Hybrid Meta-heuristics with VNS and Exact Methods: Application to Large Unconditional and Conditional Vertex p-Centre Problems

Large-scale unconditional and conditional vertex p-centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with p varying from 5 to 100. To the best of our knowledge, these are the largest instances solved for unconditional and conditional vertex p-centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems

Solving Large p-median Problems by a Multistage Hybrid Approach Using Demand Points Aggregation and Variable Neighbourhood Search

A hybridisation of a clustering-based technique and of a variable neighbourhood search (VNS) is designed to solve large-scale p-median problems. The approach is based on a multi-stage methodology where learning from previous stages is taken into account when tackling the next stage. Each stage is made up of several subproblems that are solved by a fast procedure to produce good feasible solutions. Within each stage, the solutions returned are put together to make up a new promising subset of potential facilities. This augmented p-median problem is then solved by VNS. As these problems used aggregation, a cost evaluation based on the original demand points instead of aggregation is computed for each of the ‘aggregation’-based solution. The one yielding the least cost is then selected and its chosen facilities included into the next stages. This multi-stage process is repeated several times until a certain criterion is met. This approach is enhanced by an efficient way to aggregate the data and a neighbourhood reduction scheme when allocating demand points to their nearest facilities. The proposed approach is tested, using various values of p, on the largest data sets from the literature with up to 89,600 demand points with encouraging results

Optimizing the location of helicopter emergency medical service operating sites

The European Commission Regulation (EU) No 965/2012, now completely operative in all the European countries, allows helicopter night landings for emergency medical service in dedicated spaces, provided with a minimum amount of facilities, called HEMS Operating Sites. This possibility opens new scenarios for the mixed, ambulance/ helicopter, rescue procedure, today not fully exploited. The paper studies the problem of optimal positioning for HEMS sites, where the transfer of the patient from ambulance to helicopter takes place, through the use of Geographic Information System (GIS) and optimization algorithms integrated in the software ArcGIS for Desktop. The optimum is defined in terms of the minimum intervention time. The solution approach has been applied to the area of competence of “SOREU dei Laghi”, in Lombardia region, with a catchment area of almost two million people

Integrating the maximum capture problem into a GIS framework

GIS, Location–allocation modeling, Maximum capture problem, C6,