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Books on the topic 'Capacity Maximization and Optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Lockett, Alan J. General-Purpose Optimization Through Information Maximization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62007-6.

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2

Narayanaswami, Sundaravalli. Yard capacity optimization. Ahmedabad: Indian Institute of Management, 2014.

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3

Bailey, Michael P. Maximization on matroids with random weights. Monterey, Calif: Naval Postgraduate School, 1991.

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4

Cellular mobile radio systems: Designing systems for capacity optimization. Chichester: John Wiley & Sons, 1997.

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5

Geerdes, Hans-Florian. UMTS Radio Network Planning: Mastering Cell Coupling for Capacity Optimization. Wiesbaden: Vieweg+Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, 2008.

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6

Ishiguro, M. DALL : Davidson's Algorithm for Log Likelihood Maximization: A FORTRAN subroutine for statistical model builders. Tokyo: Institute of Statistical Mathematics, 1989.

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7

Caramia, Massimiliano. Multi-objective management in freight logistics: Increasing capacity, service level and safety with optimization algorithms. London: Springer, 2008.

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8

1958-, Dell'Olmo Paolo, ed. Multi-objective management in freight logistics: Increasing capacity, service level and safety with optimization algorithms. London: Springer, 2008.

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9

Caramia, Massimiliano. Multi-objective management in freight logistics: Increasing capacity, service level and safety with optimization algorithms. London: Springer, 2008.

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10

Wilfried, Brauer, Hromkovič Juraj 1958-, Rozenberg Grzegorz, Salomaa Arto, and SpringerLink (Online service), eds. Time-Dependent Scheduling. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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11

Błażewicz, Jacek. Scheduling computer and manufacturing processes. 2nd ed. Berlin: Springer, 2001.

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12

Ovsyannikov, Evgeniy, and Tamara Gaytova. Optimal control of traction electric drives. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141767.

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The monograph considers various types of traction electric drives of motor vehicles intended for operation in urban conditions. Mathematical models of these systems are proposed. On the basis of parametric optimization and graphoanalytic method, a method of joint control of electric drives according to the criteria of minimum losses and maximum overload capacity, taking into account possible restrictions on the resources of power elements, has been developed. For a wide range of readers interested in improving motor vehicles. It will be useful for students, postgraduates and teachers of engineering and technical universities.

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13

Utility Maximization In Nonconvex Wireless Systems. Springer, 2012.

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14

Stockton, Richard V. Maximization of the capacity of the most survivable connections in a network. 1986.

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15

Kemp, Sid, and Adam N. Rosenberg. CDMA Capacity and Quality Optimization (Telecom Engineering). McGraw-Hill Professional, 2003.

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16

CDMA Capacity and Quality Optimization (Telecom Engineering). McGraw-Hill Professional, 2003.

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17

Optimization of Design for Better Structural Capacity. IGI Global, 2018.

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18

Hammuda, Husni. Cellular Mobile Radio Systems: Designing Systems for Capacity Optimization. John Wiley & Sons, 1998.

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19

Nolasco, Daniel A. Applied Optimization Debottle Capacity Reevaluation of Wastewater Treatment Plan. CRC, 2004.

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20

UMTS Radio Network Planning: Mastering Cell Coupling for Capacity Optimization. Wiesbaden: Vieweg+Teubner, 2008. http://dx.doi.org/10.1007/978-3-8348-9260-7.

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21

Cicchelli, Mauro. Planning pair-gain capacity in local telephone networks: an optimization model. 1986.

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22

Dynamic Capacity Management For Healthcare Advanced Methods And Tools For Optimization. Productivity Press, 2010.

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23

Caramia, Massimiliano, and Paolo Dell'Olmo. Multi-objective Management in Freight Logistics: Increasing Capacity, Service Level and Safety with Optimization Algorithms. Springer, 2008.

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24

Caramia, Massimiliano, and Paolo Dell’Olmo. Multi-objective Management in Freight Logistics: Increasing Capacity, Service Level, Sustainability, and Safety with Optimization Algorithms. Springer, 2020.

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25

Barz, Christiane. Risk-Averse Capacity Control in Revenue Management. Springer, 2008.

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26

Risk-Averse Capacity Control in Revenue Management. Springer, 2007.

