Pre-allocation Strategy Research Articles

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth’s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining the health of the Earth’s ecosystems. LUCC is a dynamic geographical process involving complex spatiotemporal dependencies. Existing LUCC simulation models suffer from insufficient spatiotemporal feature learning, and traditional cellular automaton (CA) models exhibit limitations in neighborhood effects. This study proposes a cellular automaton model based on spatiotemporal feature learning and hotspot area pre-allocation (VST-PCA). The model utilizes the video swin transformer to acquire transformation rules, enabling a more accurate capture of the spatiotemporal dependencies inherent in LUCC. Simultaneously, a pre-allocation strategy is introduced in the CA simulation to address the local constraints of neighborhood effects, thereby enhancing the simulation accuracy. Using the Chongqing metropolitan area as the study area, two traditional CA models and two deep learning-based CA models were constructed to validate the performance of the VST-PCA model. Results indicated that the proposed VST-PCA model achieved Kappa and FOM values of 0.8654 and 0.4534, respectively. Compared to other models, Kappa increased by 0.0322–0.1036, and FOM increased by 0.0513–0.1649. This study provides an accurate and effective method for LUCC simulation, offering valuable insights for future research and land management planning.

Two-stage approximation allocation approach for real-time parking reservations considering stochastic requests and reusable resources

The digital economy has greatly impacted the traditional parking industry. This study focuses on a real-time parking reservation system that allocates available parking slots (supplies) to serve the parking requests (demands) of drivers within a certain area. The challenge lies in dealing with the randomness of requests with varying parking durations and the reusability of parking resources, which have not been well researched. To address this problem, we propose a stochastic optimization model and a data-driven two-stage approximation approach. The first stage generates a pre-allocation scheme as a basis, while the second stage dynamically adjusts the scheme based on observed requests and available parking slots to improve the system’s performance throughout a finite operational horizon. The study has three main contributions. Firstly, this paper proposes a data-driven nonparametric approach based on sample average approximation (SAA) to solve the stochastic optimization model. Secondly, it develops a stratified ranked set sampling (SRSS) method to ensure the quality of the sample set and proves that the sample set sampled by the SRSS method is unbiased. Finally, an evaluation method is proposed to establish a confidence interval for the expected revenue obtained by the data-driven method. Experimental results based on real parking demand data show that our approach outperforms both a commonly used heuristic allocation policy in the parking industry and the standard SAA approach with two popular sampling methods and an evaluation method based on Student’s T-test in the literature.

Joint Optimization of Ticket Pricing and Allocation on High-Speed Railway Based on Dynamic Passenger Demand during Pre-Sale Period: A Case Study of Beijing–Shanghai HSR

Against the background of the gradual deepening of China Railway’s market-oriented reform, and in order to improve the revenue and competitiveness for high-speed railway (HSR) passenger transport, this paper studies the joint optimization problem of the high-speed railway ticket pricing and allocation considering the dynamic demand characteristics of passengers during the pre-sale period. Firstly, we use the compound non-homogeneous Poisson process to describe the passengers’ ticket-purchasing process and use the sparse method to simulate the passengers’ ticket demand during the pre-sale period. Secondly, taking the ticket pricing and allocation as the decision variables and considering the full utilization of the train seat capacity, a stochastic nonlinear-programming mathematical model is established with the goal of maximizing the train revenue. A particle swarm algorithm is designed to solve the model. Finally, this study takes the G19 train running on the Beijing–Shanghai HSR in China as a case study to verify the effectiveness of the model and algorithm. The results show that the joint optimization scheme of ticket pricing and allocation considering dynamic demand yields a revenue of CNY 601,881, which increases the revenue by 1.01% with a small adjustment of the price compared with the fixed ticket price and pre-allocation scheme. This study provides scientific support for the decisions made by railway transportation enterprises, which is conducive to further increasing the potential ticket revenue and promoting sustainable development.

