Relevant bibliographies by topics / Resource allocation – Mathematical models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Resource allocation – Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Resource allocation – Mathematical models"

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Azibek, B., S. Kusdavletov, D. Aresh, Ph Quoc-Viet, and B. Maham. "DEFENDER-ATTACKER MODELS FOR RESOURCE ALLOCATION IN INFORMATION SECURITY." Scientific Journal of Astana IT University, no. 8 (December 29, 2021): 4–11. http://dx.doi.org/10.37943/aitu.2021.96.94.001.

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Today, information security in defender-attacker game models is getting more attention from the research community. A game-theoretic approach applied in resource allocation study requires security in information for successive defensive strategy against attackers. For the defensive side players, allocating resources effectively and appropriately is essential to maintain the winning position against the attacking side. It can be possible by making the best response to the attack, i.e., by defining the most effective secure defensive strategy. This present work develops one defender – two attackers game model to determine the defensive strategy based on the Nash equilibrium and Stackelberg leadership equilibrium solutions of one defender-one attacker game model. Both game models are designed and studied in two scenarios: simultaneous and sequential modes. Game modes are defined according to the information that is available for attackers. In the first one, the defender is not aware of the attack and makes a simultaneous decision of how many resources should be allocated. Meanwhile, in the second mode, the defender knows about the entrance of attackers into a market and is assumed to commit a better strategy. The budget constraints are studied for both modes, all calculations and proof are presented in the work. According to obtained game mathematical models, it can be highlighted that network value of customers is important through the introduction of new variables in modeling and performing game theory equilibriums. This paper underlines the importance of information availability, budget limitations, and network value of customers in resource allocation through mathematical models and proofs; and focuses on modeling and studying defender-attacker games to define defensive strategy.
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Karpenko, М., and О. Stelma. "RESOURCE ALLOCATION MODELS IN HIERARCHICAL MANAGEMENT SYSTEMS." Municipal economy of cities 1, no. 154 (2020): 120–25. http://dx.doi.org/10.33042/2522-1809-2020-1-154-120-125.

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The article proposes a mathematical model of the hierarchical system of volume-dynamic resource allocation. The model describes resource consumption processes in multi-layered systems and allows us to view the management of such systems from a single perspective, to reflect the interrelationship of decisions formed at different levels of the hierarchy. According to the proposed model, a production (or business) system is considered as a large dynamic resource allocation system that is characterized by the interaction of three components: processes, resources, and time (R, P, and T.). Each of these components is represented by many lower-level elements with a defined ratio of a partial order, which sets the structure of the corresponding systems. The article proposes the way of description and features of the system of resources, processes and time, rules of aggregation, and disaggregation taking into account the structure of R, P, and T systems. On the basis of the described models, a description of the production system at the lower level in the form of a binary function π0 , as well as procedures for the formation of appropriate descriptions for arbitrary levels of the hierarchy in the form of a set of tetra relations πi. An algorithm for the formation of the solution π0 , as well as procedures for its transformation to the model of an arbitrary level, is proposed. The use of formal methods to describe the procedures of resource allocation at different levels of the hierarchy allows building a single database, to develop a structured and compact system of requests for information in the formation of management decisions. In such a system, data for processing queries are represented by a tuple of three elements Kin (levels of input aggregation by process and time resources), the basic solution πб, a set of elements R, P, T of the corresponding level, a tuple Kout (three levels of output aggregation). Depending on the Kin and Kout, values, the system handles the πб base solution using either aggregation or dis-aggregation procedures, resulting in a final result. Keywords: management, resources, processes, model, resource allocation, aggregation, disaggregation, math-ematical programming, optimization.
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Goelzer, Anne, and Vincent Fromion. "Resource allocation in living organisms." Biochemical Society Transactions 45, no. 4 (2017): 945–52. http://dx.doi.org/10.1042/bst20160436.

