The Producer’s Minimum Cost Function

1981 ◽  
pp. 10-17
Author(s):  
Ronald W. Shephard
Keyword(s):  
Cost Function ◽  
Minimum Cost

Estimating a Non-Minimum Cost Function for Hospitals: Comment

1992 ◽  
Vol 58 (4) ◽  
pp. 1114 ◽  
Author(s):  
Scott E. Atkinson ◽  
Robert Halvorsen
Keyword(s):  
Cost Function ◽  
Minimum Cost

Constrained optimal terrain following/threat avoidance trajectory planning using network flow

2014 ◽  
Vol 118 (1203) ◽  
pp. 523-539 ◽  
Author(s):  
R. Zardashti ◽  
A. A. Nikkhah ◽  
M. J. Yazdanpanah
Keyword(s):  
Cost Function ◽  
Trajectory Planning ◽  
Network Flow ◽  
Equations Of Motion ◽  
Minimum Cost ◽  
Masking Effect ◽  
Search Problem ◽  
Starting Point ◽  
Terrain Following ◽  
Threat Avoidance

AbstractThis paper focuses on the trajectory planning for a UAV on a low altitude terrain following/threat avoidance (TF/TA) mission. Using a grid-based approximated discretisation scheme, the continuous constrained optimisation problem into a search problem is transformed over a finite network. A variant of the Minimum Cost Network Flow (MCNF) to this problem is then applied. Based on using the Digital Terrain Elevation Data (DTED) and discrete dynamic equations of motion, the four-dimensional (4D) trajectory (three spatial and one time dimensions) from a starting point to an end point is obtained by minimising a cost function subject to dynamic and mission constraints of the UAV. For each arc in the grid, a cost function is considered as the combination of the arc length, fuel consumption and flight time. The proposed algorithm which considers dynamic and altitude constraints of the UAV explicitly is then used to obtain the feasible trajectory. The resultant trajectory can increase the survivability of the UAV using the threat region avoidance and the terrain masking effect. After obtaining the feasible trajectory, an improved algorithm is proposed to smooth the trajectory. The numeric results are presented to verify the capability of the proposed approach to generate admissible trajectory in minimum possible time in comparison to the previous works.

scholarly journals Actuator Placement for Active Surge Control in a Multi-Stage Axial Compressor

1996 ◽  
Author(s):  
M. Montazeri-Gh. ◽  
D. J. Allerton ◽  
R. L. Elder
Keyword(s):  
Cost Function ◽  
Mass Flow ◽  
Controller Design ◽  
Axial Compressor ◽  
Minimum Cost ◽  
Linear Quadratic Regulator ◽  
Algebraic Riccati Equation ◽  
Linear Quadratic ◽  
Non Linear ◽  
Actuator Placement

This paper describes an actuator placement methodology for the active control of purely one-dimensional instabilities of a seven-stage axial compressor using an air bleeding strategy. In this theoretical study, using stage-by-stage non-linear modelling based on the conservation equations of mass, momentum, and energy, a scheduling LQR (Linear Quadratic Regulator) controller is designed for several actuator locations in a compressor from the first stage to the plenum. In this controller design, the LQR weighting matrices are selected so that the associated cost function includes only air bleeding mass flow leading to the minimisation of the air bleed. The LQR cost function represents a measure of the consumption of air bleeding and can be calculated analytically using the solution of an Algebraic Riccati Equation. From analysis of the cost at different compressor stages, the location of an air bleeding actuator is selected at the stage with the minimum cost. Finally, using an ACSL simulation program, the scheduling controller has been integrated with a non-linear. stage-by-stage model and the time response of the air bleeding mass flow at different locations has been obtained to confirm the results from the analytical approach. Results are presented to show actively stabilised compressor flow beyond the surge point where the air bleed is minimised. These results also indicate the preferred location of the actuator at the compressor downstream stages for both low and high compressor speeds.

Estimating a Non-Minimum Cost Function for Hospitals: Reply

1992 ◽  
Vol 58 (4) ◽  
pp. 1118 ◽  
Author(s):  
B. Kelly Eakin ◽  
Thomas J. Kniesner
Keyword(s):  
Cost Function ◽  
Minimum Cost

scholarly journals On the Complexity of Some Variations of Sorting by Transpositions

2020 ◽  
Vol 26 (9) ◽  
pp. 1076-1094
Author(s):  
Alexsandro Alexandrino ◽  
Andre Oliveira ◽  
Ulisses Dias ◽  
Zanoni Dias
Keyword(s):  
Comparative Genomics ◽  
Computational Biology ◽  
Cost Function ◽  
Large Scale ◽  
Minimum Cost ◽  
Evolutionary Distance ◽  
The Other ◽  
Np Hard ◽  
The Past

