4,396 research outputs found
An exact algorithm for linear optimization problem subject to max-product fuzzy relational inequalities with fuzzy constraints
Fuzzy relational inequalities with fuzzy constraints (FRI-FC) are the
generalized form of fuzzy relational inequalities (FRI) in which fuzzy
inequality replaces ordinary inequality in the constraints. Fuzzy constraints
enable us to attain optimal points (called super-optima) that are better
solutions than those resulted from the resolution of the similar problems with
ordinary inequality constraints. This paper considers the linear objective
function optimization with respect to max-product FRI-FC problems. It is proved
that there is a set of optimization problems equivalent to the primal problem.
Based on the algebraic structure of the primal problem and its equivalent
forms, some simplification operations are presented to convert the main problem
into a more simplified one. Finally, by some appropriate mathematical
manipulations, the main problem is transformed into an optimization model whose
constraints are linear. The proposed linearization method not only provides a
super-optimum (that is better solution than ordinary feasible optimal
solutions) but also finds the best super-optimum for the main problem. The
current approach is compared with our previous work and some well-known
heuristic algorithms by applying them to random test problems in different
sizes.Comment: 29 pages, 8 figures, 7 table
A constrained tropical optimization problem: complete solution and application example
The paper focuses on a multidimensional optimization problem, which is
formulated in terms of tropical mathematics and consists in minimizing a
nonlinear objective function subject to linear inequality constraints. To solve
the problem, we follow an approach based on the introduction of an additional
unknown variable to reduce the problem to solving linear inequalities, where
the variable plays the role of a parameter. A necessary and sufficient
condition for the inequalities to hold is used to evaluate the parameter,
whereas the general solution of the inequalities is taken as a solution of the
original problem. Under fairly general assumptions, a complete direct solution
to the problem is obtained in a compact vector form. The result is applied to
solve a problem in project scheduling when an optimal schedule is given by
minimizing the flow time of activities in a project under various activity
precedence constraints. As an illustration, a numerical example of optimal
scheduling is also presented.Comment: 20 pages, accepted for publication in Contemporary Mathematic
Complete solution of a constrained tropical optimization problem with application to location analysis
We present a multidimensional optimization problem that is formulated and
solved in the tropical mathematics setting. The problem consists of minimizing
a nonlinear objective function defined on vectors over an idempotent semifield
by means of a conjugate transposition operator, subject to constraints in the
form of linear vector inequalities. A complete direct solution to the problem
under fairly general assumptions is given in a compact vector form suitable for
both further analysis and practical implementation. We apply the result to
solve a multidimensional minimax single facility location problem with
Chebyshev distance and with inequality constraints imposed on the feasible
location area.Comment: 20 pages, 3 figure
Fuzzy Maximum Satisfiability
In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to
{\L}ukasiewicz logic. The MaxSAT problem for a set of formulae {\Phi} is the
problem of finding an assignment to the variables in {\Phi} that satisfies the
maximum number of formulae. Three possible solutions (encodings) are proposed
to the new problem: (1) Disjunctive Linear Relations (DLRs), (2) Mixed Integer
Linear Programming (MILP) and (3) Weighted Constraint Satisfaction Problem
(WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have
numerous applications in optimization problems that involve vagueness.Comment: 10 page
Fuzzy Linear Programming in DSS for Energy System Planning
Energy system planning requires the use of planning tools. The mathematical models of real-world energy systems are usually multiperiod linear optimization programs. In these models, the objective function describes the total discounted costs of covering the demand for final energy or energy services. The demand for various forms of energy or energy services is the driving force of the models. By using such linear programming (LP) formulations, decision makers can elaborate suitable strategies for solving their planning problems, such as the development of emission reduction strategies.
Uncertainties that affect the process of energy system planning can be divided into parameter and decision uncertainties. Data or parameter uncertainties can be addressed either by stochastic optimization or by the methodology of fuzzy linear programming (FLP). In addition, FLP allows explicit incorporation of decision uncertainties into a mathematical model.
This paper therefore aims at evaluating the methodology of FLP with respect to the support that it offers the decision-making process in energy system planning under uncertainty.
Employing the parallels between multi-objective linear programming (MOLP) and FLP, problems of FLP in decision support system applications are pointed out and solutions are offered. The proposed modifications are based on the methodology of aspiration-reservation based decision support and still enable modeling of uncertainties in a fuzzy sense. A case study is documented to show the application of the modified FLP approach
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