Yanira González

Multiobjectivisation of the Antenna Positioning Problem

Multiobjectivisation is a technique which transforms a mono-objective optimisation problem into a multi-objective one with the aim of avoiding stagnation. The transformation can be performed by the addition of artificial objectives or by the decomposition of the original objective function. Several well-known multiobjectivisation schemes, based on the addition of artificial objectives, are analysed. Also, some novel artificial objectives are suggested. The main advantages of these multiobjectivisation methods are their generality and ease of implementation. Different multiobjectivisation schemes have been applied to the mono-objective version of the Antenna Positioning Problem. Tests have been performed using NSGA-II, one of the most successful moeas. The experimental evaluation demonstrates that high quality results can be achieved by multiobjectivisation, when they are compared to the results obtained by the best mono-objective approaches.

A multi-objective evolutionary approach for the antenna positioning problem

Antenna Positioning Problem (app) is an NP-Complete Optimisation Problem which arises in the telecommunication field. It consists in identifying the infrastructures required to establish a wireless network. Several objectives must be considered when tackling app: minimise the cost, and maximise the coverage, among others. Most of the proposals simplify the problem, converting it into a mono-objective problem. In this work, multi-objective evolutionary algorithms are used to solve app. In order to validate such strategies, computational results are compared with those obtained by means of mono-objective algorithms. An extensive comparison of several evolutionary algorithms and variation operators is performed. Results show the advantages of incorporating problem-dependent information into the evolutionary strategies. Also, they show the importance of properly tuning the evolutionary approaches.

A Multi-level Filling Heuristic for the Multi-objective Container Loading Problem

This work deals with a multi-objective formulation of the Container Loading Problem which is commonly encountered in transportation and wholesaling industries. The goal of the problem is to load the items (boxes) that would provide the highest total volume and weight to the container, without exceeding the container limits. These two objectives are conflicting because the volume of a box is usually not proportional to its weight. Most of the proposals in the literature simplify the problem by converting it into a mono-objective problem. However, in this work we propose to apply multi-objective evolutionary algorithms in order to obtain a set of non-dominated solutions, from which the final users would choose the one to be definitely carried out. To apply evolutionary approaches we have defined a representation scheme for the candidate solutions, a set of evolutionary operators and a method to generate and evaluate the candidate solutions. The obtained results improve previous results in the literature and demonstrate the importance of the evaluation heuristic to be applied.

Parallel Hyperheuristics for the Antenna Positioning Problem

Antenna Positioning Problem (app) is an NP-Complete Optimisation Problem which arises in the telecommunication field. It consists in identifying the infrastructures required to establish a wireless network. Several objectives must be considered when tackling app and multi-objective evolutionary algorithms have been successfully applied to solve it. However, they required a deep analysis, and a correct parameterisation in order to obtain high quality solutions. In this work, a parallel hyperheuristic island-based model approach is presented. Several hyperheuristic scoring strategies are tested. Results show the advantages of the parallel hyperheuristic. On one hand, the testing of each sequential configuration can be avoided. On the other hand, it speeds up the attainment of high-quality solutions even when compared with the best sequential approaches.

Parallelization of the multi-objective container loading problem

This work presents a multi-objective approach to solve the Container Loading Problem. The single-objective formulation of the problem has been widely studied in the related literature, trying to optimise the total volume of the packed pieces into the container. However, a rather common aspect in the scope of this problem is the weight limit of the containers, since they normally cannot exceed a certain weight for their transportation, and they should make the most without exceeding that limit. For this reason, we have focused on a multi-objective formulation of the problem which seeks to maximize the volume at the same time that the weight, without exceeding the weight limit. To solve this multi-objective problem we have applied multi-objective optimisation evolutionary algorithms given their great effectiveness with other types of real-world multi-objective problems. One of the goals of this work is to improve the results of the only known work in the literature that addresses the same problem with multiple objectives. Once we have achieved it, we have parallelized the problem applying different island-based models to enhance the effectiveness and efficiency of our approach.