Preallocation of Electric Buses for Resilient Restoration of Distribution Network: A Data-Driven Robust Stochastic Optimization Method

In recent years, severe hurricanes have occurred frequently, posing a huge challenge to the distribution network (DN) operation. Electric buses (EBs) possess large-capacity batteries and are widely used in public transit under normal circumstances. Before a hurricane occurs, some idle EBs can be preallocated to different charging stations equipped with vehicle-to-grid and served as sources for emergency power supply. This article proposes a prehurricane EB preallocation method to assist the resilience enhancement of a fragile DN. A two-stage data-driven robust stochastic programming technique is applied to build this model. Different scenarios are generated, and the corresponding posthurricane restoration processes are considered in the determination of the preallocation strategy. Also, the uncertainties of hurricane-induced physical damages are considered in modeling, which is described as a strengthened confidence set. The established model aims at exploring the optimal preallocation strategy of EBs with minimum load losses under the worst-case distribution. A column-and-constraint generation algorithm is then used to solve this proposed model. The proposed method is tested on a modified IEEE 33-bus system with 50 EBs. The results indicate that preallocating EBs to charging stations can improve the resilience of the posthurricane DN effectively.

Enhancing resilience of emergency heat and power supply via deployment of LNG tube trailers: A mean-risk optimization approach

Tube trailers are widely used for distributing liquefied natural gas (LNG) through transportation networks. When a snowstorm occurs in winter, bad weather conditions may cause pipeline blockages and restrict the movement of trailers, leading to an LNG disruption of an islanded integrated energy system (IES). Thus, it is necessary to pre-allocate the trailers and LNG to enhance IES’s resilience and support its LNG consumption during a snowstorm. This paper explores the resilience potential of LNG tube trailers and investigates their optimal pre-allocation strategy. At first, the system operation and damages of IES during a snowstorm are modeled. The network reconfiguration helps reconstruct the damaged network, and pre-allocated LNG helps support heat and power supply. Then, risk-averting two-stage stochastic programming is applied to solve the optimal trailer pre-allocation plan. The first stage tries to reduce the cost of LNG consumption and trailer pre-allocation; the second stage tries to avoid losses of heat and electricity loads. Besides, the conditional-value-at-risk is considered in the model to prevent low-probability but heavy-loss-load-shedding events. Note that the proposed model contains many integer variables, making it non-convex and prohibiting duality theory. A penalty-based Gauss–Seidel method is developed to solve this issue. This method decomposes the model into scenario-independent sub-problems that can be solved in parallel and achieve a consistent first-stage decision. The simulation results demonstrate the effectiveness of trailer pre-allocation in the IES’s resilience enhancement.

Flood drainage rights in watersheds based on the harmonious allocation method

Flood drainage rights (FDR) refer to the rights of all regions in a watershed to drain floodwaters into river reaches under the permission of law. FDR is an important measure used to manage the flooding in various regions of China. This paper explores the problem of flood drainage conflicts caused by regional competition for FDR during flood disasters. We designed an FDR allocation indicator system based on the principles of fairness, efficiency, sustainable development, respect for historical context, and flood resilience strategy. We used the interval multi-objective allocation model based on the projection method to create an FDR pre-allocation plan, and iteratively improved it until it exceeded the standards of a harmonious diagnosis method, thereby obtaining a harmonious allocation plan. The Jiangsu section of the Sunan Canal in the Yangtze River Delta region of China was taken as an example, using data from 2009 to 2018, to study the suitability of our approach and compare it with other indicator systems. Our results showed that: (1) in the FDR pre-allocation, the proportions of FDR obtained by Suzhou, Zhenjiang, Wuxi, and Changzhou were 32.81%, 19.83%, 24.68%, and 22.69%, respectively. However, the FDR pre-allocation scheme did not pass the harmonious diagnosis and would have been objectionable to some regions. (2) After two rounds of harmonious adjustment, the model yielded a final scheme that would promote the harmonious development of the region and improve the acceptability of the FDR allocation plan. In the harmonious distribution of FDR, the proportions of FDR obtained by Suzhou, Zhenjiang, Wuxi, and Changzhou were changed to 29.18%, 20.03%, 28.10%, and 22.69%, respectively. (3) The allocation results based on our five-principle approach were the most harmonious of the tested plans. Our method is geared toward solving the problem of flood drainage conflicts between regions to ensure an orderly flood drainage situation, reduce flood losses, and promote the harmonious development of the entire watershed. As such, it provides a reference for governments and agencies involved in FDR management.

