Matching Scheduling Research Articles

Path Selection of Integrated Development of Enterprise Supply Chain Management Driven by Digital Transformation

Digital transformation provides a new impetus for the development of the supply chain, which can not only improve and expand the visualization level of the supply chain system and the matching scheduling ability, so as to improve the system agility and work efficiency. Based on this, this paper analyzes and discusses the integrated development of supply chain management driven by digital transformation, discusses how to integrate different technologies under the digital background to form a centralized and fast response of supply chain management, and correspondingly cultivate a dynamic supply chain ecosystem to achieve the overall strategic change of the organization.

Efficient microservices offloading for cost optimization in diverse MEC cloud networks

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

An integrated energy system (IES) breaks down barriers between different energy subsystems, enhancing energy reliability and efficiency. However, issues such as uneven equipment capacity allocation and suboptimal scheduling persist in multi-energy flow IES. To maximize economic benefits while ensuring energy balance and the operational characteristics of the equipment, a capacity matching optimization and scheduling strategy model for IES was developed. Firstly, mathematical models for the electricity, gas, and thermal networks within the IES were established. Secondly, considering the efficiency of energy conversion between different forms and constraints of energy storage in the electricity–thermal–gas interconnected energy system, optimization solutions were obtained using regional contraction algorithms and sequential quadratic programming methods. Finally, case studies conducted in a real park demonstrated that, through optimized capacity matching, unit prices for electricity, heat, and gas decreased by 39.9%, 90.5%, and 74.2%, respectively, effectively improving the economic viability of the system.

REVOLUTIONIZING RECRUITMENT: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TALENT ACQUISITION

The rapid advancements in Artificial Intelligence (AI) are transforming various sectors, and talent acquisition is no exception. This research paper exploring how AI-driven tools are reshaping the way organizations attract, evaluate, and hire talent. With the global competition for top talent intensifying, the need for efficient, data-driven hiring strategies is critical. AI technologies, including machine learning, natural language processing, and predictive analytics, have become pivotal in streamlining tasks such as resume screening, candidate matching, and interview scheduling, significantly reducing time-to-hire and improving candidate experiences. This paper conducts a thorough analysis of AI's integration into talent acquisition, AI’s benefits, challenges, and ethical considerations. Key findings show that AI reduces hiring time by up to 50%, enhances candidate engagement through automation, and allows for more informed hiring decisions. Despite its potential, organizations must carefully manage the ethical challenges AI poses to ensure fair and transparent recruitment practices. In conclusion, AI has the potential to revolutionize talent acquisition, but its success depends on thoughtful implementation and continuous evaluation to mitigate risks and maximize benefits.

Robust optimization of geoenergy production using data-driven deep recurrent auto-encoder and fully-connected neural network proxy

Robust and efficient optimization of post-history well production schedule under history-matched geomodel known as closed-loop production management is crucial to achieve reasonable and reliable decision-making for subsurface geoenergy resources (e.g., oil/gas resources, geothermal, natural gas hydrate, etc.) recovery in petroleum industry. The procedure generally consists of a cycling implementation of geomodel history matching and production schedule optimization through repeatedly simulating high-fidelity reservoir models and therefore needs almost infeasible computation cost particularly for field-scale oil production scenario. Through hybridizing deep recurrent neural network and particle-swarm optimization algorithm, we present a novel data-driven computation framework for robust post-history production optimization without explicitly performing history matching. In the proposed framework, we develop a hybrid neural network proxy model, which combines recurrent autoencoder (RAE) and fully-connected neural network (FCNN), referred to as RAE-FCNN. The RAE structure can extract latent information from time-series history data, and the FCNN structure can map the combination of extracted latent variables by RAE and post-history well-control sequence to net present value (NPV). The trained RAE-FCNN proxy model can predict NPV values corresponding to the history measurements and user-specific post-history well controls. In the robust optimization framework, the averaged NPV over an ensemble of noisy history measurements are regarded as the objective function while the post-history well-controls are chosen as the decision variables. The proposed surrogate-based optimization framework has been demonstrated on two waterflooding reservoir production cases, e.g., a 2D synthetic Gaussian model and a 3D benchmaRk channelized EGG model.The decision variables considered in optimization are the injection rates with linear bound constraints at the injectors. In comparison with the optimization results by high-fidelity reservoir models, solely 1000 high-fidelity reservoir models for both cases are sufficient to train accurate proxy model and achieve satisfactory post-history production optimization results. Our proposed deep-learning-based optimization method can effectively make full use of history data to predict post-history oil well production without the need of computationally expensive history matching step, which enables us to achieve quick decision-making and risk evaluation for close-loop waterflooding reservoir production management and optimization.

