Stochastic Resource Allocation Research Articles

Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased.

Toward Green Metaverse Networking: Technologies, Advancements, and Future Directions

As the Metaverse is iteratively being defined, its potential to unleash the next wave of digital disruption and create real-life value becomes increasingly clear. With distinctive features of immersive experience, simultaneous interactivity, and user agency, the Metaverse has the capability to transform all walks of life. However, the enabling technologies of the Metaverse, i.e., digital twin, artificial intelligence, blockchain, and extended reality, are known to be energy-hungry, therefore raising concerns about the sustainability of its large-scale deployment and development. This article proposes Green Metaverse Networking for the first time to optimize energy efficiencies of all network components for Metaverse sustainable development. We first analyze energy consumption, efficiency, and sustainability of energy-intensive technologies in the Metaverse. Next, focusing on computation and networking, we present major advancements related to energy efficiency and their integration into the Metaverse. A case study of energy conservation by incorporating semantic communication and stochastic resource allocation in the Metaverse is presented. Finally, we outline the critical challenges of Metaverse sustainable development, thereby indicating potential directions of future research towards the green Metaverse.

Combining optimisation and simulation using logic-based Benders decomposition

Operations Research practitioners often want to model complicated functions that are difficult to encode in their underlying optimisation framework. A common approach is to solve an approximate model, and then use a simulation to evaluate the true objective value of one or more solutions. We propose a new approach to integrating simulation into the optimisation model itself. The idea is to run the simulation at each incumbent solution to a master problem. The simulation results are then used to guide the trajectory of the optimisation model itself using logic-based Benders cuts. We test the approach on a class of stochastic resource allocation problems with monotonic performance measures. We derive strong novel Benders cuts that are provably valid for all problems of the given form. We consider two concrete examples: a nursing home shift scheduling problem, and an airport check in counter allocation problem. While previous papers on these applications could only approximately solve realistic instances, we are able to solve them exactly within a reasonable amount of time. Moreover, while those papers account for the inherent variance of the problem by including estimates of the underlying random variables as model parameters, we are able to compute sample-average approximations to optimality with up to 100 scenarios.

Efficient optimization techniques for resource allocation in UAVs mission framework.

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of 'n' candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid.

Inverse-GMM: A Latency Distribution Shaping Method for Industrial Cooperative Deep Learning Systems

The front deployed deep learning is a promising technology of the next generation industrial applications, which can extract essential information from high dimension sensors. However, part of these heavy computation tasks at resource constrained front devices have to be offloaded to the edge or cloud devices, which forms the cooperative deep learning system through the exchange of intermediate data. The inference efficiency of cooperative deep learning system will then be highly correlated with the communication latency caused by the non-stationary industrial multipath-rich fading channel. This paper proposes a novel method to control the distribution of communications latency, which is able to support efficient cooperative deep learning architecture in the harsh industrial environment. The proposed method is essentially an inverse process of Gaussian Mixture Model (GMM), which adjusts latency samples to approach the given arbitrary shape function. To achieve this objective, a new variation of Expectation-Maximization (EM) algorithm in analytical domain is derived to decompose arbitrary distribution shape with multiple Gaussian kernels and an optimized stochastic resource allocation algorithm is proposed to approximate each Gaussian kernels. The performance of proposed method is verified by both classical Rician channel model and field measured industrial fading channel responses.

Stochastic Resource Algorithm for Effective Utilization of Bandwidth for IoT Sensor Networks in Arecanut Agriculture Applications

Cognitive radio technology is growing positive solution for the effective utilization of the bandwidth for IoT device networks. The fixed spectrum allocation scheme leads to insufficient spectrum utilization, to overcome this problem, the stochastic resource allocation algorithm is proposed in the present work. This method of internet spectrum allocation considerably increases the performance of IoT sensor network by utilizing the internet spectrum by non-cognitive inactive IoT sensors (white space/spectrum holes) by continuous spectrum sensing. In this paper we have discussed the effective resource allocation for interweave and underlay conditions and also at variable interference constraints (short time and longtime spectrum sensing) of IoT network. The arecanut plantation for agriculture scenario is considered in the present paper and the sensor environment simulation is performed using MATLAB.

Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50\% compared to state-of-the-art schemes.