Task-Level Aware Scheduling of Energy-Constrained Applications on Heterogeneous Multi-Core System

Minimizing the schedule length of parallel applications, which run on a heterogeneous multi-core system and are subject to energy consumption constraints, has recently attracted much attention. The key point of this problem is the strategy to pre-allocate the energy consumption of unscheduled tasks. Previous articles used the minimum value, average value or a power consumption weight value as the pre-allocation energy consumption of tasks. However, they all ignored the different levels of tasks. The tasks in different task levels have different impact on the overall schedule length when they are allocated the same energy consumption. Considering the task levels, we designed a novel task energy consumption pre-allocation strategy that is conducive to minimizing the scheduling time and developed a novel task schedule algorithm based on it. After getting the preliminary scheduling results, we also proposed a task execution frequency re-adjustment mechanism that can re-adjust the execution frequency of tasks, to further reduce the overall schedule length. We carried out a considerable number of experiments with practical parallel application models. The results of the experiments show that our method can reach better performance compared with the existing algorithms.

Channel Allocation Scheme for Wireless Sensor Networks Based on Graphical Topology Information

Wireless sensor network (WSN) is a distributed sensor network. The main research direction of wireless sensor network is the distribution method of its channel. With the development of information technology, the contradiction that between scarce wireless resources and high-density AP points will adversely affect people’s daily life. In this paper, we design a heuristic algorithm based on the channel assignment method by comparing the similarity of the network graphic topology. By calculating the pre-allocation scheme, the optimal throughput of the new topology is compared with the optimal throughput of the genetic algorithm to verify the superiority in the new algorithm. The network model is constructed by three graphical methods: scatter plot, density map and thermal graph. Finally determine the heat map-based channel allocation algorithm as the optimal solution.

A Prediction-Based Resource Matching Scheme for Rentable LEO Satellite Communication Network

The resource allocation strategy is a critical problem in the low earth orbit (LEO) satellite communication network (SCN) due to the limited resources and high movement. In this letter, we first introduce the rental service into LEO-SCN to improve the resource utilization by renting out unused resources. Secondly, we investigate a prediction-based resource matching scheme for rentable LEO-SCN. Specifically, a long-short-term memory (LSTM) based prediction framework is developed to predict the traffic value of local tasks, which is affected by the periodic motion and the inherent traffic law. Then, based on the predicted traffic, a task-driven joint resource allocation method is proposed to efficiently match power and spectrum resources. Finally, the simulation results verify the validity of the proposed approach compared with the pre-allocation strategy and the adaptive allocation strategy. Moreover, we analyze the key factors that affect the achievable performance of the proposed prediction-based resource matching scheme.

Harmonious allocation of carbon emission permits based on dynamic multiattribute decision-making method

Fair and reasonable allocation of carbon emission permits between regions is the premise of effective operation of carbon emission trading scheme. Thus, based on the harmonious management theory, we propose a two-stage allocation model of “preliminary allocation-harmonious diagnosis and evolution” to analyze the harmonious allocation of carbon emission permits. Specifically, we integrate four principles of fairness, efficiency, sustainability, and government macrocontrol to construct an allocation index system and use the dynamic multiattribute decision-making method to obtain a preallocation scheme. Also, by establishing a directional diagnosis criterion and degree diagnosis criterion, harmonious diagnosis and evolutionary adjustment of preallocation scheme were carried out to obtain a harmonious allocation scheme with high acceptability. The allocation results of North China in 2020 indicate the following: (1) As the preallocation scheme may be difficult to be accepted by various regions, harmonious diagnosis and adjustment play an important role in the allocation of carbon emission permits. By simulating the negotiation process of each region, we obtained the allocation with high acceptability. (2) According to the allocation result, Hebei obtained the highest proportion of carbon emission quota, while Beijing and Tianjin obtained a lower proportion of carbon emission quota. (3) Compared with the allocation results of grandfather principle, Shanxi and Nei Mongol have great pressure on emission reduction, i.e., these regions should occupy a larger share in the future carbon emission trading market. This study is expected to provide a reference for the initial allocation of carbon emission permits, thus reducing conflicts and promoting the harmonious development of region.