Transformer language model for mapping construction schedule activities to uniformat categories

Creating consistency among project schedule data, BIM, and payment applications requires activities in a construction schedule to be mapped with the most relevant ASTM Uniformat classifications. To do so, we introduce UniformatBridge, a new transformer-based natural language processing model, that automatically labels activities in a project schedule with Uniformat classification. Our model introduces construction sequencing tokens that capture logistically-constrained predecessor and successor activities into BERT architecture. We also introduce a dataset of real-world construction project schedules with their ground-truth Uniformat classifications for validation. Experimental results using this dataset achieve F1-scores of 0.93 and 0.87 when matching unstructured schedule data to Uniformat Level 2 and 3 classifications, respectively. We share how our method unlocks development of new techniques to (1) automatically create 4D BIM, and (2) computer-vision progress monitoring to tie semantic segmentation of reality capture data based on Uniformat classes against schedule or payment application data structures, with/without BIM.

A Matching Scheduling Control Strategy Based on Modulation Wave Reconstruction for the Single-Phase Photovoltaic Cascaded Multilevel Inverter

The single-phase cascaded multilevel inverter (CMI) becomes an attractive solution for grid-connected photovoltaic (PV) power generation owing to its several advantages, such as the scalable modularity, the ability to reach the component level maximum power point tracking (MPPT), and the multilevel output voltage. However, the PV power of each H-bridge (HB) is uneven due to unequal illumination and partial shading of the PV panels, which may lead to overmodulation and further deteriorate the power quality of the grid-connected current. Existing methods to address this issue have not considered the effects on various operating conditions, and their application scope is limited. Therefore, this paper proposes a matching scheduling control strategy based on modulation wave reconstruction (MSMR). The relatively optimal modulation waveform of each HB is reconstructed scheduledly to match its PV power by detecting the operating conditions. Meanwhile, the PV power of each HB is adjusted to improve the ability to cope with the power unevenness. Finally, the proposed strategy is validated on a down-scaled experimental test bench.

Optimal Coordination of Platoons of Connected and Automated Vehicles at Signal-Free Intersections

In this paper, we address the problem of coordinating platoons of connected and automated vehicles crossing a signal-free intersection. We present a decentralized, two-level optimal framework to coordinate the platoons with the objective to minimize travel delay and fuel consumption of every platoon crossing the intersection. At the upper-level, each platoon leader derives a proven optimal schedule to enter the intersection. At the low-level, the platoon leader derives their optimal control input (acceleration/deceleration) for the optimal schedule derived in the upper-level. We validate the effectiveness of the proposed framework in simulation and show significant improvements both in travel delay and fuel consumption compared to the baseline scenarios where platoons enter the intersection based on first-come-first-serve and longest queue first - maximum weight matching scheduling algorithms.

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

Coordinated appointment scheduling with multiple providers and patient-and-physician matching cost in specialty care

To achieve effective care, it is critical to match patients with capable physicians in specialty care. Motivated by the rising popularity of patient-and-physician matching applications in specialty care, this study optimizes the matching and appointment scheduling problems simultaneously in a stochastic environment, in which a decision-maker determines the patient-and-physician pair assignment and the starting times of services. We develop a stochastic optimization model to minimize the matching and operational costs (i.e., patients’ waiting time costs, service providers’ idle time and overtime costs). This paper is the first study that incorporates matching and appointment scheduling problems together. The benefits of combining these two problems are enormous. The experimental results show that the operational costs gap is as large as 51% between the ill-matched and the well-matched patient-and-physician scenarios. We first reformulate this problem as a two-stage optimization problem. With the analysis for the optimal solution of the second stage problem, a Benders decomposition algorithm is developed. To improve the efficiency of the proposed algorithm, we also prove a low bound of our problem and use it to construct a set of feasibility cuts. Then, we extend our method to incorporate no-shows. Our algorithm can solve problems efficiently, and it can obtain optimal solutions for medium-size problems within 2 or 3 min. In contrast, traditional optimal methods require nearly 2 h. For large-size problems, our algorithm can obtain optimal solutions within 5 or 6 min, whereas traditional optimal methods cannot generate a result within 5 h. Finally, numerical experiments are conducted to evaluate the performance of our proposed algorithm and to investigate the variation of the optimal solutions in different scenarios. To provide quality care as well as minimize the total cost of appointment scheduling in specialty care, we suggest that physicians should develop or train their specialties based on the local patients’ disease pattern. We also disclose that the no-show has less influence on the service system when the weight of the matching cost is substantial.

