Helena is visiting the Information, Operations & Management Sciences Department at NYU Stern School of Management. She has been invited by Michael Pinedo who she shares many common research interests, as for example in scheduling, supply chain management, metaheuristics, etc.
Helena Ramalhinho is one of the Plenary Speakers of the 10th International Workshop on Hybrid Metaheuristics
The title of the talk is “From ILS to Hybrid ILS … and other extensions“.
Abstract: Iterated Local Search (ILS) is a conceptually simple and efficient Metaheuristic. The main idea behind ILS is to drive the search not on the full space of all candidate solutions but on the solutions that are returned by some underlying algorithm, typically a local search heuristic. This method has been applied to many different optimization problems with more than 5,000 entries in Google Scholar. In this talk, we will review briefly the ILS method and focus on the extensions of ILS as the hybrid ILS approaches, the different hybridizations implementations and the advantages and disadvantages of these hybridizations. We will also discuss other ILS extensions as the SimILS (Simulation+ILS) to solve Stochastic Combinatorial Optimization Problems, and the MoILS, to solve Multiobjective Combinatorial Optimization Problems. In addition, we will review the most important applications of ILS and extensions in different areas, from Supply Chain Management to Health Care. Finally, future research topics will be presented.
Helena Ramalhinho was one of the members of the panel session of the 83rd EURO Working Group on Multicriteria Decision Aiding (EWG-MCDA) organized by the Research Group on Knowledge Engineering, ESADE with the topic:
Current and future trends in decision aiding systems for business and management
The panelists were:Helena Ramalhinho (UPF); Miguel Angel Ariño (IESE); Francesc Miralles (La Salle, URL); Marc Le Menestrel (UPF); Esteve Almirall (ESADE, URL).
Helena Ramalhinho was invited to participate in a Big Data Workshop at UPF, organized by the Engineering and Information and Communication Technology – Academic Unit (DTIC) and the Cátedra de Telefónica.
The main topic was the Big Data in the Public Sector. See the program.
See the video (in Spanish).
This paper analyzes a realistic variant of the permutation flow-shop problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathemati- cal model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are an- alyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.
- Ferrer A., Guimarans D., Ramalhinho H.R. and Juan A.A. (2015), A BRILS Metaheuristic For Non-Smooth Flow-Shop Problems with Failure-Risk Costs, Expert Systems with Applications (accepted for publication) doi:10.1016/j.eswa.2015.09.011
- On the UPF web in Catalan and Spanish.
Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the products. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers, and a realistic objective function with vehicles’ fixed and distance-based costs and a cost per customer visited. We design a trajectory search heuristic called GILS-VND that combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. This method obtains competitive solutions and improves the company solutions leading to significant savings in transportation costs.
Coelho V.N., Grasas A., Ramalhinho H., Coelho I.M., Souza M.J.F. (2015), An ILS-based Algorithm to Solve a Large-scale Real Heterogeneous Fleet VRP with Multi-trips and Docking Constraints, European Journal of Operational Research (accepted for publication September 2015) doi:10.1016/j.ejor.2015.09.047.
On the UPF web (in Catalan)