A Learning-Based Pre-Allocation Scheme for Low-Latency Access in Industrial Wireless Networks

To promote the revolution of Industrial Internet of Things, the next generation communication system is expected to provide latency critical services in industry. However, for the traditional downlink-centric cellular systems, the timely delivery of packets cannot be guaranteed by the default dynamic access scheme due to complex signaling procedure. A promising solution to low-latency access is the resource pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, however at the expense of low spectrum utilization. Aiming to make those pre-allocated resources more rewarding, a so-called DPre, a predictive pre-allocation scheme based on learning for low-latency uplink access in industrial wireless networks, is proposed in this paper. It intelligently explores the correlation of devices’ access behavior and device utility diversity through sequential learning. Thus, flexible and judicious per-allocation decisions in both time and frequency domains can be made in an on-demand manner. Moreover, with the proposed temporal-spatial utility metric, DPre is guaranteed to reserve for more informative devices. Both theoretical analysis and simulation validate its high spectrum utilization through accurate prediction and the potential to pre-allocate for valuable packets.

Towards tenant demand-aware bandwidth allocation strategy in cloud datacenter

As a critical resource for tenants in cloud datacenter, network bandwidth is shared and competed by tenants at the same time. Previous static bandwidth allocation strategies have a good performance in the sharing case. However, for the competing case where bandwidth oversubscription causes conflicts in network resources, existing bandwidth allocation strategies cannot offer a satisfactory solution. In this article, we propose an auto pre-allocation strategy to solve the bandwidth oversubscription issue in cloud datacenter. Our proposal aims to design and implement a bandwidth allocation system embedded in cloud platform using the technology of software-defined networking (SDN). We employ two sampling methods in bandwidth collection and adopt the ARIMA model to make the prediction. Firstly, the virtual machines (VMs) are divided into predictable and unpredictable groups based on ARIMA model, and each predictable VM has three states in terms of its loading status. After that, corresponding bandwidth allocation strategy is produced to limit the bandwidth utilization in a proper range by adjusting the bandwidth for next period. The experimental results show that the auto pre-allocation strategy improves network performance of cloud datacenter, in both bandwidth utilization ratio and network capacity.

Choice of Antiviral Allocation Scheme for Pandemic Influenza Depends on Strain Transmissibility, Delivery Delay and Stockpile Size.

Recently, pandemic response has involved the use of antivirals. These antivirals are often allocated to households dynamically throughout the pandemic with the aim being to retard the spread of infection. A drawback of this is that there is a delay until infection is confirmed and antivirals are delivered. Here an alternative allocation scheme is considered, where antivirals are instead preallocated to households at the start of a pandemic, thus reducing this delay. To compare these two schemes, a deterministic approximation to a novel stochastic household model is derived, which allows efficient computation of key quantities such as the expected epidemic final size, expected early growth rate, expected peak size and expected peak time of the epidemic. It is found that the theoretical best choice of allocation scheme depends on strain transmissibility, the delay in delivering antivirals under a dynamic allocation scheme and the stockpile size. A broad summary is that for realistic stockpile sizes, a dynamic allocation scheme is superior with the important exception of the epidemic final size under a severe pandemic scenario. Our results, viewed in conjunction with the practical considerations of implementing a preallocation scheme, support a focus on attempting to reduce the delay in delivering antivirals under a dynamic allocation scheme during a future pandemic.