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27

Cleaver, Joseph P., Alice Schmidt Kehaya, and Mikhail Kogan. Exercise, Frailty, and Functional Reserve: Concepts and Optimization. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190466268.003.0003.

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Regular exercise can increase the healthy lifespan of elderly patients. Despite this, most seniors are not getting enough exercise. This chapter summarizes the age-related declines in physical capacity, including loss of muscle power, decreased aerobic capacity (VO2max), sarcopenia, and increased adipose tissue, that can lead to inflammation and limitations of functional reserve. The concept of frailty is discussed. Clinicians are given practical techniques for evaluating risks in patients, providing an “exercise prescription” in an individually tailored manner, and measuring progress in their geriatric patients.

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28

Capacity Options for Revenue Management: Theory and Applications in the Air Cargo Industry (Lecture Notes in Economics and Mathematical Systems). Springer, 2006.

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29

Okasha, Samir. Wright’s Adaptive Landscape, Fisher’s Fundamental Theorem. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815082.003.0004.

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Fitness maximization, or optimization, is a controversial idea in evolutionary biology. One classical formulation of this idea is that natural selection will tend to push a population up a peak in an adaptive landscape, as Sewall Wright first proposed. However, the hill-climbing property only obtains under particular conditions, and even then the ascent is not usually by the steepest route; this shows why it is misleading to assimilate the process of natural selection to a process of goal-directed choice. A different formulation of the idea of fitness-maximization is R. A. Fisher’s ‘fundamental theorem of natural selection’. However, the theorem points only to a weak sense in which selection is an optimizing process, for it requires that ‘environmental constancy’ be understood in a highly specific way. It does not vindicate the claim that natural selection has an intrinsic tendency to produce adaptation.

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30

Ekblom, Paul. Evolutionary Approaches to Rational Choice. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.2.

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This chapter seeks to enrich and extend thinking about the rational choice perspective to offender decision making and its pivotal application in situational crime prevention by taking an evolutionary approach, which is still uncommon in crime science and criminology. The chapter introduces basic concepts of evolution, covering the brain and behavior, levels and types of explanation, the strained relationship with social science, and the evidencing of evolutionary processes. The focus then shifts to rationality, covering decision making; the wider suite of processes needed to understand rationality in action; and specific discussions of cooperation, humans’ wider “sociocognitive niche,” and development. Although evolutionary issues are addressed throughout, the penultimate section discusses how rationality in the broadest sense has unfolded over evolutionary history and the significant connection between maximization of utility in contemporary rational choice and maximization/optimization of fitness in evolution. The conclusion raises practical, empirical, and theoretical questions for crime science.

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31

The Optimization of water resource management in the ESCWA countries: A survey of measures taken by the ESCWA countries during the 1990s for the optimization of water resource management and capacity-building in the water sector. New York: United Nations, 2004.

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32

Silva, Elvira, Spiro E. Stefanou, and Alfons Oude Lansink. Dynamic Efficiency and Productivity Measurement. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190919474.001.0001.

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The book takes on a systematic treatment of dynamic decision making and performance measurement. The analytical foundations of the dynamic production technology are introduced and developed in detail for several primal representations of the technology with an emphasis on dynamic directional distance functions. Dynamic cost minimization and dynamic profit maximization are developed for primal and dual representations of the dynamic technology. A dynamic production environment can be characterized as one where current production decisions impact future production possibilities. Consequently, the dynamic perspective of production relationships necessarily involves the close interplay between stock and flow elements in the transformation process and how current decisions impact the changes in future stocks. Stock elements in the production transformation process can involve physical elements that can be effectively employed in the transformation process, which can include the stock of technical knowledge and expertise available to the decision maker during the decision period. The dynamic generalization of concepts measuring the production structure (e.g., economies of scale, economies of scope, capacity utilization) and performance (e.g., allocative, scale and technical inefficiency, productivity) are developed from primal and dual perspectives. As an important source of productivity growth, production efficiency analysis is the subject of countless studies. Yet, theoretical and empirical studies focusing on production efficiency have ignored typically the time interdependence of production decisions and the adjustment paths of the firm over time. The empirical implementation of these production and performance measures is developed at length for both nonparametric and econometric approaches.