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Quantitative prediction of resource allocation for living systems has been an intensive area of research in the field of biology. Resource allocation was initially investigated in higher organisms by using empirical mathematical models based on mass distribution. A challenge is now to go a step further by reconciling the cellular scale to the individual scale. In the present paper, we review the foundations of modelling of resource allocation, particularly at the cellular scale: from small macro-molecular models to genome-scale cellular models. We enlighten how the combination of omic measurements and computational advances together with systems biology has contributed to dramatic progresses in the current understanding and prediction of cellular resource allocation. Accurate genome-wide predictive methods of resource allocation based on the resource balance analysis (RBA) framework have been developed and ensure a good trade-off between the complexity/tractability and the prediction capability of the model. The RBA framework shows promise for a wide range of applications in metabolic engineering and synthetic biology, and for pursuing investigations of the design principles of cellular and multi-cellular organisms.
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BURDETT, ROBERT L., and ERHAN KOZAN. "THE ASSIGNMENT OF INDIVIDUAL RENEWABLE RESOURCES IN SCHEDULING." Asia-Pacific Journal of Operational Research 21, no. 03 (2004): 355–77. http://dx.doi.org/10.1142/s021759590400028x.

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Resource constrained scheduling problems are concerned with the allocation of limited resources to tasks over time. The solution to these problems is often a sequence, resource allocation, and schedule. When human workers are incorporated as a renewable resource, the allocation is defined as the number of workers assigned to perform each task. In practice, however, this solution does not adequately address how individual workers are to be assigned to tasks. This paper, therefore, provides mathematical models and heuristic techniques for solving this multi-period precedence constrained assignment problem. Results of a significant numerical investigation are also presented.
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Russell, D. G. "RESOURCE ALLOCATION IN AGRICULTURAL RESEARCH USING SOCIO-ECONOMIC EVALUATION AND MATHEMATICAL MODELS*." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 23, no. 2 (2008): 29–52. http://dx.doi.org/10.1111/j.1744-7976.1975.tb00949.x.

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Ahrari, Amir, and Ali Haghani. "A New Decision Support System for Optimal Integrated Project Scheduling and Resource Planning." International Journal of Information Technology Project Management 10, no. 3 (2019): 18–33. http://dx.doi.org/10.4018/ijitpm.2019070102.

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Two scheduling practices are commonly used depending on the availability of resources. When resources are not expensive, activities are scheduled and then resources are allocated until the available resources are exhausted. Then, iterative adjustments are applied to the resource allocation plan and the activities sequence to reach a feasible solution. Conversely, when expensive resources are involved, a resource allocation plan based on the economics of the resource is established and then activities are scheduled accordingly. However, Resource Constrained Scheduling Problems (RCSP) are not solved efficiently with either of these approaches. To find the optimal solution, activity scheduling and resource allocation should be formulated as an integrated optimization problem. Such models become numerically cumbersome for practical size problems and difficult to solve. In this article, a novel mathematical formulation and an efficient solution algorithm are proposed for solving RCSPs. Then, this framework is used for solving a practical problem in the context of the construction industry.
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Wang, Yanyan, and Baiqing Sun. "A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness." Mathematical Problems in Engineering 2018 (October 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/7943498.

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Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allocation after Ya’an earthquake in China to verify the applicability of the proposed model.
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Hosseinzadeh Lotfi, F., A. A. Noora, G. R. Jahanshahloo, J. Gerami, and M. R. Mozaffari. "Centralized resource allocation for enhanced Russell models." Journal of Computational and Applied Mathematics 235, no. 1 (2010): 1–10. http://dx.doi.org/10.1016/j.cam.2010.05.029.

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Shen, Linfang, Kuoyu Liu, Jinfei Chai, et al. "Research on the Mathematical Model for Optimal Allocation of Human Resources in the Operation and Maintenance Units of a Heavy Haul Railway." Mathematics 10, no. 19 (2022): 3707. http://dx.doi.org/10.3390/math10193707.

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According to the existing personnel structure, quantity, development strategy, and market demand of the Shuohuang Railway Company’s operation and maintenance project, the demand quantity of various employees of the company for the past three years is predicted, and a human resource optimization model based on existing human resources and future plans is established. Then, the optimal solutions of the two mathematical models were calculated and analyzed using LINGO software. Finally, combined with the actual situation, the optimal allocation of human resources for the operation and maintenance project of KY company was obtained. The following conclusions are obtained. (1) For the optimal allocation model of existing human resources, the maximum net profit of the optimal staffing model is CNY 3258000. (2) The human resources allocation cost of the minimum dismissal model is CNY 81000. (3) The human resources allocation cost of the lowest cost model is CNY 15500. The research results can effectively guide the human resource management of the operation and maintenance project of the Shuohuang Railway Company, and have important theoretical and practical significance for further analysis of human resources model and its optimal allocation method.
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KAPUR, P. K., P. C. JHA, and A. K. BARDHAN. "OPTIMAL ALLOCATION OF TESTING RESOURCE FOR A MODULAR SOFTWARE." Asia-Pacific Journal of Operational Research 21, no. 03 (2004): 333–54. http://dx.doi.org/10.1142/s0217595904000278.

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Several Software Reliability Growth Models (SRGMs) have been developed in the literature to account for exponential and S-shaped growth curves. There are others, which can account for both depending on the testing environment. Such models are termed as flexible models. Most of the models use calendar/execution time as the testing time. Very few SRGMs have been developed which define explicitly the testing effort functions into the modeling. Testing effort/resource may be computer time and manpower needed during testing. The aim of this paper is twofold. 1. Develop an SRGM with testing efforts which is also flexible 2. Use model in (1) to allocate optimally the testing resource to a modular software subject to different constraints. Model developed in (1) is validated on different data sets and predictive validity is established. Optimization problems in (2) are mathematical programming problems having the sum of fractional functions as the common objective. These are solved using a dynamic programming approach and closed form solutions have been obtained. Finally, numerical illustrations are provided for two optimization problems.
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Dissertations / Theses on the topic "Resource allocation – Mathematical models"

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Nagarajan, Krishnamurthy. "New resource allocation strategies based on statistical network traffic models." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/33437.

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Di, Sheng, and 狄盛. "Optimal divisible resource allocation for self-organizing cloud." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4703130X.

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Dharmakadar, Aida. "An algorithmic solution to the minimax resource allocation problem with multimodal functions." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-10062009-020310/.

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Liu, Kai, and 劉愷. "A decentralized congestion management approach for the multilateral energy transaction via optimal resource allocation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38750107.

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Nsoh, Stephen Atambire. "Resource allocation in WiMAX mesh networks." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2012, 2012. http://hdl.handle.net/10133/3371.

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The IEEE 802.16 standard popularly known as WiMAX is at the forefront of the technological drive. Achieving high system throughput in these networks is challenging due to interference which limits concurrent transmissions. In this thesis, we study routing and link scheduling inWiMAX mesh networks. We present simple joint routing and link scheduling algorithms that have outperformed most of the existing proposals in our experiments. Our session based routing and links scheduling produced results approximately 90% of a trivial lower bound. We also study the problem of quality of service (QoS) provisioning in WiMAX mesh networks. QoS has become an attractive area of study driven by the increasing demand for multimedia content delivered wirelessly. To accommodate the different applications, the IEEE 802.16 standard defines four classes of service. In this dissertation, we propose a comprehensive scheme consisting of routing, link scheduling, call admission control (CAC) and channel assignment that considers all classes of service. Much of the work in the literature considers each of these problems in isolation. Our routing schemes use a metric that combines interference and traffic load to compute routes for requests while our link scheduling ensures that the QoS requirements of admitted requests are strictly met. Results from our simulation indicate that our routing and link scheduling schemes significantly improve network performance when the network is congested.<br>ix, 77 leaves : ill. ; 29 cm
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Nordai, Frederick Leon. "Balanced, capacitated, location-allocation problems on networks with a continuum of demand." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54313.

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Location-allocation problems can be described generically as follows: Given the location or distribution (perhaps, probabilistic) of a set of customers and their associated demands for a given product or service, determine the optimum location of a number of service facilities and the allocation of products or services from facilities to customers, so as to minimize total (expected) location and transportation costs. This study is concerned with a particular subclass of location-allocation problems involving capacitated facilities and a continuum of demand. Specifically, two minisum, network-based location-allocation problems are analyzed in which facilities having known finite capacities are to be located so as to optimally supply/serve a known continuum of demand. The first problem considered herein, is an absolute p-median problem in which p > l capacitated facilities are to be located on a chain graph having both nodal and link demands, the latter of which are defined by nonnegative, integrable demand functions. In addition, the problem is balanced, in that it is assumed the total demand equals the total supply. An exact solution procedure is developed, wherein the optimality of a certain location-allocation scheme (for any given ordering of the facilities) is used to effect a branch and bound approach by which one can identify an optimal solution to the problem. Results from the chain graph analysis are then used to develop an algorithm with which one can solve a dynamic, sequential location-allocation problem in which a single facility per period is required to be located on the chain. Finally, an exact solution procedure is developed for locating a capacitated, absolute 2-median on a tree graph having both nodal and link demands and for which the total demand is again equal to the total supply. This procedure utilizes an algorithm to construct two subtrees, each of whose ends constitute a set of candidate optimal locations for one of the two elements of an absolute 2-median. Additional localization results are used to further reduce the number of candidate pairs (of ends) that need to be considered, and then a post-localization analysis provides efficient methods of comparing the relative costs of the remaining pairs.<br>Ph. D.
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Kamat, Kuldip U. "Minimizing total tardiness and crew size in labor intensive cells using mathematical models." Ohio : Ohio University, 2007. http://www.ohiolink.edu/etd/view.cgi?ohiou1181108441.

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Nyamugure, Philimon. "Modification, development, application and computational experiments of some selected network, distribution and resource allocation models in operations research." Thesis, University of Limpopo, 2017. http://hdl.handle.net/10386/1930.

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Thesis (Ph.D. (Statistics)) -- University of Limpopo, 2017<br>Operations Research (OR) is a scientific method for developing quantitatively well-grounded recommendations for decision making. While it is true that it uses a variety of mathematical techniques, OR has a much broader scope. It is in fact a systematic approach to solving problems, which uses one or more analytical tools in the process of analysis. Over the years, OR has evolved through different stages. This study is motivated by new real-world challenges needed for efficiency and innovation in line with the aims and objectives of OR – the science of better, as classified by the OR Society of the United Kingdom. New real-world challenges are encountered on a daily basis from problems arising in the fields of water, energy, agriculture, mining, tourism, IT development, natural phenomena, transport, climate change, economic and other societal requirements. To counter all these challenges, new techniques ought to be developed. The growth of global markets and the resulting increase in competition have highlighted the need for OR techniques to be improved. These developments, among other reasons, are an indication that new techniques are needed to improve the day-to-day running of organisations, regardless of size, type and location. The principal aim of this study is to modify and develop new OR techniques that can be used to solve emerging problems encountered in the areas of linear programming, integer programming, mixed integer programming, network routing and travelling salesman problems. Distribution models, resource allocation models, travelling salesman problem, general linear mixed integer ii programming and other network problems that occur in real life, have been modelled mathematically in this thesis. Most of these models belong to the NP-hard (non-deterministic polynomial) class of difficult problems. In other words, these types of problems cannot be solved in polynomial time (P). No general purpose algorithm for these problems is known. The thesis is divided into two major areas namely: (1) network models and (2) resource allocation and distribution models. Under network models, five new techniques have been developed: the minimum weight algorithm for a non-directed network, maximum reliability route in both non-directed and directed acyclic network, minimum spanning tree with index less than two, routing through 0k0 specified nodes, and a new heuristic to the travelling salesman problem. Under the resource allocation and distribution models section, four new models have been developed, and these are: a unified approach to solve transportation and assignment problems, a transportation branch and bound algorithm for the generalised assignment problem, a new hybrid search method over the extreme points for solving a large-scale LP model with non-negative coefficients, and a heuristic for a mixed integer program using the characteristic equation approach. In most of the nine approaches developed in the thesis, efforts were done to compare the effectiveness of the new approaches to existing techniques. Improvements in the new techniques in solving problems were noted. However, it was difficult to compare some of the new techniques to the existing ones because computational packages of the new techniques need to be developed first. This aspect will be subject matter of future research on developing these techniques further. It was concluded with strong evidence, that development of new OR techniques is a must if we are to encounter the emerging problems faced by the world today. Key words: NP-hard problem, Network models, Reliability, Heuristic, Largescale LP, Characteristic equation, Algorithm.
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Garkusha, Sergey, and Essa Mohammed Al-Azzawi. "Model of Transmission Rate Allocation WiMAX with Taking Into Account the Defined Priorities." Thesis, TCSET'2014, 2014. http://dspace.puet.edu.ua/handle/123456789/1955.

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The results of development a mathematical model for transmission rate allocation downlink technology WiMAX are presented. The novelty of the proposed model is possibility to prevent a limit transmission rate allocated to the service flows of the user stations in the downlink by using the WiMAX technology linear or linearquadratic objective function. Using the mathematical model is directed to allocation between subscriber stations of a time-frequency resource of the downlink, which in turn improves the conditions in the electromagnetic frequency range used. The influence of the priority request rate used in the model is the nature of the possible failures
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Westhoek, Annet. "Resource allocation in the legume-rhizobia symbiosis : an integration of modelling and experimental approaches." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:66ed2e7d-85d3-4090-a822-28609ea866c7.

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The symbiosis between plants of the legume family and nitrogen-fixing rhizobia underpins global food security. Legume crops are a major source of protein in human diets, either directly or indirectly as feed for livestock. Application of inoculant rhizobial strains is common practice in many areas, as plant growth is often nitrogen limited and the symbiosis can significantly enhance yields. However, rhizobial strains and outcomes of the symbiosis vary widely. This variation has also been studied by evolutionary biologists interested in the stability of mutualisms. They proposed that plants may prevent establishing symbioses with ineffective strains (partner choice), or provide them with fewer resources (sanctioning). I studied both mechanisms, combining modelling and experimental approaches. Mathematical modelling was used to predict how plants should allocate resources to maximise growth rates, depending on rhizobial nitrogen provision and carbon requirements and on soil nitrogen conditions. The use of marked mutant strains – easily distinguishable and differing in a single rhizobial characteristic – overcame previous experimental difficulties. It was found that pea (Pisum sativum L.) plants are not able to exert partner choice, but do sanction in a more complex way than was previously established. In line with model predictions, resources were preferentially allocated to the single – best available – strain, so that resources allocated to an intermediate-fixing strain depended on whether or not a strain providing more nitrogen was available. Contrary to model predictions, there was no indication of discrimination based on rhizobial carbon requirements. The results cannot be explained by resource allocation in proportion to nitrogen received, and indicate systemic integration of information from different nodules. I formulate a hypothesis about the underlying plant regulatory mechanisms, and discuss implications of the results for selecting inoculant strains and enhancing yields in the field. Future work will rely on further integration of theoretical and applied methods and perspectives.
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Books on the topic "Resource allocation – Mathematical models"

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Naoki, Katoh, ed. Resource allocation problems: Algorithmic approaches. MIT Press, 1988.

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Corless, Martin J. AIMD dynamics and distributed resource allocation. Society for Industrial and Applied Mathematics, 2016.

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1953-, Weber Richard, and Glazebrook Kevin D. 1950-, eds. Multi-armed bandit allocation indices. 2nd ed. John Wiley & Sons, 2011.

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Gittins, John C. Multi-armed bandit allocation indices. Wiley, 1989.

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James, Haddad Lawrence, Hoddinott John, Alderman Harold 1948-, and International Food Policy Research Institute., eds. Intrahousehold resource allocation in developing countries: Models, methods, and policy. Johns Hopkins University Press, 1997.

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Capital income taxation and resource allocation. North-Holland, 1987.

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Allocation models: Specification, estimation, and applications. Ballinger, 1986.

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Conrad, Jon M. Resource economics. 2nd ed. Cambridge University Press, 2010.

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An introduction to allocation rules. Springer, 2009.

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Rahders, Ralf. Verfahren und Probleme der Bestimmung des optimalen Werbebudgets: Eine modellorientierte Analyse unter besonderer Berücksichtigung dynamischer Aspekte und Entscheidungen bei mehrfacher Zielsetzung. Schulz-Kirchner, 1989.

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Book chapters on the topic "Resource allocation – Mathematical models"

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Domansky, Victor, and Victoria Kreps. "Social Equilibria for Competitive Resource Allocation Models." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56038-5_21.

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Mutanov, Galimkair. "Multi-Objective Stochastic Models for Making Decisions on Resource Allocation." In Mathematical Methods and Models in Economic Planning, Management and Budgeting. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45142-7_7.

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Vani, B. P., and R. Sundaraguru. "Novel Analytical Model for Resource Allocation Over Cognitive Radio in 5G Networks." In Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31362-3_30.

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Feng, Runhuan, José Garrido, Longhao Jin, Sooie-Hoe Loke, and Linfeng Zhang. "Epidemic Compartmental Models and Their Insurance Applications." In Springer Actuarial. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78334-1_2.

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AbstractOur society’s efforts to fight pandemics rely heavily on our ability to understand, model and predict the transmission dynamics of infectious diseases. Compartmental models are among the most commonly used mathematical tools to explain reported infections and deaths. This chapter offers a brief overview of basic compartmental models as well as several actuarial applications, ranging from product design and reserving of epidemic insurance, to the projection of healthcare demand and the allocation of scarce resources. The intent is to bridge classical epidemiological models with actuarial and financial applications that provide healthcare coverage and utilise limited healthcare resources during pandemics.
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Ageev, Kirill, Eduard Sopin, and Konstantin Samouylov. "Resource Sharing Model with Minimum Allocation for the Performance Analysis of Network Slicing." In Information Technologies and Mathematical Modelling. Queueing Theory and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72247-0_28.

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Ibrahim, Ahmed, and Attahiru S. Alfa. "Multicasting in a Single HAP System: System Model and Mathematical Formulation." In Optimization Methods for User Admissions and Radio Resource Allocation for Multicasting over High Altitude Platforms. River Publishers, 2022. http://dx.doi.org/10.1201/9781003339007-3.

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Tasneem, Rayeesa, and M. A. Jabbar. "An Insight into Load Balancing in Cloud Computing." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_113.

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AbstractCloud Computing has emerged as a High-performance computing model providing on-demand computing resources as services via the Internet. Services include applications, storage, processing power, allocation of resources and many more. It is a pay-per-use model. Despite of providing various services, it is also experiencing numerous challenges like data security, optimized resource utilization, performance management, cost management, Cloud migration and many more. Among all, Load Balancing is another key challenge faced by Cloud. Effective load balancing mechanism will optimize the utilization of resources and improve the cloud performance. Load balancing is a mechanism to identify the overloaded and under loaded nodes and then balance the load by uniformly distributing the workload among the nodes. Various load balancing mechanisms are proposed by various researchers by taking different performance metrics. However existing load balancing algorithms are suffering from various drawbacks. This paper emphasizes the comparative review of various algorithms on Load Balancing along with their advantages, shortcomings and mathematical models.
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Johansson, Stefan, Paul Davidsson, and Bengt Carlsson. "Coordination Models for Dynamic Resource Allocation." In Coordination Languages and Models. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45263-x_12.

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Indrusiak, Leandro Soares, Piotr Dziurzanski, and Amit Kumar Singh. "Load and Resource Models." In Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337997-2.

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Shimizu, Kiyotaka, Yo Ishizuka, and Jonathan F. Bard. "General Resource Allocation Problem for Decentralized Systems." In Nondifferentiable and Two-Level Mathematical Programming. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6305-1_12.

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Conference papers on the topic "Resource allocation – Mathematical models"

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Ming Liu, Zhihui Sun, and Xiaoning Zhang. "Mathematical model and algorithm for the berth and yard resource allocation at seaports." In 2017 14th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2017. http://dx.doi.org/10.1109/icsssm.2017.7996122.

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Zaitseva, Irina, Oleg Malafeyev, Olga Pankratova, Lydia Novozhilova, and Viktor Smelik. "Software implementation of game-theoretic models accompanying labor resource allocation processes." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0026853.

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Lemeshko, Oleksandr, Tetiana Lebedenko, Amal Mersni, and Ahmad M. Hailan. "Mathematical Optimization Model of Congestion Management, Resource Allocation and Congestion Avoidance on Network Routers." In 2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). IEEE, 2019. http://dx.doi.org/10.1109/ukrmico47782.2019.9165445.

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Agieva, Movlatkhan T., Olga I. Gorbaneva, and Gennady A. Ougolnitsky. "Dynamic SPICE-Model of Resource Allocation in Marketing Networks with Co-Directed Interests." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280699.

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Akai, Ryota, Hirofumi Amaya, and Kikuo Fujita. "Product Family Deployment Through Optimal Resource Allocation Under Market System." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28662.

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A series of products, i.e. a product family is deployed for effectively and flexibly meeting with a variety of customer’s needs under a given product platform. Since such a deployment consumes various engineering resources and simultaneously brings profits gradually over the time sequence, when and how respective modules are designed and respective products are launched to the market must be rationally planed. Further, as a nature of product families, module commonalization accelerates the deployment but infuses some overheads on features and production cost. This paper investigates such a product family deployment problem under the optimal design viewpoint. After some general discussions, a mathematical model of dynamic design decisions is conditionally developed by integrating a combinatorial optimization technique for decision of module selection on commonalization and a market system model with discrete choice analysis and for describing the compromise among sequence of product rollout, arrangement of product lineup, required engineering resource, expected profit, etc. Then, the compromise among those factors is illustrated through the case study on a simplified deployment problem of circuit boards for digital television sets. Finally, an optimal planning approach for product family deployment and accompanied resource allocation is envisioned based on the developed model and findings from the case studies.
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Pan, Deng, and Yingping Zheng. "Mathematical model and algorithm of optimal resource allocation in the large iron & steel complex." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6561473.

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Qiu, Yuming, Ping Ge, and Solomon C. Yim. "Risk-Based Resource Allocation for Collaborative System Design in Distributed Environment." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35478.

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Risk is becoming an important factor in facilitating the resource allocation in engineering design because of its essential role in evaluating functional reliability and mitigating system failures. In this work, we aim at expanding existing quantitative risk modeling methods to collaborative system designs regarding resource allocation in a distributed environment, where an overlapped risk item can affect multiple stakeholders, and correspondingly be examined by multiple evaluators simultaneously. Because of different perspectives and limited local information, various evaluators (responsible for same or different components of a system), though adopting the same risk definition and mathematical calculation, can still yield unsatisfying global results, such as inconsistent probability and/or confusing consequence evaluations, which can then cause potential barriers in achieving agreement or acceptable discrepancies among different evaluators involved in the collaborative system design. Built upon our existing work, a Risk-based Distributed Resource Allocation Methodology (R-DRAM) is developed to help system manager allocate limited resource to stakeholders, and further to components of the targeted system for the maximum global risk reduction. Besides probability and consequence, two additional risk properties, tolerance and hierarchy, are considered for comprehensive systematic risk design. Tolerance is introduced to indicate the effective risk reduction, and hierarchy is utilized to model the comprehensive risk hierarchy. Finally a theoretical framework based on cost-benefit measure is developed for resource allocation. A case study is demonstrated to show the implementation process. The preliminary investigation shows promise of the R-DRAM in facilitating risk-based resource allocation for collaborative system design using a systematic and quantifiable approach in distributed environment.
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Ozer, Ali Haydar, and Can Ozturan. "An auction based mathematical model and heuristics for resource co-allocation problem in grids and clouds." In 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control. IEEE, 2009. http://dx.doi.org/10.1109/icsccw.2009.5379493.

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Gromov, Yuri, Yuri Minin, Ali Abdulkarem Habib Alrammahi, and Farah Abbac Sari. "Probabilistic and Fuzzy Models of the Optimal Allocation of Resources of a Network Information System." In 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA). IEEE, 2019. http://dx.doi.org/10.1109/summa48161.2019.8947608.

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Suk, Hailie, and John Hall. "Connecting Qualitative and Quantitative Analysis Through Bond Graph Modeling and System Dynamics." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70796.

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Abstract Access to resources can contribute to social progress in extremely impoverished communities. The introduction of cyber-physical systems for electricity, water, and irrigation facilitates greater fulfillment of needs. Yet, the availability of resources may be inconsistent or lacking. The social dynamics of the community can provide insight into how the available resources support well-being. Thus, the cyber-physical system requires the addition of a social consideration to become cyber-physical-social systems. However, the social considerations typically include qualitative parameters. This prompts the need for integrating qualitative and quantitative information. In this paper, we present a method for mathematically representing qualitative and quantitative relationships. This is achieved by connecting Bond Graph Modeling and System Dynamics. The Bond Graph model is used to mathematically represent relationships between qualitative and quantitative elements. These relationships are used in the System Dynamics analysis. The method is anchored in expanding cyber-physical to cyber-physical-social systems through incorporating both qualitative and quantitative information in the systems analysis. The mathematical connectivity of qualitative and quantitative information is a key feature of this approach. A test problem in resource allocation is used to demonstrate the function and flexibility of the method. This is anchored in connecting qualitative and quantitative information in the analysis.
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