generally, an engineering design problem has multiple objective functions. some of these problems can be formulated as multiobjective geometric programming models. on the other hand,often in the real world, coefficients of the objective functions are not known precisely. coefficients may be interpreted as fuzzy numbers, which lead to a multiobjective geometric programming with fuzzy parameters....

We consider resource allocation with separable objective functions defined over subranges of the integers. While it is well known that (the maximization version of) this problem can be solved efficiently if the objective functions are concave, the general problem of resource allocation with non-concave functions is difficult. In this article we show that for fairly well-shaped non-concave objec...

Chung, S.-J., H.W. Hamacher, F. Maffioli and K.G. Murty, Note on combinatorial optimization with max-linear objective functions, Discrete Applied Mathematics 42 (1993) 139-145. We consider combinatorial optimization problems with a feasible solution set SC(O,I )” specified by a system of linear constraints in O-l variables. Additionally, several cost functions c,,., ,,c~ are given. The max-line...

i This paper describes a solution technique mr Linear Programming problems with multiple objective functions. In this type of problem it is often necessary to replace the concept of "optimgm" with that of "best compromise". In contrast with methods dealing with a priori weighted sums of the objective functions, the method described here involves a sequential exploration of solutions. This explo...

Solving constrained optimization problems by multi-objective evolutionary algorithms has scored tremendous achievements in the last decade. Standard multi-objective schemes usually aim at minimizing the objective function and also the degree of constraint violation simultaneously. This paper proposes a new multi-objective method for solving constrained optimization problems. The new method keep...

Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to determine the model parameters that best match a given image by searching for the global optimum of the involved objective function. Unfortunately, this function is usually designed manually, based on implicit and domain-dependent ...

The paper deals with the open-shop problems with unit-time operations and nonde-creasing symmetric objective functions depending on job completion times. We construct two schedules, one of them is optimal for any symmetric convex function, the other is optimal for any symmetric concave function. Each of these two schedules is given by an analytically deened function which assigns to each operat...

CP-Nets are a framework for dealing with qualitative preferences, both conditional and unconditional. They have received a lot of attention recently, and many extensions have been provided. In particular, the framework of constrained CP-Nets aims to choose, amongst the solutions that satisfy a set of constraints, the preferred one. While the semantics of CP-Nets allows for cycles (and, indeed, ...