Addressing the Growing Inadequacies of the Ellerth/Faragher Affirmative Defense: Fashioning a Sensible and Feasible Solution

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

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Efficiency of Commercial Banking in Developing Countries

Mathematics ◽

10.3390/math9141597 ◽

2021 ◽

Vol 9 (14) ◽

pp. 1597

Author(s):

Violeta Cvetkoska ◽

Katerina Fotova Čiković ◽

Marija Tasheva

Keyword(s):

Developing Countries ◽

Commercial Banks ◽

Feasible Solution ◽

Banking System ◽

Efficiency Score ◽

Commercial Banking ◽

Real Situation ◽

Super Efficiency ◽

Bank Management ◽

The Stability

The aim of this paper is to evaluate the relative efficiency of commercial banks in three developing countries in Europe (North Macedonia, Serbia, and Croatia) in the period from 2015 to 2019, and to provide targets for improvement for the inefficient banks by using DEA. The variables are selected under the income-based approach. Based on the output-oriented BCC model, unusual results are obtained for a few commercial banks in each country, that is, they are BCC relative efficient, which is contrary to the real situation. In order to identify outliers that can affect the efficiency results, a super-efficiency procedure is applied so that banks with a super-efficiency score higher than 1.2 (outliers) or for which a feasible solution was not found are considered in detail and removed, and then the output-oriented BCC model is rerun. Based on the obtained results, the Macedonian commercial banking system shows the highest efficiency (91.1%), followed by the Croatian (90.9%) and the Serbian (81.9%) banking system. The estimated targets for improvement of the inefficient commercial banks could help their top bank management in better resource allocation and making fact-based and faster decisions by which they can improve the operation of the banks they lead and contribute to the stability of the financial system.

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Nonconvex constrained optimization by a filtering branch and bound

Journal of Global Optimization ◽

10.1007/s10898-020-00956-2 ◽

2020 ◽

Author(s):

Gabriele Eichfelder ◽

Kathrin Klamroth ◽

Julia Niebling

Keyword(s):

Constrained Optimization ◽

Optimization Problem ◽

Feasible Solution ◽

Constrained Optimization Problem ◽

Numerical Tests ◽

Nonconvex Constrained Optimization ◽

Objective Value ◽

Feasible Solutions ◽

Quality Guarantee ◽

Single Objective

AbstractA major difficulty in optimization with nonconvex constraints is to find feasible solutions. As simple examples show, the $$\alpha $$ α BB-algorithm for single-objective optimization may fail to compute feasible solutions even though this algorithm is a popular method in global optimization. In this work, we introduce a filtering approach motivated by a multiobjective reformulation of the constrained optimization problem. Moreover, the multiobjective reformulation enables to identify the trade-off between constraint satisfaction and objective value which is also reflected in the quality guarantee. Numerical tests validate that we indeed can find feasible and often optimal solutions where the classical single-objective $$\alpha $$ α BB method fails, i.e., it terminates without ever finding a feasible solution.

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Optimum Path Planning for Mechanical Manipulators

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.3139654 ◽

1981 ◽

Vol 103 (2) ◽

pp. 142-151 ◽

Cited By ~ 112

Author(s):

J. Y. S. Luh ◽

C. S. Lin

Keyword(s):

Path Planning ◽

Feasible Solution ◽

Computing Time ◽

Inequality Constraints ◽

Programming Algorithm ◽

Planning Problem ◽

Linear Inequality Constraints ◽

Nonlinear Inequality ◽

Angular Velocities ◽

Nonlinear Inequality Constraints

To assure a successful completion of an assigned task without interruption, such as the collision with fixtures, the hand of a mechanical manipulator often travels along a preplanned path. An advantage of requiring the path to be composed of straight-line segments in Cartesian coordinates is to provide a capability for controlled interaction with objects on a moving conveyor. This paper presents a method of obtaining a time schedule of velocities and accelerations along the path that the manipulator may adopt to obtain a minimum traveling time, under the constraints of composite Cartesian limit on linear and angular velocities and accelerations. Because of the involvement of a linear performance index and a large number of nonlinear inequality constraints, which are generated from physical limitations, the “method of approximate programming (MAP)” is applied. Depending on the initial choice of a feasible solution, the iterated feasible solution, however, does not converge to the optimum feasible point, but is often entrapped at some other point of the boundary of the constraint set. To overcome the obstacle, MAP is modified so that the feasible solution of each of the iterated linear programming problems is shifted to the boundaries corresponding to the original, linear inequality constraints. To reduce the computing time, a “direct approximate programming algorithm (DAPA)” is developed, implemented and shown to converge to optimum feasible solution for the path planning problem. Programs in FORTRAN language have been written for both the modified MAP and DAPA, and are illustrated by a numerical example for the purpose of comparison.

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Barrier Function Method in Reliability Based Design Optimization

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.297-300.1882 ◽

2005 ◽

Vol 297-300 ◽

pp. 1882-1887

Author(s):

Tae Hee Lee ◽

Jung Hun Yoo

Keyword(s):

Design Optimization ◽

Barrier Function ◽

Reliability Index ◽

Feasible Solution ◽

Reliability Based Design Optimization ◽

User Requirement ◽

Mean Values ◽

Reliability Based Design ◽

Design Variables ◽

Reliability Method

In practical design applications, most design variables such as thickness, diameter and material properties are not deterministic but stochastic numbers that can be represented by their mean values with variances because of various uncertainties. When the uncertainties related with design variables and manufacturing process are considered in engineering design, the specified reliability of the design can be achieved by using the so-called reliability based design optimization. Reliability based design optimization takes into account the uncertainties in the design in order to meet the user requirement of the specified reliability while seeking optimal solution. Reliability based design optimization of a real system becomes now an emerging technique to achieve reliability, robustness and safety of the design. It is, however, well known that reliability based design optimization can often have so multiple local optima that it cannot converge into the specified reliability. To overcome this difficulty, barrier function approach in reliability based design optimization is proposed in this research and feasible solution with specified reliability index is always provided if a feasible solution is available. To illustrate the proposed formulation, reliability based design optimization of a bracket design is performed. Advanced mean value method and first order reliability method are employed for reliability analysis and their optimization results are compared with reliability index approach based on the accuracy and efficiency.

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