Ordering circuit establishment in multiplane NoCs

Segregating networks-on-chips (NoCs) into data and control planes yields several opportunities for improving power and performance in chip-multiprocessor systems (CMPs). This article describes a hybrid packet/circuit switched multiplane network optimized to reduce latency in order to improve system performance and/or reduce system energy. Unlike traditional circuit preallocation techniques which require timestamps to reserve circuit resources, this article proposes an order-based preallocation scheme . By enforcing the order in which resources are scheduled and utilized rather than a fixed time, the NoC can take advantage of messages that arrive early while naturally tolerating message delays due to contention. Ordered circuit establishment is presented using two techniques. First, Déjà Vu switching preestablishes circuits for data messages once a cache hit is detected and prior to the requested data becoming available. Second, using Red Carpet Routing , circuits are proactively reserved for a return data message as a request message traverses the NoC. The reduced communication latency over configured circuits enable system performance improvement or saving NoC energy by reducing voltage and frequency without sacrificing performance. In simulations of 16 and 64 core CMPs, Déjà Vu switching enabled average NoC energy savings of 43% and 53% respectively. On the other hand, simulations of communication sensitive benchmarks using Red Carpet Routing show speedup in execution time of up to 16%, with an average of 10% over a purely packet switched NoC and an average of 8% over preconfiguring circuits using Déjà Vu switching .

Markov Chains Based Dynamic Bandwidth Allocation in DiffServ Network

This letter proposes a Markov chain based model for dynamic bandwidth allocation in DiffServ networks. At a time-slot, the proposed Markov chain is used to predict the bandwidth requirement at the next time-slot, and resource is then allocated accordingly. Such a pre-allocation scheme can effectively reduce the operation overhead in bandwidth allocation and further reduce the connection blocking probability. We present numerical results showing that our dynamic bandwidth allocation mechanism can reduce the network blocking probability by one order of magnitude, compared with the existing bandwidth borrowing mechanism.

Dynamic Spectrum Access Based on MAC-Layer Spectrum Sensing and Prior Channel Pre-Allocation Strategy

This paper investigates dynamic spectrum access based on MAC-Layer spectrum sensing and prior channel pre-allocation strategy. We first combine channel utilization with channel state transition probability from idle to busy to reflect the channel opportunity quality in cognitive radio systems. Then a MAC-Layer spectrum sensing algorithm based on Channel Opportunity Quality Descending Order (COQDO) is proposed for the single secondary user scenario, so that the single secondary user can be provided with dynamic spectrum access. For the multi-secondary users scenario, in order to solve the channel collision problem among secondary users in dynamic spectrum access, a joint MAC-Layer spectrum sensing and prior channel pre-allocation algorithm is proposed and analyzed. Channel collision problem occurs when more than one secondary users detect the channel as idle and access it at the same time. Furthermore, the prior channel pre-allocation is optimized by using the conventional Color Sensitive Graph Coloring (CSGC) algorithm. Extensive simulation results are presented to compare our proposed algorithms with existing algorithms in terms of idle channel search delay and accumulated channel handoff delay.

Preferential transmission of multicast packets using a preallocation scheme in combination with a wavelength monitoring scheme on a RingO network

AbstractThis paper proposes a multicast packet preferential transmission system combining preallocation and waveform monitoring schemes with the RingO network. In the proposed system, a multicast packet preferential time slot is allocated to each node by preallocation. Each node can use all wavelengths in the allocated time slot to transmit a multicast packet simultaneously to all destination nodes. Wavelength monitoring is applied to time slots other than the allocated time slot, and if there is an available wavelength channel, a unicast packet is transmitted. Thus, the data packets are transmitted with high efficiency. The performance is evaluated by computer simulation. It is shown that the proposed system can greatly improve the mean packet delay and the throughput characteristics, compared to the simultaneous transmission system, in a heavily loaded situation with an input load exceeding 0.9. The proposed method is also shown to be more effective than the partitioned transmission system, in that the transmission delay difference of multicast packets between destination nodes, and also the packet delay difference of multicast packets for each destination node, can be reduced. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(7): 56– 68, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20375

A pre-allocation strategy for implement ADA95′s distrbuted computing

In order to realize distributed computing of Ada95, this paper discusses Ada95′s distributed system model and an implement model of Ada95′s distributed computing—workstation cluster model. Under this model, we presents a pre-allocation strategy for allocating the computation quantity of distributed units evenly among workstations and also reducing the communication expense between those distributed units.