17,190 research outputs found
Lexicographic Methods for Fuzzy Linear Programming
Fuzzy Linear Programming (FLP) has addressed the increasing complexity of real-world
decision-making problems that arise in uncertain and ever-changing environments since its
introduction in the 1970s. Built upon the Fuzzy Sets theory and classical Linear Programming
(LP) theory, FLP encompasses an extensive area of theoretical research and algorithmic development.
Unlike classical LP, there is not a unique model for the FLP problem, since fuzziness can
appear in the model components in different ways. Hence, despite fifty years of research,
new formulations of FLP problems and solution methods are still being proposed. Among the
existing formulations, those using fuzzy numbers (FNs) as parameters and/or decision variables
for handling inexactness and vagueness in data have experienced a remarkable development in
recent years. Here, a long-standing issue has been how to deal with FN-valued objective functions
and with constraints whose left- and right-hand sides are FNs. The main objective of this paper is
to present an updated review of advances in this particular area. Consequently, the paper briefly
examines well-known models and methods for FLP, and expands on methods for fuzzy single- and
multi-objective LP that use lexicographic criteria for ranking FNs. A lexicographic approach to the
fuzzy linear assignment (FLA) problem is discussed in detail due to the theoretical and practical
relevance. For this case, computer codes are provided that can be used to reproduce results presented
in the paper and for practical applications. The paper demonstrates that FLP that is focused on
lexicographic methods is an active area with promising research lines and practical implications.Spanish Ministry of Economy and CompetitivenessEuropean Union (EU)
TIN2017-86647-
A New Method to Solve Fuzzy Interval Flexible Linear Programming Using a Multi-Objective Approach
The first author would like to appreciate from the research grant of University of Mazandaran. The research of Jose Luis Verdegay is supported in part by the project TIN2017-86647-P (Spanish Ministry of Economy and Competitiveness) which includes FEDER funds from the European Union.Recently fuzzy interval flexible linear programs have attracted many
interests. These models are an extension of the classical linear programming
which deal with crisp parameters. However, in most of the
real-world applications, the nature of the parameters of the decisionmaking
problems is generally imprecise. Such uncertainties can lead
to increased complexities in the related optimisation efforts. Simply
ignoring these uncertainties is considered undesired as it may result
in inferior or wrong decisions. Therefore, inexact linear programming
methods are desired under uncertainty. In this paper,weconcentrate
a fuzzy flexible linear programming model with flexible constraints
and the interval objective function and then propose a new solving
approach based on solving an associated multi-objective model.
Finally, numerical example is included to illustrate the mentioned
solving process.University of MazandaranSpanish Ministry of Economy and Competitiveness TIN2017-86647-PEuropean Commissio
Fuzzy linear programming problems : models and solutions
We investigate various types of fuzzy linear programming problems based on models and solution methods. First, we review fuzzy linear programming problems with fuzzy decision variables and fuzzy linear programming problems with fuzzy parameters (fuzzy numbers in the definition of the objective function or constraints) along with the associated duality results. Then, we review the fully fuzzy linear programming problems with all variables and parameters being allowed to be fuzzy. Most methods used for solving such problems are based on ranking functions, alpha-cuts, using duality results or penalty functions. In these methods, authors deal with crisp formulations of the fuzzy problems. Recently, some heuristic algorithms have also been proposed. In these methods, some authors solve the fuzzy problem directly, while others solve the crisp problems approximately
Aggregated fuzzy answer set programming
Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Solving fully neutrosophic linear programming problem with application to stock portfolio selection
Neutrosophic set is considered as a generalized of crisp set, fuzzy set, and intuitionistic fuzzy set for representing the uncertainty, inconsistency, and incomplete knowledge about the real world problems. In this paper, a neutrosophic linear programming (NLP) problem with single-valued trapezoidal neutrosophic numbers is formulated and solved. A new method based on the so-called score function to find the neutrosophic optimal solution of fully neutrosophic linear programming (FNLP) problem is proposed. This method is more flexible than the linear programming (LP) problem, where it allows the decision maker to choose the preference he is willing to take. A stock portfolio problem is introduced as an application. Also, a numerical example is given to illustrate the utility and practically of the method
A general framework integrating techniques for scheduling under uncertainty
Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof
A weight space-based approach to fuzzy multiple-objective linear programming
In this paper, the effects of uncertainty on multiple-objective linear programming models are studied using the concepts of fuzzy set theory. The proposed interactive decision support system is based on the interactive exploration of the weight space. The comparative analysis of indifference regions on the various weight spaces (which vary according to intervals of values of the satisfaction degree of objective functions and constraints) enables to study the stability and evolution of the basis that correspond to the calculated efficient solutions with changes of some model parameters.http://www.sciencedirect.com/science/article/B6V8S-45S9DHF-2/1/f597062363c29e9bb464a6ba6f21f0d
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