The left-behind human detection and tracking system based on vision with multi-model fusion and microwave radar inside the bus

Left-behind humans inside the car or bus have caused a lot of accidents, so it is essential to detect the humans in vehicle. Current human detection methods rely on wearable devices, oxygen sensors, and special seat designs in vehicles, but those sensors cannot adapt to ever-changing environments. To solve those problems and especially to improve passengers’ safety on the bus, we propose a method to accomplishing human detection by fusion vision and microwave radar information in various environments in vehicle. For vision information, we use different networks to extract human and human face features, and fusion of the detection results in different models to improve human detection accuracy. The human detection model is MobileNet-V2, and the human face detection model is MTCNN. A new matching schedule and tracking objects management rule based on the Kernelized Correlation Filter tracker are designed to track the human and human face detection boxes. The microwave radar information is used to detect moving objects. Finally, the fusion vision and microwave radar detection results are implemented. Experiments show that our method has improved the human detection accuracy in vehicle, and this method can be used for detection of left-behind children on the school bus.

Many-to-Many Recruitment and Scheduling in Spatial Mobile Crowdsourcing

Spatial mobile crowdsourcing (SMCS) enables a task requester to commission workers to physically travel to specific locations to perform a set of spatial assignments (i.e., tasks are related to a specific geographical location besides time). To efficiently perform such tasks and guarantee the best possible quality of returned results, optimizing the worker recruitment and task assignment processes must be conducted. Because both workers and task requesters impose certain criteria, this procedure is not obvious. To tackle this issue, we propose a novel formulation of the SMCS recruitment where task matching and worker scheduling are jointly optimized. A Mixed Integer Linear Program (MILP) is first developed to optimally maximize the quality of matching measured as a weighted score function of different recruitment metrics while determining the trajectory of each selected worker executing tasks. To cope with NP-hardness, we propose a heuristic SMCS recruitment approach allowing the achievement of sub-optimal matching and recruitment solution by iteratively solving a weighted bipartite graph problem. Simulation results illustrate the performance of the SMCS framework for selected scenarios and show that our proposed SMCS recruitment algorithm outperforms an existing greedy recruitment approach. Moreover, compared to the optimal MILP solution, the proposed SMCS recruitment approach achieves close results with significant computational time saving.

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

Nowadays, the usage of the fog cloud-based Internet of Things (IoT) applications among users has been growing progressively. These applications may be E-Transport, E-Healthcare, Augmented Reality, and 3D-Game. Generally, contemporary cloud frameworks offer services based on virtual machines. These frameworks incurred with following issues such as long boot-time, overhead and unnecessary cost to run the IoT applications. We propose a new Microservice container fog system (MSCFS) based framework to run the mobility and delay-sensitive applications with minimum cost. The study the cost-efficient task scheduling problem in the heterogeneous fog servers. Furthermore, the study introduces the Cost Aware Computational Offloading and task scheduling (CACOTS)framework, which solves the task scheduling into multiple steps. Such as task sequencing step, resource matching step and scheduling step. The experimental results prove that the proposed MSCFS and CACOTS schemes can enhance server utilization. As decrease the services latency and average services bootup time more effectively, and minimize costs.

