in this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. in this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. to tackle the problem efficiently and effectively, an effici...

Locating Radio Frequency (RF) emitters can be done with a number of methods, but cheap and widely available sensors make the Power Difference of Arrival (PDOA) technique a prominent choice. Predicting the location of an unknown RF emitter can be seen as a continuous optimization problem, minimizing the error w.r.t. the sensor measurements gathered. Most instances of this problem feature multimo...

Meta-heuristics are of significant interest to decision-makers due to the capability of finding good solutions for complex problems within a reasonable amount of computational time. These methods are further known to perform according to how their algorithm-specific parameters are set. As most practitioners aim for an off-the-shelf approach when using meta-heuristics, they require an easy appli...

This special issue presents 5 articles that cover the methods and applications of meta-heuristics and other related models, which highlight innovation and recent development for challenging problems in industry and science. The issue begins with an article by Bouhmala and Granmo, “Stochastic Learning for SATEncoded Graph Coloring Problems”. The article presents a new algorithm based on the rand...

Both coarse-to-fine and A∗ parsing use simple grammars to guide search in complex ones. We compare the two approaches in a common, agenda-based framework, demonstrating the tradeoffs and relative strengths of each method. Overall, coarse-to-fine is much faster for moderate levels of search errors, but below a certain threshold A∗ is superior. In addition, we present the first experiments on hie...

Meta-heuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a metaheuristic for solving a problem in a certain domain. Hyper-heuristics introduce a novel approach for search and optimization. A hyper-heuris...

Nurse scheduling is a difficult optimization problem with multiple constraints. There is extensive research in the literature solving the problem using meta-heuristics approaches. In this paper, we will investigate an intelligent search heuristics that handles cost based scheduling problem. The heuristics demonstrated superior performances compared to the original algorithms used to solve the p...

We perform a statistical analysis of the structure of the search space of some planar, euclidian instances of the traveling salesman problem. We want to depict this structure from the point of view of iterated local search algorithms. The objective is two-fold: understanding the experimentally known good performance of metaheuristics on the TSP and other combinatorial optimization problems; des...

Bridging the gap between simulation and reality for successful micro-grid (MG) implementation requires accurate mathematical modelling of underlying energy infrastructure extensive optimisation design space defined by all possible combinations size equipment. While exact approaches to MG capacity planning are highly computationally efficient, they often fail preserve associated problem nonlinea...