Carlos Llopis-Albert

Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty

In decision-making processes, reliability and risk aversion play a decisive role. This paper presents a framework for stochastic optimization of control strategies for groundwater nitrate pollution from agriculture under hydraulic conductivity uncertainty. The main goal is to analyze the influence of uncertainty in the physical parameters of a heterogeneous groundwater diffuse pollution problem on the results of management strategies, and to introduce methods that integrate uncertainty and reliability in order to obtain strategies of spatial allocation of fertilizer use in agriculture. A hydro-economic modeling approach is used for obtaining the allocation of fertilizer reduction that complies with the maximum permissible concentration in groundwater while minimizes agricultural income losses. The model is based upon nonlinear programming and groundwater flow and mass transport numerical simulation, condensed on a pollutant concentration response matrix. The effects of the hydraulic conductivity uncertainty on the allocation of nitrogen reduction among agriculture pollution sources are analyzed using four formulations: Monte Carlo simulation with pre-assumed parameter field, Monte Carlo optimization, stacking management, and mixed-integer stochastic model with predefined reliability. The formulations were tested in an illustrative example for 100 hydraulic conductivity realizations with different variance. The results show a high probability of not meeting the groundwater quality standards when deriving a policy from just a deterministic analysis. To increase the reliability several realizations can be optimized at the same time. By using a mixed-integer stochastic formulation, the desired reliability level of the strategy can be fixed in advance. The approach allows deriving the trade-offs between the reliability of meeting the standard and the net benefits from agricultural production. In a risk-averse decision making, not only the reliability of meeting the standards counts, but also the probability distribution of the maximum pollutant concentrations. A sensitivity analysis was carried out to assess the influence of the variance of the hydraulic conductivity fields on the strategies. The results show that the larger the variance, the greater the range of maximum nitrate concentrations and the worst case (or maximum value) that could be reached for the same level of reliability.

Stochastic Simulation of Non-Gaussian 3D Conductivity Fields in a Fractured Medium with Multiple Statistical Populations: Case Study

This paper applies a stochastic inverse method, named as gradual conditioning (GC) method, to the fractured site of Aspo, Sweden, which is an underground hard rock laboratory initially designed as a potential future deep geological repository for spent nuclear fuel. The aim of this paper is (1) the verification of GC method in a real three-dimensional (3D) fractured rock medium, showing that the GC method is a competitive stochastic tool in highly heterogeneous aquifers, furthermore, it gathers a set of capabilities so far not included in any existing method; (2) to characterize the site as adequately as possible, experimental data are reproduced closely; (3) to provide measures on the uncertainty of the estimates by means of using multiple equally likely realizations, thus showing the importance of conditioning to as much information as possible in order to reduce the uncertainty; (4) to prove that this fractured media can be adequately modeled by assuming a (pseudo-) continuum media, or equivalent porou...

Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory

An efficient algorithm is presented to obtain trajectories for industrial robots working in industrial environments. The procedure starts with the obtaining of an optimal time trajectory neglecting the presence of obstacles. When obstacles are considered, the initial trajectory (obtained by neglecting obstacles) will not be feasible and will have to evolve so that it can become a solution. In this paper, the way that it evolves until a new feasible collision-free trajectory is obtained considering the possible obstacles is described. This is a direct algorithm that works in a discrete space of trajectories, approaching the global solution as the discretization is refined. The solutions obtained are efficient trajectories near to the minimum time one and they meet the physical limitations of the robot (the maximum values of torque, power and jerk are considered for each actuator), avoid collisions, and take into account the constraint of energy consumed. Examples already published and new examples in real industrial environments have been solved to verify the working of the algorithm. An efficient algorithm to obtain trajectories for industrial robots working in industrial environment is presented.Kinematics and dynamics of the industrial robot are considered and collision-free trajectories are obtained.The methodology has been successfully applied to several cases whose results are shown in the paper.A robot similar to PUMA 560 has been modelled.Optimal trajectory time of the robot and computational times have been presented.

Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators

There is a growing interest in environmental policies about how to implement public participation engagement in the context of water resources management. This paper presents a robust methodology, based on ordered weighted averaging (OWA) operators, to conflict resolution decision-making problems under uncertain environments due to both information and stakeholders’ preferences. The methodology allows integrating heterogeneous interests of the general public and stakeholders on account of their different degree of acceptance or preference and level of influence or power regarding the measures and policies to be adopted, and also of their level of involvement (i.e., information supply, consultation and active involvement). These considerations lead to different environmental and socio-economic outcomes, and levels of stakeholders’ satisfaction. The methodology establishes a prioritization relationship over the stakeholders. The individual stakeholders’ preferences are aggregated through their associated weights, which depend on the satisfaction of the higher priority decision maker. The methodology ranks the optimal management strategies to maximize the stakeholders’ satisfaction. It has been successfully applied to a real case study, providing greater fairness, transparency, social equity and consensus among actors. Furthermore, it provides support to environmental policies, such as the EU Water Framework Directive (WFD), improving integrated water management while covering a wide range of objectives, management alternatives and stakeholders.

Decision making under uncertainty in environmental projects using mathematical simulation modeling

In decision-making processes, reliability and risk aversion play a decisive role. The aim of this study is to perform an uncertainty assessment of the effects of future scenarios of sustainable groundwater pumping strategies on the quantitative and chemical status of an aquifer. The good status of the aquifer is defined according to the terms established by the EU Water Framework Directive (WFD). A decision support systems (DSS) is presented, which makes use of a stochastic inverse model (GC method) and geostatistical approaches to calibrate equally likely realizations of hydraulic conductivity (K) fields for a particular case study. These K fields are conditional to available field data, including hard and soft information. Then, different future scenarios of groundwater pumping strategies are generated, based on historical information and WFD standards, and simulated for each one of the equally likely K fields. The future scenarios lead to different environmental impacts and levels of socioeconomic development of the region and, hence, to a different degree of acceptance among stakeholders. We have identified the different stakeholders implied in the decision-making process, the objectives pursued and the alternative actions that should be considered by stakeholders in a public participation project (PPP). The MonteCarlo simulation provides a highly effective way for uncertainty assessment and allows presenting the results in a simple and understandable way even for non-experts stakeholders. The methodology has been successfully applied to a real case study and lays the foundations to perform a PPP and stakeholders’ involvement in a decision-making process as required by the WFD. The results of the methodology can help the decision-making process to come up with the best policies and regulations for a groundwater system under uncertainty in groundwater parameters and management strategies and involving stakeholders with conflicting interests.

Assembly Line Productivity Assessment by Comparing Optimization-Simulation Algorithms of Trajectory Planning for Industrial Robots

In this paper an analysis of productivity will be carried out from the resolution of the problem of trajectory planning of industrial robots. The analysis entails economic considerations, thus overcoming some limitations of the existing literature. Two methodologies based on optimization-simulation procedures are compared to calculate the time needed to perform an industrial robot task. The simulation methodology relies on the use of robotics and automation software called GRASP. The optimization methodology developed in this work is based on the kinematics and the dynamics of industrial robots. It allows us to pose a multiobjective optimization problem to assess the trade-offs between the economic variables by means of the Pareto fronts. The comparison is carried out for different examples and from a multidisciplinary point of view, thus, to determine the impact of using each method. Results have shown the opportunity costs of non using the methodology with optimized time trajectories. Furthermore, it allows companies to stay competitive because of the quick adaptation to rapidly changing markets.