One of the main challenges in Computational Biology is to find the evolutionary distance between two organisms. In the field of comparative genomics, one way to estimate such distance is to find a minimum cost sequence of rearrangements (large scale mutations) needed to transform one genome into another, which is called the rearrangement distance. In the past decades, these problems were studied considering many types of rearrangements (such as reversals, transpositions, transreversals, and revrevs) and considering the same weight for all rearrangements, or different weights depending on the types of rearrangements. The complexity of the problems involving reversals, transpositions, and both rearrangements is known, even though the hardness proof for the problem combining reversals and transpositions was recently given. In this paper, we enhance the knowledge for these problems by proving that models involving transpositions alongside reversals, transreversals, and revrevs are NP-hard, considering weights w1 for reversals and w2 for the other rearrangements such that w2/w1 ≤ 1.5. In addition, we address a cost function related to the number of fragmentations caused by a rearrangement, proving that the problem of finding a minimum cost sorting sequence, considering the fragmentation cost function with some restrictions, is NP-hard for transpositions and the combination of reversals and transpositions.

scholarly journals Economical Optimization of the Mechanized Longwall Faces with Top Coal Caving Mining, In Horizontal Slices

2016 ◽  
Vol 61 (3) ◽  
pp. 651-676 ◽  
Author(s):  
Ilie Onica ◽  
Viorel Mihăilescu ◽  
Felicia Andrioni
Keyword(s):  
Mathematical Model ◽  
Cost Function ◽  
Unit Cost ◽  
Minimum Cost ◽  
Point Of View ◽  
Hard Coal ◽  
Economic Point ◽  
Optimizing Model ◽  
The Individual ◽  
Economical Optimization

Abstract To increase the economic and technical performances of the Jiu Valley hard coal mines, the top coal caving, in horizontal slices, mining methods (Bourbaki methods) were introduced, adapted to the local geo-mining conditions. This mining was successfully experimented by using classical technology, using the individual supports and coal blasting. In the future, it is planned to adopt the mechanized technology, with frame supports and shearers. The mechanized longwall faces with top coal caving mining, in horizontal slices, of coal seam no. 3 could be efficient only if the sizes of the top coal height and the panel length determine a minimum cost of production. Therefore, the goal of this paper is the optimization of these parameters, from a technical and economic point of view, taking into account the general model of the cost function, at the panel level. For that, it was necessary to make a certain sequence of analysis involving: technological unit establishment, purpose function and optimizing model. Thus, there attaining to the mathematical model of the unit cost, after determination of all the individual calculation articles, regarding the preparatory workings, coal face equipments, materials, energy, workforce, etc. Because of the complexity of the obtained technical and economic model, to determine the optimum sizes of the panel length and top coal height, it was necessary to archive a sensitivity analysis of the unit cost function to the main parameters implied into this mathematical model.

N-Body Potential Interaction as a Cost Function in the Elastic Model for SANET Cloud Computing

2012 ◽  
Vol 58 (1) ◽  
pp. 63-70
Author(s):  
Zenon Chaczko ◽  
Germano Resconi ◽  
Christopher Chiu ◽  
Shahrazad Aslanzadeh
Keyword(s):  
Cloud Computing ◽  
Cost Function ◽  
Interaction Potential ◽  
Minimum Cost ◽  
Potential Interaction ◽  
General Definition ◽  
Elastic Model ◽  
Metric Tensor ◽  
Body Potential ◽  
Macro Variables

N-Body Potential Interaction as a Cost Function in the Elastic Model for SANET Cloud ComputingGiven a connection graph of entities that send and receive a flow of data controlled by effort and given the parameters, the metric tensor is computed that is in the elastic relational flow to effort. The metric tensor can be represented by the Hessian of the interaction potential. Now the interaction potential or cost function can be among two entities: 3 entities or ‘N’ entities and can be separated into two main parts. The first part is the repulsion potential the entities move further from the others to obtain minimum cost, the second part is the attraction potential for which the entities move near to others to obtain the minimum cost. For Pauli's model [1], the attraction potential is a functional set of parameters given from the environment (all the elements that have an influence in the module can be the attraction of one entity to another). Now the cost function can be created in a space of macro-variables or macro-states that is less of all possible variables. Any macro-variable collect a set of micro-variables or microstates. Now from the hessian of the macro-variables, the Hessian is computed of the micro-variables in the singular points as stable or unstable only by matrix calculus without any analytical computation - possible when the macro-states are distant among entities. Trivially, the same method can be obtained by a general definition of the macro-variable or macro-states and micro-states or variables. As cloud computing for Sensor-Actor Networks (SANETS) is based on the bonding concept for complex interrelated systems; the bond valence or couple corresponds to the minimum of the interaction potential V and in the SANET cloud as the minimum cost.

Research on the Method of Reliability Allocation of Diesel Engine Base on the Cost Function

2012 ◽  
Vol 271-272 ◽  
pp. 1115-1120
Author(s):  
Jia Li ◽  
Ji Ze Guo ◽  
Hai Qing Zhou ◽  
You Wen Wei
Keyword(s):  
Diesel Engine ◽  
Cost Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Minimum Cost ◽  
Copula Function ◽  
Reliability Allocation ◽  
Cost Coefficient ◽  
Set Up ◽  
The Cost

In this paper, cost coefficient is introduced by using the technology of FMECA and FTA, and the DM model is proposed, the parameters of cost function are determined by applying the comprehensive evaluation method, the system reliability correlation model is set up by using copula function. the model is nonlinear programming, and the minimum cost is the goal of the model. The reliability allocation for diesel engine is completed by use of genetic algorithm. Finally, the feasibility and effectiveness of the model are verified through example.

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