Secure Multicast Energy-Efficiency Maximization With Massive RISs and Uncertain CSI: First-Order Algorithms and Convergence Analysis

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the eavesdroppers’ channel state information is imperfect at the base station. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for massive antennas and reflecting elements secure transmission are challenging to solve due to the outage probabilistic constraint with coupled variables. To fill this gap, this paper formulates a problem of energy-efficient secure transmission design with the probabilistic outage constraint. By leveraging the exponential distribution property of the received signal power, the stochastic resource allocation is equivalently transformed into a deterministic one, and the secure energy efficiency maximization problem can be iteratively solved via low complexity first-order algorithms under the alternating maximization (AM) framework. However, due to the nonsmooth problem, the convergence of the objective function value and nature of the converged solution under AM iteration are uncertain. Therefore, the convergence properties with respect to the objective function value and sequence of solutions are further established. Simulation results corroborate the convergence results of the first-order algorithms and show that the proposed algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant RIS aided secure transmission significantly improves the energy efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Power Grid Resilience Enhancement via Protecting Electrical Substations Against Flood Hazards: A Stochastic Framework

Natural disasters, such as floods, may damage power system assets and lead to widespread and long outages. The impact of flood can be alleviated by preventive actions such as installing tiger dams around power substations before the flood. In this regard, it is imperative that critical substations are identified in terms of the connected load and imposed costs to the system. This article presents a stochastic resource allocation approach for protecting power substations against flood events a day ahead of the event. Flood probability distribution functions are used to generate several flood scenarios at each substation. Using flood scenarios and substations’ fragility, damage, and repair time curves obtained from historical data, the failure probability, damage percentage, damage cost, and repair time of substations are estimated. A day-ahead risk-aware stochastic scheduling model is proposed to identify the critical substations whose protection by tiger dams maximizes grid resilience. The risk-aware approach prevents high cost and low resilience if a particular scenario with a low probability is realized. A scenario reduction method is developed to generate representative substation failure scenarios and reduce the computational cost of the optimization problem. The simulation results on a realistic 30-substation system show the effectiveness of the proposed model.

The Risk-Averse Static Stochastic Knapsack Problem

This research proposes and analyzes new models for a stochastic resource allocation problem that arises in a variety of operations contexts. One of the primary contributions of the paper lies in providing a succinct, robust, and general model that can address a range of different risk-based objectives and cost assumptions under uncertainty. Although the model expression is relatively simple, it embeds a reasonably high degree of underlying complexity, as the analysis shows. In addition, in-depth analysis of the model, both in its general form and under various specific risk measures, uncovers some interesting and powerful insights regarding the problem trade-offs. Furthermore, this analysis leads to a highly efficient class of heuristic algorithms for solving the problem, which we demonstrate via numerical experimentation to provide close-to-optimal solutions. This computational benefit is a critical element for solving a class of broadly applicable larger problems for which our problem arises as a subproblem that requires repeated solution.

An Efficient Elite-Based Simulation–Optimization Approach for Stochastic Resource Allocation Problems in Manufacturing and Service Systems

Stochastic resource allocation problems (SRAPs) involve determining the optimal configuration of a limited resource to achieve an objective function under given constraints and random effects in manufacturing systems (MSs) and service systems (SSs). The problems are traditionally solved by determining the optimal solution. It is generally preferable to determine as many global optima as possible, or at least a small set of diverse but good candidates, to help the decision-maker rapidly adopt alternative solutions from the set if one solution is unsuitable. However, many local or global optima occur in SRAPs in MSs and SSs due to the interaction between random system factors, such as processing time uncertainty and machine failure rates. Thus, enhancing the searching efficiency of algorithms for SRAPs is a challenge. This study proposes an efficient simulation–optimization approach, called elite-based particle swarm optimization (EPSO), using an optimal replication allocation strategy (ORAS) (i.e., EPSO[Formula: see text], to address three types of SRAPs from the literature. Three simulation models were constructed to evaluate the system performance under random factors. We developed a novel EPSO to explore and exploit the solution space. We created an elite group (EG) that includes multiple solutions, and each solution of the EG has a statistically nonsignificant difference from the current optimal solution. The new feature of EPSO updates the velocity and position of the particles in the design space based on multiple global optima from the EG to enhance diversity and prevent premature convergence. We propose an ORAS to allocate a limited number of replications to each solution. Three numerical experiments were performed to verify the effectiveness and efficiency of EPSO[Formula: see text] compared with other simulation–optimization approaches, namely particle swarm optimization (PSO) and the genetic algorithm (GA) with both optimal computing budget allocation (OCBA) and the ORAS. The experimental results reveal that the solution quality of EPSO improved compared with that of PSO and GA, and the ORAS provides a more efficient allocation of the number of replications compared with the OCBA in the three experiments. Finally, the proposed approach also provides an elite set at the end of the algorithm, instead of a single optimal solution, to support decision-making.

