The multisource Weber problem is to locate simultaneously m facilities in the Euclidean plane in order to minimize the total transportation cost for satisfying the demand of n xed users, each supplied from its closest facility. Many heuristics have been proposed for this problem, as well as a few exact algorithms. Heuristics are needed to solve quickly large problems and to provide good initial...

Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics, which approximate the stochastic dynamic programming algorithm. We s...

Exact algorithms allow one to find optimal solutions to NP-hard combinatorial optimization (CO) problems. Many research papers report on solving large instances of some NPhard problems (see, e.g., [25, 27]). The running time of exact algorithms is often very high for large instances (many hours or even days), and very large instances remain beyond the capabilities of exact algorithms. Even for ...

In order to lowering level of emissions of internal combustion engines (ICEs), they should be optimally controlled. However, ICEs operate under numerous operating conditions, which in turn makes it difficult to design controller for such nonlinear systems. In this article, a generalized unique controller for idle speed control under whole loading conditions is designed. In the current study, in...

The co phylogeny reconstruction problem is that of finding minimum cost explanations of differences between historical associations. The problem arises in parasitology, molecular systematics, and biogeography. Existing software tools for this problem either have worst-case exponential time or use heuristics that do not guarantee optimal solutions. To date, no polynomial time optimal algorithms ...

A lot of heuristic approaches have been explored in the last two decades in order to tackle large size optimization problems. These areas include parallel meta-heuristics, hybrid meta-heuristic, and cooperative search algorithms. Different taxonomies have been proposed in the literature for parallel and hybrid meta-heuristics. In these taxonomies, one can realize that cooperative search algorit...

Modularity is a popular measure for the quality of a cluster partition. Primarily, its popularity originates from its suitability for community identification through maximization. A lot of algorithms to maximize modularity have been proposed in recent years. Especially agglomerative hierarchical algorithms showed to be fast and find clusterings with high modularity. In this paper we present se...

In this paper we propose several heuristic algorithms that, using MPLS capabilities, provide network operators with a control framework for the routing plane. In particular, a monitoring block collects real-time measurements, such as the link load, and feeds them to a centralized decision block which runs a routing optimization algorithm. The output of the optimization process is then conveyed ...

HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyperheuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iter...

The min-con ict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become ine cient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improvemen...