. This model than provides an allocation pattern for projects to achieve organizational objectives. The application of the model is discussed and illustrated.

32 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202335
202271
2021151
2020138
2019160
2018145