Topic
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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07 Oct 2002
TL;DR: A qualitative and a numerical axiomatization for goal modeling primitives are proposed and label propagation algorithms that are shown to be sound and complete with respect to their respectiveAxiomatizations are introduced.
Abstract: Over the past decade, goal models have been used in Computer Science in order to represent software requirements, business objectives and design qualities. Such models extend traditional AI planning techniques for representing goals by allowing for partially defined and possibly inconsistent goals. This paper presents a formal framework for reasoning with such goal models. In particular, the paper proposes a qualitative and a numerical axiomatization for goal modeling primitives and introduces label propagation algorithms that are shown to be sound and complete with respect to their respective axiomatizations. In addition, the paper reports on preliminary experimental results on the propagation algorithms applied to a goal model for a US car manufacturer.
264 citations
TL;DR: This paper proposes a new idea for programming the MCAL problem, which allows decision-makers to set multiple aspiration levels for their problems in which "the more/higher is better" and "the less/lower is better in the aspiration levels are addressed.
Abstract: The situation of multi-choice aspiration levels (MCAL) may exist in many decision/management problems. However, the problem cannot be solved by current goal programming (GP) techniques. In order to improve the utility of GP and solve the MCAL problem, this paper proposes a new idea for programming the MCAL problem. The proposed method allows decision-makers (DMs) to set multiple aspiration levels for their problems in which “the more/higher is better” and “the less/lower is better” in the aspiration levels are addressed. In addition, illustrative examples are given to demonstrate the correctness of the proposed model.
264 citations
Book•
05 Nov 2010
TL;DR: Practical Goal Programming is intended to allow academics and practitioners to be able to build effective goal programming models, to detail the current state of the art, and to lay the foundation for its future development and continued application to new and varied fields.
Abstract: Practical Goal Programming is intended to allow academics and practitioners to be able to build effective goal programming models, to detail the current state of the art, and to lay the foundation for its future development and continued application to new and varied fields. Suitable as both a text and reference, its nine chapters first provide a brief history, fundamental definitions, and underlying philosophies, and then detail the goal programming variants and define them algebraically. Chapter 3 details the step-by-step formulation of the basic goal programming model, and Chapter 4 explores more advanced modeling issues and highlights some recently proposed extensions. Chapter 5 then details the solution methodologies of goal programming, concentrating on computerized solution by the Excel Solver and LINGO packages for each of the three main variants, and includes a discussion of the viability of the use of specialized goal programming packages. Chapter 6 discusses the linkages between Pareto Efficiency and goal programming. Chapters 3 to 6 are supported by a set of ten exercises, and an Excel spreadsheet giving the basic solution of each example is available at an accompanying website. Chapter 7 details the current state of the art in terms of the integration of goal programming with other techniques, and the text concludes with two case studies which were chosen to demonstrate the application of goal programming in practice and to illustrate the principles developed in Chapters 1 to 7. Chapter 8 details an application in healthcare, and Chapter 9 describes applications in portfolio selection.
262 citations
01 Jan 2001
TL;DR: In this article, the AHP is used to model landowners' strategic decision making in forest management, and a case study of a Finnish company in North America using the A'WOT to forest industry investment strategies is presented.
Abstract: Contributors. Preface. Foreword. Basic Principles of Decision Making in Natural Resources and the Environment D.L. Schmoldt, et al. Fundamentals of the Analytic Hierarchy Process T.L. Saaty. On Using the AHP in Multiple Objective Linear Programming P. Korhonen, J. Wallenius. HERO: Heuristic Optimisation for Multi-Criteria Forestry Decision Analysis J. Kangas, et al. Strategic and Tactical Planning for Managing National Park Resources D.L. Schmoldt, D.L. Peterson. Combined Use of Goal Programming and the Analytic Hierarchy Process in Forest Management L. Diaz-Balteiro, C. Romero. Efficient Group Decision Making in Workshop Settings D.L. Schmoldt, D.L. Peterson. Prioritizing Criteria and Indicators for Sustainable Forest Management: A Case Study on Participatory Decision Making G.A. Mendoza, R. Prabhu. Integrating the AHP and HERO into the Process of Participatory Natural Resources Planning J. Kangas, et al. Environmental Cognition: Contributions from the Analytic Hierarchy Process Toward Construction of Cognitive Maps R. Banai. Potential Allowable Cut of Finland Using the AHP to Model Landowners' Strategic Decision Making M. Pesonen. Applying A'WOT to Forest Industry Investment Strategies: Case Study of a Finnish Company in North America M. Pesonen, et al. Prioritizing Salmon Habitat Restoration with the AHP, SMART, and Uncertain Data K.M. Reynolds. A Fuzzy Analytic Hierarchy Process for Assessing Biodiversity Conservation G.A. Mendoza, R. Prabhu. Regression Methods for Pairwise Comparison Data J.M. Alho, et al. Using GeoChoicePerspectives in Collaborative Spatial Decision Making P. Jankowski, T. Nyerges. Integrating the AHP with Geographic Information Systems for Assessing Resource Conditions in Rural Catchments in Australia R.M. Itami, et al. PastDevelopments and Future Directions for the AHP in Natural Resources D.L. Schmoldt, et al.
258 citations
TL;DR: A fuzzy goal programming (FGP) approach that integrates activity-based costing (ABC) and performance evaluation in a value-chain structure for optimal GSC supplier selection and flow allocation is proposed.
Abstract: Supply chain operation with sustainable consideration has become an increasingly important issue in recent years. However, the decision framework with integrated costing and performance evaluation for green supply chain (GSC) has not been well developed so far in the literature. For this reason, this paper is aimed to propose a fuzzy goal programming (FGP) approach that integrates activity-based costing (ABC) and performance evaluation in a value-chain structure for optimal GSC supplier selection and flow allocation. The FGP approach is particularly suitable for such a decision model which includes flexible goals, financial and non-financial measures, quantitative and qualitative methods, multi-layer structure, multiple criteria, multiple objectives, and multiple strategies. An activity-based example of structural GSC with relevant costs and performances is presented for computing the composite performance indices of the GSC suppliers. A green supply chain of a mobile phone is used as an illustrative case...
258 citations