The objective of this paper is to design an operating program to maximize the operating rooms' use efficiency and minimize the overtime cost.
The optimization aimed to maximize the operating profit and organic matter balance, and to minimize the labor requirement and soil nitrogen losses.
On the premise of determining the minimum sufficient number of Monte Carlo simulation, this method maximizes the operating COP (coefficient of performance) and minimizing the annual total cost.
Two control strategies (decentralized control and unit-based model predictive control) are compared according to their capacity to reduce the total error and maximize the operating profits.
An Artificial Neural Network – Genetic Algorithm hybrid algorithm is then developed to determine the optimum configuration of the pin fin heat sink that maximizes the operating time for the n-eicosane based heat sink.
To maximize the operating performance of FMS with AGVs, many parameters must be considered, including the number, velocity, and dispatching rule of AGV, part-types, scheduling, and buffer sizes.
The design and the kinematics of the robot were optimized in order to reach all of the area of the middle ear, to maximize the operating field of view and the distance between the robot's body and the patient (for safety reasons) [ 7, 9].
The problem is modeled as a Mixed Integer Linear Programming (MILP) problem with an objective of maximizing the train operating company's profit while maintaining ε level of passenger satisfaction.
