Informatica

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising.

Ligand Based Virtual Screening methods are used to screen molecule databases to select the most promising compounds for a query. This is performed by decision-makers based on the information of the descriptors, which are usually processed individually. This methodology leads to a lack of information and hard post-processing dependent on the expert’s knowledge that can end up in the discarding of promising compounds. Consequently, in this work, we propose a new multi-objective methodology called MultiPharm-DT where several descriptors are considered simultaneously and whose results are offered to the decision-maker without effort on their part and without relying on their expertise.

This paper deals with the two-stage transportation problem with fixed charges, denoted by TSTPFC. We propose a fast solving method, designed for parallel environments, that allows solving real-world applications efficiently. The proposed constructive heuristic algorithm is iterative and its primary feature is that the solution search domain is reduced at each iteration. Our achieved computational results were compared with those of the existing solution approaches. We tested the method on two sets of instances available in literature. The outputs prove that we have identified a very competitive approach as compared to the methods than one can find in literature.

We consider a geographical region with spatially separated customers, whose demand is currently served by some pre-existing facilities owned by different firms. An entering firm wants to compete for this market locating some new facilities. Trying to guarantee a future satisfactory captured demand for each new facility, the firm imposes a constraint over its possible locations (a finite set of candidates): a new facility will be opened only if a minimal market share is captured in the short-term. To check that, it is necessary to know the exact captured demand by each new facility. It is supposed that customers follow the partially binary choice rule to satisfy its demand. If there are several new facilities with maximal attraction for a customer, we consider that the proportion of demand captured by the entering firm will be equally distributed among such facilities (equity-based rule). This ties breaking rule involves that we will deal with a nonlinear constrained discrete competitive facility location problem. Moreover, minimal attraction conditions for customers and distances approximated by intervals have been incorporated to deal with a more realistic model. To solve this nonlinear model, we first linearize the model, which allows to solve small size problems because of its complexity, and then, for bigger size problems, a heuristic algorithm is proposed, which could also be used to solve other constrained problems.

We consider the Multilevel Uncapacitated Facility Location Problem (MLUFLP) and propose a new efficient integer programming formulation of the problem that provides optimal solutions for the MLUFLP test instances unsolved to optimality up to now. Further, we design a parallel Memetic Algorithm (MA) with a new strategy for applying the local search improvement within the MA frame. The conducted computational experiments show that the proposed MA quickly reaches all known optimal and best known solutions from the literature and additionally improves several solutions for large-scale MLUFLP test problems.