Decision aid system founded on nonlinear valuation, dispersion-based weighting and correlative aggregation for wire rope selection in slope stability cable nets

This paper presents a decision aid system to address hierarchically structured decision-making problems based on the determination of the satisfaction provided by a group of alternatives in relation to multiple conflicting subcriteria grouped into criteria. The system combines the action of three new methods related to the following concepts: nonlinear valuation, dispersion-based weighting and correlative aggregation. The first includes five value functions that allow the conversion of the ratings of the alternatives regarding the subcriteria into the satisfaction they produce in a versatile and simple manner through the Beta Cumulative Distribution Function. The use of measures of dispersion to weight the subcriteria by giving more importance to those factors that can make a difference due to their heterogeneity is revised to validate it when the values are not normally distributed. Dependencies between subcriteria are taken into account through the determination of their correlation coefficients, whose incorporation adjusts the results provided by the system to favour those alternatives having a balanced behaviour with respect to conflicting aspects. The overall satisfaction provided by each alternative is determined using a prioritisation operator to avoid compensation between criteria when aggregating the subcriteria. The system was tested through a novel field of application such as the selection of wire rope to form slope stability cable nets.The authors wish to express their gratitude to the IP department of INCHALAM S.A., whose collaboration and support made this paper possible

Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts

[EN] RGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimization of workstation layouts. RGB-D sensors are used to capture workers' movements when they reach objects on workbenches. Collected data are then used to optimize workstation layout by means of genetic algorithms considering multiple ergonomic criteria. Results show that typical drawbacks of using RGB-D sensors for body tracking are not a problem for this application, and that the combination with intelligent algorithms can automatize the layout design process. The procedure described can be used to automatically suggest new layouts when workers or processes of production change, to adapt layouts to specific workers based on their ways to do the tasks, or to obtain layouts simultaneously optimized for several production processes.This work was supported by the Programa estatal de investigacion, desarrollo e innovacion orientada a los retos de la sociedad of the Government of Spain under Grant TIN2013-42504-R.Diego-Mas, JA.; Poveda Bautista, R.; Garzon-Leal, D. (2017). Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts. Applied Ergonomics. 65:530-540. doi:10.1016/j.apergo.2017.01.012S5305406

User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms

[EN] Establishing the best layout configuration for software-generated interfaces and control panels is a complex problem when they include many controls and indicators. Several methods have been developed for arranging the interface elements; however, the results are usually conceptual designs that must be manually adjusted to obtain layouts valid for real situations. Based on these considerations, in this work we propose a new automatized procedure to obtain optimal layouts for software-based interfaces. Eye-tracking and mouse-tracking data collected during the use of the interface is used to obtain the best configuration for its elements. The solutions are generated using a slicing-trees based genetic algorithm. This algorithm is able to obtain really applicable configurations that respect the geometrical restrictions of elements in the interface. Results show that this procedure increases effectiveness, efficiency and satisfaction of the users when they interact with the obtained interfaces.This work was supported by the Programa estatal de investigacion, desarrollo e innovacion orientada a los retos de la sociedad of the Government of Spain under Grant DPI 2016-79042-R.Diego-Mas, JA.; Garzon Leal, D.; Poveda Bautista, R.; Alcaide Marzal, J. (2019). User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. Applied Ergonomics. 78:197-209. https://doi.org/10.1016/j.apergo.2019.03.004S1972097

Vehicle Routing Problem with Uncertain Demands: An Advanced Particle Swarm Algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers¿ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers¿ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.Moghaddam, BF.; Ruiz García, R.; Sadjadic, SJ. (2012). Vehicle Routing Problem with Uncertain Demands: An Advanced Particle Swarm Algorithm. Computers and Industrial Engineering. 62(1):306-317. doi:10.1016/j.cie.2011.10.001S30631762

Strategies to improve energy and carbon efficiency of luxury hotels in Iran

Luxury hotels generate substantial carbon footprint and scholarly research is urgently required to better understand how it could be effectively mitigated. This study adopts a method of life cycle energy analysis (LCEA) to assess the energy and carbon performance of six luxury, five star, hotels located in Iran. The results of the energy and carbon assessment of luxury hotels in Iran are compared against the energy and carbon values reported in past hotel research. This current study finds that luxury hotels in Iran are up to 3–4 times more energy- and 7 times more carbon-intense than similar hotels examined in past research. Low cost of fossil fuels, international trade sanctions and the lack of governmental and corporate energy conservation targets discourage Iranian hoteliers from carbon footprint mitigation. To counteract poor energy and carbon efficiency of luxury hotels in Iran, it is important to relax economic sanctions, develop alternative energy sources, refine corporate energy conservation targets, regularly benchmark hotel energy performance and enable exchange of good practices amongst Iranian hoteliers