Resource Allocation for Wireless Cooperative IoT Network With Energy Harvesting

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT nodes, taking into consideration the bounded transmit power of IoT nodes, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

Approximate dynamic programming for stochastic resource allocation problems

A stochastic resource allocation model, based on the principles of Markov decision processes &#x0028 MDPs &#x0029 , is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations &#x0028 i.e., specified and unspecified time interval reservation requests &#x0029 , and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming &#x0028 DP &#x0029 based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations, occurs. In particular, an approximate dynamic programming &#x0028 ADP &#x0029 technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.

Robust and Adaptive Anticoagulant Control

Summary We consider a control theory approach to adaptive dose allocation of anticoagulants, based on an analysis of records of 152 patients on long-term warfarin treatment. We consider a selection of statistical models for the relationship between the dose of drug and subsequent blood clotting speed, measured through the international normalized ratio. Our main focus is on subsequent use of the model in guiding the choice of the next dose adaptively as patient-specific information accrues. We compare a naive long-term approach with a proportional-integral-plus method, with parameters estimated by either linear quadratic optimization or by stochastic resource allocation. We demonstrate advantages of the control approaches in comparison with a naive approach in simulations and through calculation of robust stability margins for the observed data.

Solving bi-objective uncertain stochastic resource allocation problems by the CVaR-based risk measure and decomposition-based multi-objective evolutionary algorithms

This paper investigates the uncertain stochastic resource allocation problem in which the results of a given allocation of resources are described as probabilities and these probabilities are considered to be uncertain from practical aspects. Here uncertainties are introduced by assuming that these probabilities depend on random parameters which are impacted by various factors. The redundancy allocation problem (RAP) and the multi-stage weapon-target assignment (MWTA) problem are special cases of stochastic resource allocation problems. Bi-objective models for the uncertain RAP and MWTA problem in which the conditional value-at-risk measure is used to control the risk brought by uncertainties are presented in this paper. The bi-objective formulation covers the objectives of minimizing the risk of failure of completing activities and the resulting cost of resources. With the aim of determining referenced Pareto fronts, a linearized formulation and an approximated linear formulation are put forward for RAPs and MWTA problems based on problem-specific characteristics, respectively. Two state-of-the-art decomposition-based multi-objective evolutionary algorithms (i.e., MOEA/D-AWA and DMOEA- $$\varepsilon \hbox {C}$$ ) are used to solve the formulated bi-objective problem. In view of differences between MOEA/D-AWA and DMOEA- $$\varepsilon \hbox {C}$$ , two matching schemes inspired by DMOEA- $$\varepsilon \hbox {C}$$ are proposed and embedded in MOEA/D-AWA. Numerical experiments have been performed on a set of uncertain RAP and MWTA instances. Experimental results demonstrate that DMOEA- $$\varepsilon \hbox {C}$$ outperforms MOEA/D-AWA on the majority of test instances and the superiority of DMOEA- $$\varepsilon \hbox {C}$$ can be ascribed to the $$\varepsilon $$ -constraint framework.

A tradeoff between throughput and energy efficiency for D2D underlaying communication systems

Device-to-device (D2D) underlaying communication systems can improve the system throughput. However, the spectrum reuse will bring the co-channel interference, which will make it difficult to improve both the energy efficiency and the system throughput. In order to balance the energy efficiency and the system throughput, this letter proposes a new resource allocation strategy. First, a mode selection scheme is proposed by delimiting the D2D interference-restricted area. Then a distance-based resource allocation method is proposed to increase the system throughput. Finally, by modifying the objective function and applying the differential evolution algorithm for power optimization, the higher energy efficiency is obtained while the system throughput approaches the maximum value. Simulation results show that compared with the traditional stochastic resource allocation scheme, the proposed scheme can improve the system throughput. Moreover, the proposed algorithm can make the system throughput close to the maxima while obtaining higher energy efficiency.

Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

This paper studies the real-time charging optimization (RTCO) of large-scale electric vehicles (EVs), which is a multistage and multidimensional stochastic resource allocation problem. In order to handle the complex RTCO of large-scale EVs, a multidimensional approximate dynamic programming (ADP-RTCO) is designed for the sequent optimal decision makings. The hierarchy of ADP-RTCO contains two layers. In the upper layer, RTCO of large-scale EVs is formulated as a multidimensional energy storage problem by classifying EVs into several virtual EV clusters (EVCs). Then, temporal difference learning based policy iteration method is used to obtain the value function approximation for each EVC. In the lower layer, priority based reallocation algorithm is employed to obtain the detailed charging power for connected EVs. ADP-RTCO is designed with on-line learning ability to enhance its adaptability and robustness to ambiguous environment. Comprehensive simulation results demonstrate the optimality and robustness of ADP-RTCO in both cost-saving and load-flattening problems. Furthermore, ADP-RTCO can obtain higher-quality solution compared with other algorithms and is applicable for RTCO of large-scale EVs under uncertainties due to its high computation efficiency.

Market-Driven Stochastic Resource Allocation Framework for Wireless Network Virtualization

Wireless network virtualization is emerging as a potential game-changer for fifth-generation wireless networks. Virtualization of network resources (e.g., infrastructure and spectrum) brings several advantages. One key advantage is that various network operators can robustly share their virtualized network resources to extend coverage, increase capacity, and reduce costs. However, inherent features of wireless communications, e.g., the uncertainty in user equipment locations and channel conditions, impose significant challenges on virtualization and sharing of the network resources. In this context, we propose a novel three-layered virtualization framework, based on a matching game model and stochastic resource allocation. Our proposed architecture aims at guaranteeing user satisfaction and maximizing the revenue for operators, with reasonable computational complexity, and affordable network overhead.

Stochastic Resource Allocation for Well Control With Digital Oil Field Infrastructure

To investigate the flux patterns for a digital oil field system, the modeling and analysis need to include waterflood optimization, which is generated to maintain pressure and sweep oil to production. In this paper, a life-cycle well control optimization is integrated with proactive resource allocation to increase computational efficiency of mature fields. Based on a noninvasive and derivative-free approach, the combinatorial model is developed to address geological uncertainty by modifying injection and production rates at individual wells. With the derived model, the joint well control and field development algorithm is proposed based on their influence on the control variables. The possible benefits from the hybrid method and heuristics are clarified. In addition, the Benders decomposition method is adopted considering the computation burden with the optimal number of wells in a distributed manner at the cost of iteration. With the relaxation technique and acceleration technique, the optimal solutions are achieved to strengthen the cuts generated and to find better feasible solutions in less time. Simulation and experimental results are presented to verify the achievements of the proposed methods.

Network Resource Allocation via Stochastic Subgradient Descent: Convergence Rate

This paper considers a general stochastic resource allocation problem that arises widely in wireless networks, cognitive radio, networks, smart-grid communications, and cross-layer design. The problem formulation involves expectations with respect to a collection of random variables with unknown distributions, representing exogenous quantities such as channel gain, user density, or spectrum occupancy. We consider the constant step-size stochastic dual subgradient descent (SDSD) method that has been widely used for online resource allocation in networks. The problem is solved in dual domain, which results in a primal resource allocation subproblem at each time instant. The goal here is to characterize the non-asymptotic behavior of such stochastic resource allocations in an almost sure sense. It is well known that with a step size of $\epsilon $ , SDSD converges to an $\mathcal {O}(\epsilon)$ -sized neighborhood of the optimum. In practice, however, there exists a trade-off between the rate of convergence and the choice of $\epsilon $ . This paper establishes a convergence rate result for the SDSD algorithm that precisely characterizes this trade-off. Toward this end, a novel stochastic bound on the gap between the objective function and the optimum is developed. The asymptotic behavior of the stochastic term is characterized in an almost sure sense, thereby generalizing the existing results for the stochastic subgradient methods. For the stochastic resource allocation problem at hand, the result explicates the rate with which the allocated resources become near-optimal. As an application, the power and user-allocation problem in device-to-device networks is formulated and solved using the SDSD algorithm. Further intuition on the rate results is obtained from the verification of the regularity conditions and accompanying simulation results.