The dominant approach for learning Bayesian networks from data is based on the use of a scoring metric, that evaluates the fitness of any given candidate network to the data, and a search procedure, that explores the space of possible solutions. The most efficient methods used in this context are (Iterated) Local Search algorithms. These methods use a predefined neighborhood structure that defi...

In this work, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-le...

We consider the NP-hard no-wait flowshop scheduling problem with sequence-dependent setup times to minimize makespan. We propose a hybrid heuristic which combines the iterated greedy process with local search method based on a block swap operator. Experimental results show that the proposed heuristic is comparatively more effective than the existing metaheuristics.

When learning classiiers, more extensive search for rules is shown to lead to lower pre-dictive accuracy on many of the real-world domains investigated. This counter-intuitive result is particularly relevant to recent systematic search methods that use risk-free pruning to achieve the same outcome as exhaustive search. We propose an iterated search method that commences with greedy search, exte...

A practical nurse rostering problem, which arises at a ward of an Italian hospital, is considered. The nurse rostering problem is a typical employee timetabling problem, where each month it is required to generate the nursing staff shifts subject to various contractual and operational requirements. These requirements may be in conflict especially for those months in which manpower is reduced du...

Integrated scheduling of distributed manufacturing operations has implications for supply chain optimization and requires further investigations to facilitate its application area various industry settings. This study extends the limited literature two-stage production-assembly problems offering a twofold contribution. First, an original mathematical extension, Distributed Two-Stage Assembly Fl...

We present a bi-objective parallel machine scheduling problem with and job sequence dependent setup times, the additional consideration of resources needed during setups. The availability such is limited. This models many practical situations where times imply, for example, cleaning and/or reconfiguration productive equipment. These setups are performed by personnel, who course limited in numbe...

There are many local and greedy algorithms for energy minimization over Markov Random Field (MRF) such as iterated condition mode (ICM) and various gradient descent methods. Local minima solutions can be obtained with simple implementations and usually require smaller computational time than global algorithms. Also, methods such as ICM can be readily implemented in a various difficult problems ...

In this paper, as an application of fuzzy matroids, the fuzzifying greedy algorithm is proposed and an achievableexample is given. Basis axioms and circuit axioms of fuzzifying matroids, which are the semantic extension for thebasis axioms and circuit axioms of crisp matroids respectively, are presented. It is proved that a fuzzifying matroidis equivalent to a mapping which satisfies the basis ...