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...

ÐTask scheduling is essential for the proper functioning of parallel processor systems. Scheduling of tasks onto networks of parallel processors is an interesting problem that is well-defined and documented in the literature. However, most of the available techniques are based on heuristics that solve certain instances of the scheduling problem very efficiently and in reasonable amounts of time...

Combinatorial optimization problems are often used to test heuris-tics. Among heuristics, stochastic ones deserve particular consideration being generally meta-heuristics that aim at performing reasonnably well on a wide spectrum of problems. Among them, evolutionary algorithms have recently appeared. Emphasis have been put on them by researches that have shown that they are able to solve eecie...

Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about functions. Thus, utilization is critical performance of heuristics. However, concept has remained vague and abstract there no reliable metric reflect extent which function utilized by heuristic algorithms. In this paper, ratio (IUR) defined, quantity over acquired in se...

We propose two general heuristics to transform a batch Hillclimbing search into an incremental one. Then, we apply our heuristics to two Bayesian network structure learning algorithms and experimentally see that our incremental approach saves a significant amount of computing time while it yields similar networks than the batch algorithms.

The constraint satisfaction community has developed a number of heuristics for variable ordering during backtracking search. For example, in conjunction with algorithms which check forwards, the Fail-First (FF) and Brelaz (Bz) heuristics are cheap to evaluate and are generally considered to be very eeective. Recent work to understand phase transitions in NP-complete problem classes enables us t...

Selection hyper-heuristics are automated methodologies for selecting existing low-level heuristics to solve hard computational problems. They have been found very useful for evolutionary algorithms when solving both single and multi-objective real-world optimization problems. Previous work mainly focuses on empirical study, while theoretical study, particularly in multi-objective optimization, ...

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...

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem...