Journal articles: 'Optimization nonlinear resource allocation problems'

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Hu, Yifan, Mingang Liu, and Yizhi Feng. "Resource Allocation for SWIPT Systems with Nonlinear Energy Harvesting Model." Wireless Communications and Mobile Computing 2021 (April 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/5576356.

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In this paper, we study the resource allocation for simultaneous wireless information and power transfer (SWIPT) systems with the nonlinear energy harvesting (EH) model. A simple optimal resource allocation scheme based on the time slot switching is proposed to maximize the average achievable rate for the SWIPT systems. The optimal resource allocation is formulated as a nonconvex optimization problem, which is the combination of a series of nonconvex problems due to the binary feature of the time slot-switching ratio. The optimal problem is then solved by using the time-sharing strong duality theorem and Lagrange dual method. It is found that with the proposed optimal resource allocation scheme, the receiver should perform EH in the region of medium signal-to-noise ratio (SNR), whereas switching to information decoding (ID) is performed when the SNR is larger or smaller. The proposed resource allocation scheme is compared with the traditional time switching (TS) resource allocation scheme for the SWIPT systems with the nonlinear EH model. Numerical results show that the proposed resource allocation scheme significantly improves the system performance in energy efficiency.

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Barkalaya, O. G. "Investigating competition in the problems of optimal resource allocation." Economics and Management 28, no. 4 (May 1, 2022): 359–68. http://dx.doi.org/10.35854/1998-1627-2022-4-359-368.

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Aim. The presented study aims to address the issues of parameter estimation in the problems of optimal resources allocation for the previously introduced competition indicator; to analyze the influence of dimensionality, resource constraints, and other factors on the competition indicator; to exemplify the relationship between the indicator and the extremum of the objective function, constraints, and dual estimates.Tasks. The authors consider cases when the competition indicator captures a change in the initial data that cannot be estimated on the basis of traditional indicators of analysis and estimates: the maximum of the objective function, the optimal solution, Lagrange multipliers, or dual variables; determine the relationship between the competition indicator and the optimum of the objective function and dual variables through examples and in general; show that the analysis of the results of solving the problem becomes more capacious and informative if the factor of variable “competitiveness” is applied; identify patterns between efficiency, competition, resource constraints, and dual estimates.Methods. The selected competition indicator for optimal resource allocation tasks is based on the concept of “rigorous selection” of competitors applying for resources. The indicators are calculated in full accordance with the known optimality conditions for problems of this class, making it possible to interpret the results of optimization as a measure of competition for resources.Results. The provided examples reflect linear and nonlinear functions as well as the relationship between the competition indicator and dual estimates, resource constraints, and efficiency. It is proved that the competition indicator logically fits into the traditional analysis of the results of solving the problem of linear and nonlinear programming with allowance for duality.Conclusion. The competition indicators considered in the study can be included in the standard analysis for solving the problems of optimal resource allocation, which involves finding an extremum, searching for an optimal plan, analyzing stability, limits, dual estimates, a measure of resource scarcity. As can be seen from the examples, applying the competition indicator to the analysis not only makes the analysis of the results more capacious and informative, but also makes it possible to detect patterns between competition and efficiency, similar to when the removal of barriers and restrictions in the economy leads to its revival, and the reduction of resources causes increased competition.

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Song, Xin, Xiuwei Han, Yue Ni, Li Dong, and Lei Qin. "Joint Uplink and Downlink Resource Allocation for D2D Communications System." Future Internet 11, no. 1 (January 6, 2019): 12. http://dx.doi.org/10.3390/fi11010012.

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In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub-problems including power allocation and channel assignment. It is proved that the objective function of power control is a convex function, in which the optimal transmission power can be obtained. The Hungarian algorithm is developed to achieve joint uplink and downlink channel assignment. The proposed scheme can improve the system capacity performance and increase the spectrum efficiency. Numerical results reveal that the performance of the proposed scheme of jointly uplink and downlink is better than that of the schemes for independent allocation.

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Zhao, Pan, Wenlei Guo, Datong Xu, Zhiliang Jiang, Jie Chai, Lijun Sun, He Li, and Weiliang Han. "Hypergraph-based resource allocation for Device-to-Device underlay H-CRAN network." International Journal of Distributed Sensor Networks 16, no. 8 (August 2020): 155014772095133. http://dx.doi.org/10.1177/1550147720951337.

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In the hybrid communication scenario of the Heterogeneous Cloud Radio Access Network and Device-to-Device in 5G, spectrum efficiency promotion and the interference controlling caused by spectrum reuse are still challenges. In this article, a novel resource management method, consisting of power and channel allocation, is proposed to solve this problem. An optimization model to maximum the system throughput and spectrum efficiency of the system, which is constrained by Signal to Interference plus Noise Ratio requirements of all users in diverse layers, is established. To solve the non-convex mixed integer nonlinear optimization problem, the optimization model is decomposed into two sub-problems, which are all solvable quasi-convex power allocation and non-convex channel allocation. The first step is to solve a power allocation problem based on solid geometric programming with the vertex search method. Then, a channel allocation constructed by three-dimensional hypergraph matching is established, and the best result of this problem is obtained by a heuristic greed algorithm based on the bipartite conflict graph and µ-claw search. Finally, the simulation results show that the proposed scheme improves the throughput performance at least 6% over other algorithms.

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Lan, Yanwen, Xiaoxiang Wang, Chong Wang, Dongyu Wang, and Qi Li. "Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems." Electronics 8, no. 12 (November 30, 2019): 1430. http://dx.doi.org/10.3390/electronics8121430.

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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes.

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Zhou, Yang, and Rui Xing Chen. "An Improved Dynamic Programming Method for Solving the Problem of Nonlinear Programming." Applied Mechanics and Materials 353-356 (August 2013): 3359–64. http://dx.doi.org/10.4028/www.scientific.net/amm.353-356.3359.

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This paper, which based on the conventional dynamic programming solution , using the method that the decision variables of various stages are fully discrete in their feasible region to solve the optimal target function value under the various state variables. The method can be generic in solving the maximum and minimum objective function value, while avoiding the problem of the different discrete step lengths of the state variables lead to lower the precision of the target value. So, the method will make the solution process of the various stages more specific image, contributing to combining with the practical problems and understanding the connotation of the practical problems (e.g. water resource optimization allocation ).

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Du, Yongwen, Xiquan Zhang, Wenxian Zhang, and Zhangmin Wang. "Whale Optimization Algorithm with Applications to Power Allocation in Interference Networks." Information Technology and Control 50, no. 2 (June 17, 2021): 390–405. http://dx.doi.org/10.5755/j01.itc.50.2.28210.

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Power allocation plays a pivotal role in improving the communication performance of interference-limitedwireless network (IWN). However, the optimization of power allocation is usually formulated as a mixed-integernon-linear programming (MINLP) problem, which is hard to solve. Whale optimization algorithm (WOA)has recently gained the attention of the researcher as an efficient method to solve a variety of optimizationproblems. WOA algorithm also has the disadvantages of low convergence accuracy and easy to fall into local optimum.To solve the above problems, we propose Cosine Compound Whale Optimization Algorithm (CCWOA).First of all, its unique cosine nonlinear convergence factor can balance the rate of the whole optimization processand prevent the convergence speed from being too fast. Secondly, the inertia weight and sine vector canincrease the probability of jumping out of the local optimal solution. Finally, the Archimedean spiral can reducethe risk of losing the optimal solution. A representative benchmark function is selected to test the convergencerate of CCWOA algorithm and the optimization performance of jumping out of local optimum. Compared withthe representative algorithms PFP and GAP, the optimization effect of CCWOA is almost consistent with theabove two algorithms, and even exceeds 4% - 6% in numerical value. The advantage of CCWOA is that it haslower algorithm complexity, which has a good advantage when the network computing resources are fixed. Inaddition, the optimization effect of CCWOA is higher than that of WOA, which lays a good foundation for furtherapplication of swarm intelligence optimization algorithm in network resource allocation.

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Pham, Xuan-Qui, Tien-Dung Nguyen, VanDung Nguyen, and Eui-Nam Huh. "Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing." Symmetry 11, no. 1 (January 7, 2019): 58. http://dx.doi.org/10.3390/sym11010058.

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The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

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Hu, Wenfa, and Xinhua He. "An Innovative Time-Cost-Quality Tradeoff Modeling of Building Construction Project Based on Resource Allocation." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/673248.

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The time, quality, and cost are three important but contradictive objectives in a building construction project. It is a tough challenge for project managers to optimize them since they are different parameters. This paper presents a time-cost-quality optimization model that enables managers to optimize multiobjectives. The model is from the project breakdown structure method where task resources in a construction project are divided into a series of activities and further into construction labors, materials, equipment, and administration. The resources utilized in a construction activity would eventually determine its construction time, cost, and quality, and a complex time-cost-quality trade-off model is finally generated based on correlations between construction activities. A genetic algorithm tool is applied in the model to solve the comprehensive nonlinear time-cost-quality problems. Building of a three-storey house is an example to illustrate the implementation of the model, demonstrate its advantages in optimizing trade-off of construction time, cost, and quality, and help make a winning decision in construction practices. The computational time-cost-quality curves in visual graphics from the case study prove traditional cost-time assumptions reasonable and also prove this time-cost-quality trade-off model sophisticated.

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Feng, Yizhi, and Yan Cao. "Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model." Sensors 22, no. 8 (April 15, 2022): 3041. http://dx.doi.org/10.3390/s22083041.

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In this paper, the maximization of the achievable information rate is proposed for the multi-relay amplify-and-forward cooperative simultaneous wireless information and power transfer communication systems, where the nonlinear characteristic of the energy harvesting (EH) circuits is taken into account for the receivers of the relay nodes. The time switching (TS) and power splitting (PS) schemes are considered for the EH receivers and the achievable rate maximization problems are formulated as convex and non-convex optimization problems, respectively. The optimal TS and PS ratios for the relay nodes along with the maximum achievable rates for the system are obtained, respectively, by solving the optimal problems with efficient algorithms. The asymptotic maximum achievable rates at low and high input signal-to-noise ratios (SNRs) for both the PS and TS schemes are also analyzed. It is demonstrated that the PS scheme is more susceptible to the variation of the relays’ location and the channel parameters than TS scheme, whereas the TS scheme is more susceptible to the mismatch of the resource allocation than PS scheme. Specifically, compared to the linear EH model, the nonlinear EH model achieves significant performance gain for the TS scheme, whereas inconspicuous performance improvement is achieved for the PS scheme.

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Lin, Zefang, Hui Song, and Daru Pan. "A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network." Sensors 19, no. 21 (November 4, 2019): 4799. http://dx.doi.org/10.3390/s19214799.

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Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We formulate the optimization problem which is difficult to resolve directly because of its non-convex nature. Then a novel maximum weighted ergodic energy efficiency (MWEEE) algorithm is proposed to solve the formulated optimization problem which consists of two sub-problems: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) sub-problem which can be solved by obtaining the weighted allocation matrix. In particular, we shed light into the impact on EE metric of D2D communication by revealing the nonlinear power relationship between CUE and DUE and taking the QoS of CUEs into account. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms.

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Wen, Jing, Wen Ying Liu, and Chang Xie. "A Optimal Scheduling Method Based on Source and Load Interactive for Power System with Large-Scale Wind Power Integrated." Advanced Materials Research 953-954 (June 2014): 389–94. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.389.

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The random fluctuation and anti-peaking characteristics of wind power has brought new problems for the power system optimal dispatch. Based on the interaction characteristic of the load, this paper played the utility of interactive load which can help system consumers the positive and negative fluctuations of wind power, and considered interactive load as a scheduling resource into the traditional day-ahead scheduling model. Taking into account the effects of interactive load on system operating costs and power flow distribution, this paper established a generation scheduling model which the aim are both the system operating costs and network loss minimization in large-capacity wind power integrated system, and reformulated the multi-objective optimization problem into a single objective nonlinear programming problem by means of the fuzzy theory, and made the generation side and the demand side of the power grid can participate optimal allocation of resources, and provided a new ideas and methods for achieving source and load interactive in smart grid environment. The simulation on IEEE 30-bus system indicated this method can reduce system operating costs and network losses effectively, and improve wind power consumptive level as well as too.

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Saeidian, B., M. Saadi Mesgari, and M. Ghodousi. "OPTIMUM ALLOCATION OF WATER TO THE CULTIVATION FARMS USING GENETIC ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 631–38. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-631-2015.

