Oliviu Matei

An improved hybrid algorithm for solving the generalized vehicle routing problem

The generalized vehicle routing problem (GVRP) is a natural extension of the classical vehicle routing problem (VRP). In GVRP, we are given a partition of the customers into groups (clusters) and a depot and we want to design a minimum length collection of routes for the fleet of vehicles, originating and terminating at the depot and visiting exactly one customer from each group, subject to capacity restrictions. The aim of this paper is to present an efficient hybrid heuristic algorithm obtained by combining a genetic algorithm (GA) with a local-global approach to the GVRP and a powerful local search procedure. The computational experiments on several benchmark instances show that our hybrid algorithm is competitive to all of the known heuristics published to date.

A genetic algorithm for solving the generalized vehicle routing problem

The generalized vehicle routing problem is a variant of the well-known vehicle routing problem in which the nodes of a graph are partitioned into a given number of node sets (clusters) and the objective is to find the minimum-cost delivery or collection of routes, subject to capacity restrictions, from a given depot to the number of predefined clusters passing through one node from each clusters We present an effective metaheuristic algorithm for the problem based on genetic algorithms The proposed metaheuristic is competitive with other heuristics published to date in both solution quality and computation time Computational results for benchmarks problems are reported and the results point out that GA is an appropriate method to explore the search space of this complex problem and leads to good solutions in a short amount of time.

A new approach for solving the generalized traveling salesman problem

The generalized traveling problem (GTSP) is an extension of the classical traveling salesman problem. The GTSP is known to be an NP-hard problem and has many interesting applications. In this paper we present a local-global approach for the generalized traveling salesman problem. Based on this approach we describe a novel hybrid metaheuristic algorithm for solving the problem using genetic algorithms. Computational results are reported for Euclidean TSPlib instances and compared with the existing ones. The obtained results point out that our hybrid algorithm is an appropriate method to explore the search space of this complex problem and leads to good solutions in a reasonable amount of time.

An efficient genetic algorithm for solving the generalized traveling salesman problem

The generalized traveling salesman problem (GTSP) is a generalization of the classical traveling salesman problem. The GTSP is known to be an NP-hard problem and has many interesting applications. In this paper we present a local-global approach for the generalized traveling salesman problem and as well an efficient algorithm for solving the problem based on genetic algorithms. Computational results are reported for Euclidean TSPlib instances and compared with the existing ones. The obtained results point out that our GA is an appropriate method to explore the search space of this complex problem and leads to good solutions in a short amount of time.

A Platform to Support the Product Servitization

Nowadays manufacturers are forced to shift from their traditional product-manufacturing paradigm to the goods-services continuum by providing integrated combination of products and services. The adoption of service-based strategies is the natural consequence of the higher pressure that these companies are facing in the global markets especially due to the presence of competitors which operate in low wage region. By betting on services, or more specifically, on servitization manufacturing companies are moving up the value chain in order to move the competition from costs to sophistication and innovation. The proliferation of new emerging technologies and paradigms together with a wider dissemination of information technology (IT) can significantly improve the capability of manufacturing companies to infuse services in their own products. The authors present a knowledge-based and data-driven platform that can support the design and development of Product Extended by Services (PESs) solutions.

An improved immigration memetic algorithm for solving the heterogeneous fixed fleet vehicle routing problem

This paper deals with the heterogeneous fixed fleet vehicle routing problem (HFFVRP) which is a generalization of the classical vehicle routing problem (VRP) in the sense that the fixed fleet of vehicles is assumed to be heterogeneous. The objective of HFFVRP is to find the best fleet composition and the collection of routes such that the total costs are minimized. To address this combinatorial optimization problem, we design and implement a hybrid heuristic model integrating a genetic algorithm, a local search mechanism and an immigration strategy. Several strategies for generating the initial population of the genetic algorithm in relation with six local search heuristics are considered. An important feature of the proposed approach refers to the immigration strategy used to ensure diversification by which the level of evolution for the new immigrant individuals increases along with the evolution of the population. The proposed algorithm is tested on a set of HFFVRP benchmark instances and the preliminary results point out that our approach is an attractive and appropriate method to explore the solution space of this complex problem leading to good solutions within reasonable computational times.

Applying evolutionary computation for evolving ontologies

In this paper, we describe a novel application of evolutionary computation, namely for evolving ontologies. The general algorithm of evolutionary ontologies follow roughly the same guidelines as any other genetic algorithms. However, we introduced a new genetic operator, called repair, which is needed in order to make the offspring viable. Experiments for the generation of user centered automatically generated scenes demonstrate the performance of the proposed approach.

A two-level solution approach for solving the generalized minimum spanning tree problem

Abstract In this paper, we are addressing the generalized minimum spanning tree problem, denoted by GMSTP, which is a variant of the classical minimum spanning tree (MST) problem. The main characteristic of this problem is the fact that the vertices of the graph are partitioned into a given number of clusters and we are looking for a minimum-cost tree spanning a subset of vertices which includes exactly one vertex from each cluster. We describe a two-level solution approach for solving the GMSTP obtained by decomposing the problem into two logical and natural smaller subproblems: an upper-level (global) subproblem and a lower-level (local) subproblem and solving them separately. The goal of the first subproblem is to determine (global) trees spanning the clusters using a genetic algorithm with a diploid representation of the individuals, while the goal of the second subproblem is to determine the best tree (w.r.t. cost minimization), for the above mentioned global trees, spanning exactly one vertex from each cluster. The second subproblem is solved optimally using dynamic programming. Extensive computational results are reported and discussed for an often used set of benchmark instances. The obtained results show an improvement in the quality of the achieved solutions, and demonstrate the efficiency of our approach compared to the existing methods from the literature.

Reducing the bandwidth of a sparse matrix with a genetic algorithm

Abstract The matrix bandwidth minimization problem (MBMP) consists in finding a permutation of the lines and columns of a given sparse matrix in order to keep the non-zero elements in a band that is as close as possible to the main diagonal. Equivalently in terms of graph theory, MBMP is defined as the problem of finding a labelling of the vertices of a given graph G such that its bandwidth is minimized. In this paper, we propose an improved genetic algorithm (GA)-based heuristic for solving the matrix bandwidth minimization problem, motivated by its robustness and efficiency in a wide area of optimization problems. Extensively computational results are reported for an often used set of benchmark instances. The obtained results on the different instances investigated show improvement of the quality of the solutions and demonstrate the efficiency of our GA compared to the existing methods in the literature.

An improved heuristic for the bandwidth minimization based on genetic programming

In this work we develop an improved heuristic based on genetic programming (GP) for the matrix bandwidth minimization problem (MBMP). This problem consists in rearranging the rows and columns of a sparse matrix such that the non-zero elements are in a band as close as possible to the main diagonal. We evaluated our heuristic on a set of 25 benchmark instances from the literature and compared with state-of-the-art algorithms. The obtained results are very encouraging and point out that GP is an appropriate method for solving the MBMP.