The design of course timetables for academic institutions is a very difficult job due to the huge number of possible feasible timetables with respect to the problem size. This process contains lots of constraints that must be taken into account and a large search space to be explored, even if the size of the problem input is not significantly large. Different heuristic approaches have been prop...

This paper proposes adaptive large neighborhood search (ALNS) heuristics for two service deployment problems in a cloud computing context. The problems under study consider the deployment problem of a provider of software-as-a-service applications, and include decisions related to the replication and placement of the provided services. A novel feature of the proposed algorithms is a local searc...

The vehicle routing problem with multiple trips consists in determining the routing of a fleet of vehicles where each vehicle can perform multiple routes during its workday. This problem is relevant in applications where the duration of each route is limited, for example when perishable goods are transported. In this work, we assume that a fixed-size fleet of vehicles is available and that it m...

We are presenting in this volume selected, peer-reviewed, short papers that were presented at the 3rd International Conference on Variable Neighborhood Search (VNS’14) which took place in Djerba, Tunisia, during October 8-11, 2014.

Available online 31 July 2008

In this paper we present a comparative study of four trajectory or single-solution based metaheuristics (S-metaheuristics): Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP), Variable Neighborhood Search (VNS), and Simulated Annealing (SA). These metaheuristics were considered to assess their respective performance to minimize the Maximum Tardiness (Tmax) for the ...

We analyze the possibility of parallelizing the Traveling Salesman Problem over the MapReduce architecture. We present the serial and parallel versions of two algorithms Tabu Search and Large Neighborhood Search. We compare the best tour length achieved by the Serial version versus the best achieved by the MapReduce version. We show that Tabu Search and Large Neighborhood Search are not well su...

In this paper we propose a general variable neighborhood search approach for the balanced location problem. Next to large shaking neighborhoods, the embedded variable neighborhood descent utilizes three neighborhood structures that focus on different solution aspects. By a computational study, we show that this VNS outperforms existing methods with respect to average solution quality and stabil...