An Instance Generator for the Multi-Objective 3D Packing Problem

Cutting and packing problems have important applications to the transportation of cargo. Many algorithms have been proposed for solving the 2D/3D cutting stock problems but most of them consider single objective optimization. The goal of the problem here proposed is to load the boxes that would provide the highest total volume and weight to the container, without exceeding the container limits. These two objectives are conflicting because the volume of a box is usually not proportional to its weight. This work deals with a multi-objective formulation of the 3D Packing Problem (3DPP). We propose to apply multi-objective evolutionary algorithms in order to obtain a set of non-dominated solutions, from which the final users would choose the one to be definitely carried out. For doing an extensive study, it would be necessary to use more problem instances. Instances to deal with the multi-objective 3DPP are non-existent. For this purpose, we have implemented an instance generator.

Multi-objective Multi-level Filling Evolutionary Algorithm for the 3D Cutting Stock Problem

3D cutting and packing problems have important applications and are of particular relevance to the transportation of cargo in the form of Container Loading Problems (CLP). Many algorithms have been proposed for solving the 2D/3D cutting stock problems but most of them consider single objective optimization. The goal of the problem is to load the boxes that would provide the highest total volume and weight to the container, without exceeding the container limits. These two objectives are conflicting because the volume of a box is usually not proportional to its weight. This work deals with a multi-objective formulation of the CLP. We propose to apply multi-objective evolutionary algorithms in order to obtain a set of non-dominated solutions, from which the final users would choose the one to be definitely carried out. To apply evolutionary approaches we have defined a representation scheme for the candidate solutions, a set of evolutionary operators and a method to generate and evaluate the candidate solutions. The obtained results for generated instances on standard containers demonstrate the importance of the evaluation heuristic to be applied.

Single and multi-objective genetic algorithms for the container loading problem

Container Loading Problems (CLPs) deal with determination of the optimal pattern for packing boxes into a given container usually with respect to the maximal utilization of the total container volume. On the other hand, it is also important to maximize the utilization of the maximal container weight for which is paid when buying a shipment service. In this paper we analyze two genetic algorithms specially adopted to solve CLP. One of them is based on the Genetic Algorithm (GA) and is suitable to solve single-objective CLPs, while another one is based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), suitable for solution of CLP by simultaneously considering both of the above mentioned objectives. The algorithms have been experimentally investigated by solving various CLP instances of different complexity. The obtained results showed that simultaneous consideration of both objectives using the proposed multi-objective optimization algorithm gives better results in utilization of container volume when solving complex CLP instances.

How Best to Manage the Urethra at the Time of Prolapse Correction

Pelvic organ prolapse usually is associated with urinary symptoms, especially urinary incontinence. In larger prolapses, the urethra tends to kink and produce mechanical urinary flow obstruction, leading to occult urinary incontinence. A high rate of women with occult urinary incontinence will present with symptoms after prolapse correction if an anti-incontinence procedure is not performed also. It is not yet clear whether both procedures should be performed together, because that approach increases the rate of complications. In our opinion, there is enough evidence to support combined surgery, at least in incontinent women and patients with occult incontinence; however, it is important to inform and advise women about the possible benefits and risks of each option for a joint decision to be made.

Graphical User Interface for the Container Loading Problem: [An aproach using JavaScript]

This paper presents a Graphical User Interface for a service used to solve the Container Loading Problem as applied to the logistic industry, commonly encountered in transportation and wholesaling industries. This interface allows users to see how items (boxes) are to be placed in the container. In order to decide where exactly to locate each item, we used two evaluation heuristics based on a fill-by-levels strategy in which the items are storaged into the container. The user must choose the input file with the definition of the problem and the solution with the coordinates for each item inside the container. This information is then used by the GUI to arrange the boxes. However, the solutions given by evaluation heuristics can be modified using the mouse. The users can remove or exchange the items inside the container. Between of the features presented by this interface is the ability to keep track of all solutions that were rejected by the heuristics in its search for the final solution. The GUI was implemented using JavaScript and Three.js, a 3D library. While the heuristics were coded using C/C++.