Nursing resource team capacity planning using forecasting and optimization methods: A case study

2020 ◽  
Vol 28 (2) ◽  
pp. 229-238
Author(s):  
Neal Kaw ◽  
Joshua Murray ◽  
Art Jerome Lopez ◽  
Muhammad M. Mamdani
Keyword(s):  
Capacity Planning ◽  
Optimization Methods ◽  
Team Capacity

scholarly journals Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3385
Author(s):  
Erickson Puchta ◽  
Priscilla Bassetto ◽  
Lucas Biuk ◽  
Marco Itaborahy Filho ◽  
Attilio Converti ◽  
...  
Keyword(s):  
Pid Controller ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Buck Converter ◽  
Algebraic Solution ◽  
Nonlinear Load ◽  
Bee Colony ◽  
Arduous Task ◽  
Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Urban Vehicle Emissions Management under Uncertainty - A Traffic Planning Model with Interval-Parameter Programming

2013 ◽  
Vol 864-867 ◽  
pp. 1586-1591
Author(s):  
Hong Liang Zhang
Keyword(s):  
Vehicle Emissions ◽  
Optimization Methods ◽  
Transportation System ◽  
Programming Method ◽  
Planning Model ◽  
Traffic Planning ◽  
Interval Parameter ◽  
Interval Linear ◽  
Virtual City

In this study, an interval-parameter programming method has been used for urban vehicle emissions management under uncertainty. The model improves upon the existing optimization methods with advantages in uncertainty reflection, system costs and limitation of emission. Moreover, the model is applied to a case study of urban vehicle emissions management in a virtual city. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.

Capacity Planning for Cluster Tools of Wafer Fabrication Plant

2010 ◽  
Vol 450 ◽  
pp. 365-368
Author(s):  
James C. Chen ◽  
Chia Wen Chen ◽  
Kou Huang Chen ◽  
Chien Hsin Lin
Keyword(s):  
Capacity Planning ◽  
Production Costs ◽  
Wafer Fabrication ◽  
Total Production ◽  
Output Rate ◽  
Cluster Tools ◽  
And Performance ◽  
Using Data ◽  
Capital Intensive

Wafer fabrication is a capital intensive industry. A 12-inch wafer fabrication plant needs a typical investment of US$ 3 billion, and the equipment cost constitutes about two-thirds to three-quarters of the total production costs. Therefore, capacity planning is crucial to the investment and performance of wafer fabrication plants. Several formulae are presented to calculate the required number of machines with sequential, parallel, and batch processing characteristics, respectively. An AutoSched AP simulation model using data from real foundry fabrication plants is used in a case study to evaluate the performance of the proposed formulae. Simulation results indicate that the proposed formulae can quickly and accurately calculate the required number of cluster tools leading to the required monthly output rate.

scholarly journals Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880113
Author(s):  
Miguel Angel Funes Lora ◽  
Edgar Alfredo Portilla-Flores ◽  
Raul Rivera Blas ◽  
Emmanuel Alejandro Merchán Cruz ◽  
Manuel Faraón Carbajal Romero
Keyword(s):  
Numerical Technique ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
End Effector ◽  
Bee Colony ◽  
Ill Posed ◽  
Metaheuristic Techniques

Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic end-effector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton’s method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.

scholarly journals Simulation modelling of patient flow and capacity planning for regional long-term care needs: a case study

2017 ◽  
Vol 8 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Ki-Hwan Bae ◽  
Molly Jones ◽  
Gerald Evans ◽  
Demetra Antimisiaris
Keyword(s):  
Capacity Planning ◽  
Long Term Care ◽  
Patient Flow ◽  
Simulation Modelling ◽  
Care Needs ◽  
Term Care

Identification of the Optimal Product Configuration and Parameters for Improving Manufacturability

1996 ◽  
Author(s):  
Deyi Xue
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Product Configuration ◽  
Design System ◽  
Optimization Approach ◽  
Continuous Variables ◽  
Integer Variables ◽  
System A ◽  
Global Optimization Methods

Abstract A global optimization approach for identifying the optimal product configuration and parameters is proposed to improve manufacturability measures including feasibility, cost, and time of production. Different product configurations, including alternative design candidates and production processes, are represented by an AND/OR graph. Product parameters are described by variables including continuous variables, integer variables, Boolean variables, and discrete variables. Two global optimization methods, genetic algorithm and simulated annealing, are employed for identifying the optimal product configuration and parameters. The introduced approach serves as a key component in an integrated concurrent design system. A case study example is given to show how the proposed method is used for solving the engineering problems.

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