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33

Raz, Amir, and Sheida Rabipour. How (not) to train the brain. Oxford University Press, 2019. http://dx.doi.org/10.1093/med-psych/9780198789673.001.0001.

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How (Not) to Train the Brain offers a scientifically honest account of brain training. It demolishes unfounded claims often made for brain training programs, offering instead useful, proven, methods for improving mental performance and capacity. It reviews the apps, books, and other products that have emerged in recent years claiming to boost cognitive power and focus emotion, destroying well-established myths and misconceptions about the brain. It offers alternative, easily implementable techniques, including a list of commercially available products that readers may wish to consider. This title includes interviews with leading experts and practitioners working with different brain training and mental optimization approaches. These interviews provide unique insights into the foundations and development of brain training techniques.

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34

Zaporozhets, A. O., and V. P. Babak. Control of fuel combustion in small and medium power boilers. PH “Akademperiodyka”, 2020. http://dx.doi.org/10.15407/akademperiodyka.418.128.

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The monograph deals with the problems of increasing the effi ciency of fuel combustion and reducing emissions of harmful substances in boilers with a capacity of up to 3.5 MW. Approaches for the formation of stoichiometric air-fuel mixtures in boilers are developed. Th e method for indirect determination of the concentration of air components was developed, which allows to increase the metrological characteristics of gas-analyzing devices. Methods, algorithms and programs to automate the combustion control process, while ensuring the reliability of the data, are created. A system for monitoring the fuel combustion process was developed, and it was implemented on the basis of the NIISTU-5 boiler unit. For researchers, engineers, as well as lecturers and postgraduates of higher educational institutions and scientifi c institutions, working in the fi eld of engineering and optimization in the energy.

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35

Veering, Bernadette, and Chris Dodds. Anaesthesia in the elderly. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0086.

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The elderly population continues to grow. As surgical intervention in disease processes becomes more aggressive, the anaesthetist is faced with an increasing number of elderly patients. Elderly patients should be approached with a clear understanding of ageing, how it occurs, how it affects specific organ systems, and how it may influence clinical care, when a patient is subjected to an operation. The ageing process is a multifactorial process, resulting in a decreased capacity for adaptation and producing a gradual decrease in functional reserve of many organ systems. This has significant effects on the physiological responses to surgical and pharmacological trespass faced during anaesthesia. Increasing age is associated with changes in the response to a wide variety of drugs. Changes in dose–response relationships may be as a result of changes in pharmacokinetics, pharmacodynamics, or a combination of both. One should realize that increasing age is associated with a large inter-individual variability in dose requirements. As such, it is important to carefully titrate the dose against the desired clinical effect in an older patient. Preoperative physical and mental state are the most important determinants of per- and postoperative morbidity and mortality. The number of co-morbidities increases with advanced age and as such, optimization of the medical condition is essential to reduce the morbidity and mortality.

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36

AlJaroudi, Wael. Risk Assessment Before Noncardiac Surgery. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0014.

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Perioperative risk assessment is essential in screening patients before noncardiac surgery. Cardiovascular complications such as fatal and non-fatal myocardial infarction (MI), ventricular arrhythmia, pulmonary edema, and stroke are important in-hospital causes of morbidity and mortality intra and post-operatively. The optimal approach is to identify patients at increased risk so that appropriate testing and therapeutic interventions are undertaken a priori to minimize such risk. The initial preoperative evaluation includes identification of surgery-specific risk, patient exercise functional capacity and clinical risk profile. Patients with major predictors of events such as acute coronary syndromes, recent MI, unstable arrhythmia, and severe valvular disease warrant further management and optimization that often lead to delaying surgery. Those with three or more predictors (history of ischemic heart disease, compensated heart failure, diabetes, renal insufficiency, or history of cerebrovascular disease) undergoing high- risk surgery often require stress testing. Although data from randomized prospective trials are lacking, numerous studies have demonstrated the utility of myocardial perfusion imaging (MPI) for determination of perioperative cardiac risk. The goal of this chapter is to review the use of MPI for preoperative risk assessment and the recommendations from the current guidelines. The focus will be on short-term and long-term prognosis including special groups such as after coronary stenting and before vascular surgery, liver and renal transplantation.

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