We present a new piecewise linear approximation of non-linear optimization problems. It can be seen as a variant of classical triangulations that leaves more degrees of freedom to define any point as a convex combination of the samples. For example, in the traditional Union Jack approach a...

The Standard Quadratic Problem (StQP) is an NP-hard problem with many local minimizers (stationary points). In the literature, heuristics based on unconstrained continuous non-convex formulations have been proposed (Bomze & Palagi, 2005; Bomze, Grippo, & Palagi, 2012) but none dominates the other in...

In humans, the relationship between experience and productivity, also known as learning (possibly also including forgetting), is non-linear. As a result, prescriptive planning models that seek to manage workforce development through task assignment are difficult to solve. To overcome this...

An effective sourcing strategy leads to cost savings and value added collaborations. For radical innovative product sourcing (RIPS), the exact nature and demand of products are highly uncertain. As such, knowledge sharing competences and production capacities of potential suppliers are...

Minimizing two different upper bounds of the matrix which generates search directions of the nonlinear conjugate gradient method proposed by Dai and Liao, two modified conjugate gradient methods are proposed. Under proper conditions, it is briefly shown that the methods are globally convergent...

Variational inequality theory facilitates the formulation of equilibrium problems in economic networks. Examples of successful applications include models of supply chains, financial networks, transportation networks, and electricity networks. Previous economic network equilibrium models that...

This paper considers line search optimization methods using a mathematical framework based on the simple concept of a v-pattern and its properties. This framework provides theoretical guarantees on preserving, in the localizing interval, a local optimum no worse than the starting point. Notably,...

A constrained optimization problem is formulated and solved in order to determine the smallest confidence region for the parameters of the Pareto distribution in a proposed family of sets. The objective function is the area of the region, whereas the constraints are related to the required...

Operations research techniques have been used in the space industry since its infancy, and various competing methods and codes, with widely varying characteristics, have been used over time. This survey is intended to give an overview of current application cases of different operations research...

We consider the k-Hyperplane Clustering problem where, given a set of m points in Rn, we have to partition the set into k subsets (clusters) and determine a hyperplane for each of them, so as to minimize the sum of the squares of the Euclidean distances between the points and the hyperplane of...