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The water scarcity crises in the world and specifically in Iran, requires the proper management of this valuable resource. According to the official reports, around 90 percent of the water in Iran is used for agriculture. Therefore, the adequate management and usage of water in this section can help significantly to overcome the above crises. The most important aspect of agricultural water management is related to the irrigation planning, which is basically an allocation problem. The proper allocation of water to the farms is not a simple and trivial problem, because of the limited amount of available water, the effect of different parameters, nonlinear characteristics of the objective function, and the wideness of the solution space. Usually To solve such complex problems, a meta-heuristic method such as genetic algorithm could be a good candidate. In this paper, Genetic Algorithm (GA) is used for the allocation of different amount of water to a number of farms. In this model, the amount of water transferable using canals of level one, in one period of irrigation is specified. In addition, the amount of water required by each farm is calculated using crop type, stage of crop development, and other parameters. Using these, the water production function of each farm is determined. Then, using the water production function, farm areas, and the revenue and cost of each crop type, the objective function is calculated. This objective function is used by GA for the allocation of water to the farms. The objective function is defined such that the economical profit extracted from all farms is maximized. Moreover, the limitation related to the amount of available water is considered as a constraint. In general, the total amount of allocated water should be less than the finally available water (the water transferred trough the level one canals). Because of the intensive scarcity of water, the deficit irrigation method are considered. In this method, the planning is on the basis of the optimum and limited allocation of water, and not on the basis of the each crop water requirement. According to the available literature, in the condition of water scarcity, the implementation of deficit irrigation strategy results in higher economical income. The main difference of this research with others is the allocation of water to the farms. Whilst, most of similar researches concentrate on the allocation of water to different water consumption sections (such as agriculture, industry etc.), networks and crops. Using the GA for the optimization of the water allocation, proper solutions were generated that maximize the total economical income in the entire study area. In addition, although the search space was considerably wide, the results of the implementation showed an adequate convergence speed. The repeatability test of the algorithm also proved that the algorithm is reasonably stable. In general the usage of GA algorithm can be considered as an efficient and trustable method for such irrigation planning problems. By optimum allocation of the water to the farms with different areas and crop types, and considering the deficit irrigation method, the general income of the entire area can be improved substantially.

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Kodialam, Muralidharan S., and Hanan Luss. "Algorithms for Separable Nonlinear Resource Allocation Problems." Operations Research 46, no. 2 (April 1998): 272–84. http://dx.doi.org/10.1287/opre.46.2.272.

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Luss, Hanan. "Minimax resource allocation problems: Optimization and parametric analysis." European Journal of Operational Research 60, no. 1 (July 1992): 76–86. http://dx.doi.org/10.1016/0377-2217(92)90335-7.

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Granmo, Ole-Christoffer, and B. John Oommen. "Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata." IEEE Transactions on Computers 59, no. 4 (April 2010): 545–60. http://dx.doi.org/10.1109/tc.2009.189.

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Yin, Peng-Yeng, and Jing-Yu Wang. "Ant colony optimization for the nonlinear resource allocation problem." Applied Mathematics and Computation 174, no. 2 (March 2006): 1438–53. http://dx.doi.org/10.1016/j.amc.2005.05.042.

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Chekanin, Vladislav A., and Alexander V. Chekanin. "Object-Oriented Class Library for Resource Allocation Problems." Applied Mechanics and Materials 799-800 (October 2015): 1149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.1149.

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The object-oriented class library designed for solving various optimization problems of resource allocation, including problems of cutting materials and any dimensional packing problems, is described in this paper. The class library enables obtaining of suboptimal solutions of NP-completed resource allocation problems using standard evolutionary and modified heuristic optimization algorithms. The developed class library can be used in creation of an applied software for a wide class of optimization problems, including problems of resource allocation in storage systems and logistics, problems of cutting materials on machine tools with numerical control, scheduling problems and a large set of other practical problems.

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Wang, Xue-Fang, Yiguang Hong, Xi-Ming Sun, and Kun-Zhi Liu. "Distributed Optimization for Resource Allocation Problems Under Large Delays." IEEE Transactions on Industrial Electronics 66, no. 12 (December 2019): 9448–57. http://dx.doi.org/10.1109/tie.2019.2891406.

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Yazidi, Anis, and Hugo L. Hammer. "Solving stochastic nonlinear resource allocation problems using continuous learning automata." Applied Intelligence 48, no. 11 (June 23, 2018): 4392–411. http://dx.doi.org/10.1007/s10489-018-1201-7.

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Wen, Jing Hua, He Ling Jiang, Mei Zhang, and Xi Yu. "Application of Dynamic Programming in Resources Optimization Allocation of Factory Production Line." Key Engineering Materials 474-476 (April 2011): 1632–37. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1632.

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The dynamic programming has significant implications for solving multi-stage decision of resource allocation problems. By inducting phase, state of variables and decision, the factory assembly line resource allocation problems was taken as a multi-stage decision process. The stage of resource allocation was divided in reason, and the dynamic programming equation was built with “Top-Down” ways to reverse recursion according to the dynamic programming principle and methods. Adopting the MATLAB7.0 as development platform, it was convenient for calculating optimal decision sequence and maximum total profits. Dynamic programming method has obvious effect in equipment resource allocation output issues, and brings in the biggest economic benefits in limited investment.

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Yin, Peng-Yeng, and Jing-Yu Wang. "A particle swarm optimization approach to the nonlinear resource allocation problem." Applied Mathematics and Computation 183, no. 1 (December 2006): 232–42. http://dx.doi.org/10.1016/j.amc.2006.05.051.

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Shamshirband, Shahab, Javad Hassannataj Joloudari, Sahar Khanjani Shirkharkolaie, Sanaz Mojrian, Fatemeh Rahmani, Seyedakbar Mostafavi, and Zulkefli Mansor. "Game theory and evolutionary optimization approaches applied to resource allocation problems in computing environments: A survey." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9190–232. http://dx.doi.org/10.3934/mbe.2021453.

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<abstract> Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they cannot develop and adaptively manage the conditions optimally. To optimize the resource allocation with minimal delay, low energy consumption, minimum computational complexity, high scalability, and better resource utilization efficiency, CC/FC/EC/IoT-based computing architectures should be designed intelligently. Therefore, the objective of this research is a comprehensive survey on resource allocation problems using computational intelligence-based evolutionary optimization and mathematical game theory approaches in different computing environments according to the latest scientific research achievements. </abstract>

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Thach, P. T., and T. V. Thang. "Problems with resource allocation constraints and optimization over the efficient set." Journal of Global Optimization 58, no. 3 (March 30, 2013): 481–95. http://dx.doi.org/10.1007/s10898-013-0055-0.

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Klein, Rachelle S., Hanan Luss, and Uriel G. Rothblum. "Relaxation-based algorithms for minimax optimization problems with resource allocation applications." Mathematical Programming 64, no. 1-3 (March 1994): 337–63. http://dx.doi.org/10.1007/bf01582580.

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Miao, Bin. "Preliminary Study about Optimal Allocation of Human Resources Management." Advanced Materials Research 268-270 (July 2011): 1913–16. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1913.

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In today's world, the competition between enterprises in the final analysis is talent competition and giving full play to the role of talents of enterprise cannot leave the optimal allocation of human resources. Along with the increasing open of global economy, our state-owned enterprises face the more and more difficult challenge, but also have many opportunities, which will encourage enterprises to renew ideas, adjust human resource structure, strengthen the cultivation of talents, learn and introduce advanced hr optimization technology and method. In Nanyang mobile company, for example, on the basis of expounding the connotation and principle of human resources optimization allocation, the paper summarizes and analyzes the human resource configuration optimization problems and also puts forward some proposals for resolving these problems using human resource configuration optimization theory. By means of human resource configuration optimization, it can realize company efficient, orderly and long-term development, and provides some suggestions about hr optimization for other companies.

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DENG, JIANPING, N. SUNDARARAJAN, and P. SARATCHANDRAN. "COMPLEX-VALUED MINIMAL RESOURCE ALLOCATION NETWORK FOR NONLINEAR SIGNAL PROCESSING." International Journal of Neural Systems 10, no. 02 (April 2000): 95–106. http://dx.doi.org/10.1142/s0129065700000090.

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This paper presents a sequential learning algorithm and evaluates its performance on complex valued signal processing problems. The algorithm is referred to as Complex Minimal Resource Allocation Network (CMRAN) algorithm and it is an extension of the MRAN algorithm originally developed for online learning in real valued RBF networks. CMRAN has the ability to grow and prune the (complex) RBF network's hidden neurons to ensure a parsimonious network structure. The performance of the learning algorithm is illustrated using two applications from signal processing of communication systems. The first application considers identification of a nonlinear complex channel. The second application considers the use of CMRAN to QAM digital channel equalization problems. Simulation results presented clearly show that CMRAN is very effective in modeling and equalization with performance achieved often being superior to that of some of the well known methods.

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Okamura, Hiroyuki, and Tadashi Dohi. "Optimizing Testing-Resource Allocation Using Architecture-Based Software Reliability Model." Journal of Optimization 2018 (September 27, 2018): 1–7. http://dx.doi.org/10.1155/2018/6948656.

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In the management of software testing, testing-recourse allocation is one of the most important problems due to the tradeoff between development cost and reliability of released software. This paper presents the model-based approach to design the testing-resource allocation. In particular, we employ the architecture-based software reliability model with operational profile to estimate the quantitative software reliability in operation phase and formulate the multiobjective optimization problems with respect to cost, testing effort, and software reliability. In numerical experiment, we investigate the difference of the presented optimization problem from the existing testing-resource allocation model.

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Yingjie, Xu. "Application of BP Neural Network to Optimize the Allocation of Art Teaching Resources." Tobacco Regulatory Science 7, no. 5 (September 30, 2021): 4122–32. http://dx.doi.org/10.18001/trs.7.5.1.188.

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Reasonable allocation of art teaching resources can improve the management efficiency of art teaching resources. There is a large delay in the allocation of art teaching resources, which leads to the long occupation time of network resource allocation channel. The traditional method of network experiment resource allocation is to assign resource tasks for different channels to complete the resource allocation. When the network resource allocation channel occupies a long time, the allocation efficiency is reduced. This paper proposes an optimal allocation method of art teaching resources based on multi rate cognition. From the point of view that there are a pair of primary users and a pair of secondary users in the network, this method constructs a resource allocation delay model, obtains the resource allocation delay under different modes, and dynamically adjusts the transmission rate on the allocation resource block. The art teaching resource allocation scheduling problem is modeled as a nonlinear optimization problem, and the constraints of the optimization problem are given, which are integrated into greedy computing. The global optimal solution of the problem is carried out by using the method, and the allocation of art teaching resources is completed. Simulation results show that the proposed algorithm greatly improves the efficiency and effect of teaching network resource allocation.

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Konnov, I. V. "Selective bi-coordinate variations for resource allocation type problems." Computational Optimization and Applications 64, no. 3 (February 2, 2016): 821–42. http://dx.doi.org/10.1007/s10589-016-9824-2.

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Wang, Ling Yan, and Ai Min Liu. "The Study on Cloud Computing Resource Allocation Method." Applied Mechanics and Materials 198-199 (September 2012): 1506–13. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1506.

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Resource allocation and scheduling problems in the field of cloud computing can be classified into two major groups. The first one is in the area of MapReduce task scheduling. The default scheduler is the FIFO one. Two other schedulers that are available as plug-in for Hadoop: Fair scheduler and Capacity scheduler. We presented recent research in this area to enhance performance or to better suit a specific application. MapReduce scheduling research involves introducing alternative schedulers, or proposing enhancements for existing schedulers such as streaming and input format specification. The second problem is the provisioning of virtual machines and processes to the physical machines and its different resources. We presented the major cloud hypervisors available today. We described the different methods used to solve the resource allocation problem including optimization, simulation, distributed multi-agent systems and SoA. Finally, we presented the related topic of connecting clouds which uses similar resource provisioning methods. The above two scheduling problems are often mixed up, yet they are related. For example, MapReduce benchmarks can be used to evaluate VM provisioning methods. Enhancing the solution to one problem can affect the other. Similar methods can be used in solving both problems, such as optimization methods. Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing.

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Long, Yan, and Hongshan Zhao. "Marketing Resource Allocation Strategy Optimization Based on Dynamic Game Model." Journal of Mathematics 2022 (January 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/4370298.

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Game theory has become an important tool to study the competition between oligopolistic enterprises. After combing the existing literature, it is found that there is no research combining two-stage game and nonlinear dynamics to analyze the competition between enterprises for advertising. Therefore, this paper establishes a two-stage game model to discuss the effect of the degree of firms’ advertising input on their profits. And the complexity of the system is analyzed using nonlinear dynamics. This paper analyzes and studies the dynamic game for two types of application network models: data transmission model and transportation network model. Under the time-gap ALOHA protocol, the noncooperative behavior of the insiders in the dynamic data transmission stochastic game is examined as well as the cooperative behavior. In this paper, the existence of Nash equilibrium and its solution algorithm are proved in the noncooperative case, and the “subgame consistency” of the cooperative solution (Shapley value) is discussed in the cooperative case, and the cooperative solution satisfying the subgame consistency is obtained by constructing the “allocation compensation procedure.” The cooperative solution is obtained by constructing the “allocation compensation procedure” to satisfy the subgame consistency. In this paper, we propose to classify the packets transmitted by the source nodes, and by changing the strategy of the source nodes at the states with different kinds of packets, we find that the equilibrium payment of the insider increases in the noncooperative game with the addition of the “wait” strategy. In the transportation dynamic network model, the problem of passenger flow distribution and the selection of service parameters of transportation companies are also studied, and a two-stage game theoretical model is proposed to solve the equilibrium price and optimal parameters under Wardrop’s criterion.

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Chen, Chao, Changjun Fan, and Xingxing Liang. "A Multiobjective Resource Allocation Algorithm for Robust Project Scheduling." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7701–4. http://dx.doi.org/10.1166/jctn.2016.4426.

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Resource allocation is an important procedure which involves allocating finite resources to the activities of a given baseline schedule. Based on the conception of Pareto Optimization, a multiobjective optimization approach for the resource allocation problem is proposed in this paper. The problem is first described. Then the detailed procedure of the proposed algorithm is given. Finally, an extensive computational results obtained on a set of benchmark problems are reported.

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Liu, Shu Shun, and Wei Tong Chen. "Construction Multi-Project Scheduling Model Considering Different Resource Allocation Behavior." Applied Mechanics and Materials 174-177 (May 2012): 2815–19. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.2815.

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According to previous researches, an investigation through small to mid-size construction contractors showed that 84% of construction contractors execute their projects in a multi-project environment. In a multi-project environment, scheduling problems with resource constraints are much more complicated than those in a single project. One of the most important factors that influence multi-project scheduling problems is resource allocation policy, depending on the types of resources, which can be defined by the way of resource acquisition and sharing behavior. This paper discusses resource allocation mechanism for construction multi-project scheduling issues, and then presents an optimization-based model to resolve resource allocation problems. This research developed a CP-based (Constraint Programming) model, which is capable of handling different optimization objectives such as minimizing total cost, overall project duration, subject to resource assignment combinations for each activity. Based on research results, the influence of different types of resource quantity on multi-project duration is discussed. Moreover, resource competitive behavior among all projects is recognized. It concludes that the effective increment of critical resources can reduce overall project duration. The major goal of this research is to find the relation among duration-cost-resource in a multi-project environment, and provide systematic information for construction parties when making resource allocation decisions.

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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 (September 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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Liu, Jie, and Li Zhu. "Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing." Complexity 2021 (March 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5556651.

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Resource allocation has always been a key technology in wireless sensor networks (WSN), but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm (PCOA, MCMH, and TDMA), it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes.

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Lin, Carrie Ka Yuk, Teresa Wai Ching Ling, and Wing Kwan Yeung. "Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization." Journal of Healthcare Engineering 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/9034737.

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This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

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Nath Roy, Sudipendra, and Tuhin Sengupta. "Indian PharmaChem: a resource allocation peccadillo." Emerald Emerging Markets Case Studies 8, no. 1 (February 6, 2018): 1–15. http://dx.doi.org/10.1108/eemcs-01-2017-0009.

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Subject area Operations Management Study level/applicability MBA/Post Graduate Case overview This case attempts to highlight a very common resource allocation dilemma in a real-life scenario. The majority of today’s problems are solved by the methodology of trial and error. This case shows how a generic trial-and-error solution, if buttressed by a proper quantitative methodology, can have substantial impact on the bottom-line of an organization. The case concentrates on three disparate focus areas in a didactic fashion, namely, the ability to retrieve raw data and convert it into a utilizable form if a quantitative method is to be applied; the ability to comprehend the resource constraints of a typical real-life situation; and the skill required to develop and solve an optimization problem in Excel Solver, a product which can easily be accessed by any practitioner. Expected learning outcomes Expected learning outcomes are as follows: students learn to formulate a Mixed-Integer programming model; to interpret optimal solutions and appreciate the application of “Optimization”; to recommend a resource allocation strategy; and to understand the importance of cost minimization in organizations. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS: 9: Operations and Logistics

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Li, Qian, Sha Tao, Heap-Yih Chong, and Zhijie Sasha Dong. "Robust Optimization for Integrated Construction Scheduling and Multiscale Resource Allocation." Complexity 2018 (July 24, 2018): 1–17. http://dx.doi.org/10.1155/2018/2697985.

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This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi-time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data-Driven Construction Scheduling and Multiscale Resource Allocation Problem (DD-CS&MRAP). Based on likelihood robust optimization, a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double-layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization (MOPSO) and interior point method named MOPSO-interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO-interior point algorithm can reduce resource cost and improve the efficiency of resource utilization.

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Mao, Li, De Yu Qi, Wei Wei Lin, Bo Liu, and Ye Da Li. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization." International Journal of Grid and High Performance Computing 8, no. 2 (April 2016): 43–57. http://dx.doi.org/10.4018/ijghpc.2016040103.

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With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

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Dolgov, D. A., and E. H. Durfee. "Resource Allocation Among Agents with MDP-Induced Preferences." Journal of Artificial Intelligence Research 27 (December 26, 2006): 505–49. http://dx.doi.org/10.1613/jair.2102.

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Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size.

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Zhang, Liqun, and Weibo Yang. "Simulation of Enterprise Human Resource Scheduling Algorithm Optimization in the Context of Smart City." Complexity 2020 (November 19, 2020): 1–10. http://dx.doi.org/10.1155/2020/8830335.

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In this paper, a new human resource scheduling algorithm is proposed based on the optimization simulation of the human resource scheduling algorithm to find the most suitable human resource allocation scheme for different regions. A simulation system for human resource allocation is proposed, which integrates the scheduling algorithm of this paper and conducts simulation experiments using the historical data of enterprise problems in each region collected in a smart city. The simulation experiment proves that the dispatching algorithm in this paper is more reasonable than the current dispatching algorithm, and the relationship between enterprise problems and the number of employees is also found, and finally, the simulation system in this paper is proved to be stable through large-scale simulation experiment.

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Zhang, Xuecong, Haolang Shen, and Zhihan Lv. "Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing." PLOS ONE 16, no. 6 (June 4, 2021): e0252244. http://dx.doi.org/10.1371/journal.pone.0252244.

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The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user’s task execution needs. A comparison with the latest algorithm also verifies the model’s performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.

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Hsieh, Yi-Chih. "A two-phase linear programming approach for redundancy allocation problems." Yugoslav Journal of Operations Research 12, no. 2 (2002): 227–36. http://dx.doi.org/10.2298/yjor0202227h.

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Provision of redundant components in parallel is an efficient way to increase the system reliability, however, the weight, volume and cost of the system will increase simultaneously. This paper proposes a new two-phase linear programming approach for solving the nonlinear redundancy allocation problems subject to multiple linear constraints. The first phase is used to approximately allocate the resource by using a general linear programming, while the second phase is used to re-allocate the slacks of resource by using a 0-1 integer linear programming. Numerical results demonstrate the effectiveness and efficiency of the proposed approach.

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Hu, Ji Wen. "The Improvement on Minimum Error Rate Optimization Criterion Dynamic Resource Allocation Algorithm." Advanced Materials Research 1079-1080 (December 2014): 799–801. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.799.

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Fischeris a classic dynamic resource allocation algorithm, and the optimizationcriterion is to minimize the system error rate base on a constant bit rate andsystem power. However, the Fischer algorithm is still have problems with highcomplexity, and it require long time to calculate, all these problems making itdifficult to use in an actual system. This paper is an improvement on Fischeralgorithm, the improved algorithm will keep the same error rate, reducing thecomplexity, shortening the arithmetic operation time and increasing thepracticality of the algorithm.

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Sidiropoulos, Epaminondas. "Spatial resource allocation via extremal optimization enhanced by cell-based local search." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 02 (May 29, 2015): 1550020. http://dx.doi.org/10.1142/s1793962315500208.

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A new treatment is presented for land use planning problems by means of extremal optimization (EO) in conjunction to cell-based neighborhood local search. EO, inspired by self-organized critical models of evolution has been applied mainly to the solution of classical combinatorial optimization problems. Cell-based local search has been employed by the author elsewhere in problems of spatial resource allocation in combination with genetic algorithms and simulated annealing. In this paper, it complements EO in order to enhance its capacity for a spatial optimization problem. The hybrid method thus formed is compared to methods of the literature on a specific resource allocation problem by taking into account both the development and the transportation cost. It yields better results both in terms of objective function values and in terms of compactness. The latter is an important quantity for spatial planning and its meaning is discussed. The appearance of significant compactness values as emergent results is investigated.

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De Waegenaere, Anja, and Jacco L. Wielhouwer. "A breakpoint search approach for convex resource allocation problems with bounded variables." Optimization Letters 6, no. 4 (February 13, 2011): 629–40. http://dx.doi.org/10.1007/s11590-011-0288-0.

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Müller, Stefan, Georg Regensburger, and Ralf Steuer. "Resource allocation in metabolic networks: kinetic optimization and approximations by FBA." Biochemical Society Transactions 43, no. 6 (November 27, 2015): 1195–200. http://dx.doi.org/10.1042/bst20150156.

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Based on recent theoretical results on optimal flux distributions in kinetic metabolic networks, we explore the congruences and differences between solutions of kinetic optimization problems and results obtained by constraint-based methods. We demonstrate that, for a certain resource allocation problem, kinetic optimization and standard flux balance analysis (FBA) give rise to qualitatively different results. Furthermore, we introduce a variant of FBA, called satFBA, whose predictions are in qualitative agreement with kinetic optimization.

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Geng, Jin-qiang, Li-ping Weng, and Si-hong Liu. "An improved ant colony optimization algorithm for nonlinear resource-leveling problems." Computers & Mathematics with Applications 61, no. 8 (April 2011): 2300–2305. http://dx.doi.org/10.1016/j.camwa.2010.09.058.

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Zheng, Maokuan, Xinguo Ming, and Guoming Li. "Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/2839125.

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The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2). The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR) is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.

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Journal articles on the topic 'Optimization nonlinear resource allocation problems'

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