Structure Adaptation in Stochastic Inverse Methods for Integrating Information

The use of inverse modeling techniques has greatly increased during the past several years because the advances in numerical modeling and increased computing power. Most of these methods require an a priori definition of the stochastic structure of conductivity (K) fields that is inferred only from K measurements. Therefore, the additional conditioning data, that implicitly integrate information not captured by K data, might lead to changes in the a priori model. Different inverse methods allow different degrees of structure adaptation to the whole set of data during the conditioning procedure. This paper illustrates the application of a powerful stochastic inverse method, the Gradual Conditioning (GC) method, to two different sets of data, both non-multiGaussian. One is based on a 2D synthetic aquifer and another on a real-complex case study, the Macrodispersion Experiment (MADE-2), site on Columbus Air Force Base in Mississippi (USA). We have analyzed how additional data change the a priori model on account of the perturbations performed when constraining stochastic simulations to data. Results show how the GC method tends to honour the a priori model in the synthetic case, showing fluctuations around it for the different simulated fields. However, in the 3D real case study, it is shown how the a priori structure is slightly modified not obeying just to fluctuations but possibly to the effect of the additional information on K, implicit in piezometric and concentration data. We conclude that implementing inversion methods able to yield a posteriori structure that incorporate more data might be of great importance in real cases in order to reduce uncertainty and to deal with risk assessment projects.

Change of the A Priori Stochastic Structure in the Conditional Simulation of Transmissivity Fields

The development of methods for the stochastic simulation of transmissivity (T) fields has progressed, allowing simulations that are conditional not only to T measurements but to piezometric head and solute concentration data. Some methods are even able to honour secondary data and travel time information. However, most of these methods require an a priori definition of the stochastic structure of T fields that is inferred only from T measurements. Thus, the additional conditioning data, that implicitly integrate information not captured by T data, might lead to changes in the a priori model. Different simulation methods will allow different degrees of structure adaptation to the whole set of data. This paper illustrates the application of a new stochastic simulation method, the Gradual Conditioning (GC) method, to two different sets of data, both non-multiGaussian, one based on a 2D synthetic aquifer and another on a 3D real case (MADE site). We have studied how additional data change the a priori model. Results show how the GC method honours the a priori model in the synthetic case, showing fluctuations around it for the different simulated fields. However, in the 3D real case study, it is shown how the a priori structure is slightly modified not following just fluctuations but possibly the effect of the additional information on T, implicit in piezometric and concentration data. Thus, we consider that implementing inversion methods able to yield a posteriori structures that incorporate more data might be of great importance in real cases.

A novel induced aggregation method for intuitionistic fuzzy set and its application in multiple attribute group decision making

In this paper, by unifying the induced ordered weighted averaging (IOWA) and the weighted average, a novel induced aggregation method for intuitionistic fuzzy set is investigated. More specifically, a new intuitionistic fuzzy (IF) induced aggregation operator called weighted intuitionistic fuzzy IOWA weighted average (WIFIOWAWA) operator is introduced. A significant advantage of the WIFIOWAWA operator is that it can eliminate the drawback of the existing operators by the dual roles of its order‐inducing variables. In addition, some of its desired properties and families are explored. Furthermore, using the proposed operator, a procedure is developed to solve multiple attribute group decision making problems in the case of IF situation. Finally, an illustrative example is provided to demonstrate the effectiveness and practicality of the developed method.

Influence of the Friction Coefficient on the Trajectory Performance for a Car-Like Robot

A collision-free trajectory planner for a car-like mobile robot moving in complex environments is introduced and the influence of the coefficient of friction on important working parameters is analyzed. The proposed planner takes into account not only the dynamic capabilities of the robot but also the behaviour of the tire. This planner is based on sequential quadratic programming algorithms and the normalized time method. Different values for the coefficient of friction have been taken following a normal Gaussian distribution to see its influence on the working parameters. The algorithm has been applied to several examples and the results show that computation times are compatible with real-time work, so the authors call them efficient generated trajectories as they avoid collisions. Besides, working parameters such as the minimum trajectory time, the maximum vehicle speed, computational time, and consumed energy have been monitored and some conclusions have been reached.