We study the classic facility location setting, where we are given n clients and m possible locations in some arbitrary metric space, want to choose a build facility. The exact same setting also arises spatial social choice, voters goal is candidate or outcome, with distance from voter an outcome representing cost of this for (e.g., based on their ideological differences). Unlike most previous ...

This paper presents results for a 31 variable helicopter rotor design example. Results are given for several numerical methods. This is a brief description of a portion of the Boe-ing/IBM/Rice University collaboration whose purpose is to develop eeective numerical methods for managing the use of approximation concepts or response surface methodology in design optimization.

Omni-optimization assumes a capability to perform any type of optimization, e.g. singleand multi-objective, using single optimization algorithm, or the omni-optimizer. This paper addresses a novel concept for omni-optimization, by coupling vectorization with genetic algorithms (GAs). Vectorization assumes converting constraints into objectives, and their optimization alongside the original set ...

Modern engineering design often involves computation-intensive simulation processes and multiple objectives. Engineers prefer an efficient optimization method that can provide them insights into the problem, yield multiple good or optimal design solutions, and assist decision-making. This work proposed a rough-set based method that can systematically identify regions (or subspaces) from the ori...

the development of optimization and mathematical models for closed loop supply chain (clsc) design has attracted considerable interest over the past decades. however, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. the aim of this paper, therefore, is to propose a n...

nonlinear knapsack problems (nkp) are the alternative formulation for the multiple-choice knapsack problems. a powerful approach for solving nkp is dynamic programming which may obtain the global op-timal solution even in the case of discrete solution space for these problems. despite the power of this solu-tion approach, it computationally performs very slowly when the solution space of the pr...

This paper presents a multi-objective optimization model for the design of a waste management system consisting of customers, transfer stations, landfills and collection vehicles. The developed model aims to simultaneously minimize the total costs, greenhouse gas emissions and the rates of energy consumption. To tackle the multiple objectives in the problem, we utilize an interactive fuzzy prog...

| Many, if not most, optimization problems have multiple objectives. Historically , multiple objectives have been combined ad hoc to form a scalar objective function , usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modiied to deal with multiple objectives by incorporating ...

The optimization and design of two different types of photonic devices a Fibre Bragg Grating and a Microstructured Polymer Optical Fibre is presented in light of multiple conflicting objectives in both problems. The fibre grating optimization uses a fixed length real valued representation, requiring the simultaneous optimization of four objectives along with variable bounds and a single objecti...