Heterogeneous Multiprocessor Matching Degree Scheduling Algorithm Based on OpenCL Framework

In a heterogeneous multi-core system, the merits of the task scheduling algorithm have a great impact on the efficiency of the system. This paper implements a matching degree scheduling algorithm based on heterogeneous multiprocessor. Before the actual scheduling, the static task feature value and the processor core configuration feature value are mapped into the same European space, and the “program-core-distance” matching value is calculated by using the WED. In the actual scheduling, the matching value and the size of the input byte are obtained, and the tasks are matched with the processor under the influence of the two to determine the execution order of the tasks on each processor. The scheduling method based on OpenCL framework is compared with the traditional scheduling algorithm. It shows that the scheduling scheme implemented in this paper has significantly improved efficiency when executing tasks.

Optimal Matching Control Schedule Research On an Energy System

Efficiency is the always ambition of energy system research. The extreme working conditions and materials limit the ceiling of improvement seriously. Based on the research of an evolutional gas turbine concept (the adaptive cycle engine) which draws widely attention with high expectations, this paper raises a new academic conception: optimal matching control schedule method. This method is to solve the complicated control schedule of an energy system. It can improve thermal efficiency when the propulsive efficiency is hold versus the turbofans: to improve propulsive efficiency when the thermal efficiency is hold. The optimal matching control schedule method combines the physical essence analysis of the components matching mechanism principle on an energy system with the mathematical programming optimization method. According to this research, it can optimize the components matching and increase the component efficiencies to raise the thermal efficiency sequentially.

Proactive Scheduling in Cloud Computing

Autonomic fault aware scheduling is a feature quite important for cloud computing and it is related to adoption of workload variation. In this context, this paper proposes an fault aware pattern matching autonomic scheduling for cloud computing based on autonomic computing concepts. In order to validate the proposed solution, we performed two experiments one with traditional approach and other other with pattern recognition fault aware approach. The results show the effectiveness of the scheme.

Scheduling for Data Transmission in Multi-Hop IEEE 802.15.4e TSCH Networks

TSCH is one of the access behavior modes defined in the IEEE 802.15.4e standard. It combines time slotted access with multi-channel and channel hopping capabilities. TSCH is a trend used as the underlying architecture of the Internet of Things. However, IEEE 802.15.4e defines when the MAC executes a schedule but it does not specify how the schedule is built. This paper focuses on the problem of data transmission scheduling for TSCH network which uses the IEEE802 .15.4e TSCH as the MAC protocol. We adopt matching rule to resolve conflicts in the TSH network and matching scheduling algorithm(MSA) to solve the problem of communication resources allocation in tree topology. The scheduling algorithm MSA can avoid conflict, reduce the delay and energy consumption. OSA is proposed by further optimization of MSA. Through simulation experiments, the proposed method can effectively shorten the time delay, reduce the energy consumption and reduce the utilization rate of the channel.

Local Computation Mechanism Design

We introduce the notion of local computation mechanism design —designing game-theoretic mechanisms that run in polylogarithmic time and space. Local computation mechanisms reply to each query in polylogarithmic time and space, and the replies to different queries are consistent with the same global feasible solution. When the mechanism employs payments, the computation of the payments is also done in polylogarithmic time and space. Furthermore, the mechanism needs to maintain incentive compatibility with respect to the allocation and payments. We present local computation mechanisms for two classical game-theoretical problems: stable matching and job scheduling. For stable matching, some of our techniques may have implications to the global (non-LCA (Local Computation Algorithm)) setting. Specifically, we show that when the men’s preference lists are bounded, we can achieve an arbitrarily good approximation to the stable matching within a fixed number of iterations of the Gale-Shapley algorithm.

Maximum weight matching with hysteresis in overloaded queues with setups

We consider a system of parallel queues where arriving service tasks are buffered, according to type. Available service resources are dynamically configured and allocated to the queues to process the tasks. At each point in time, a scheduler chooses a service configuration across the queues, in response to queue backlogs. Switching from one service configuration to another incurs a setup time, during which idling occurs and service bandwidth is lost. Such setup times are inherent in manufacturing and computer systems. Frequent switchings can significantly compromise the service capacity of such systems. A maximum weight matching (MWM) scheduler, which is known to maximize throughput in the absence of setups, can easily become unstable with setups, even under low load. To remedy this problem, we propose a new MWM-H scheduler which utilizes a controller introduced hysteresis and achieves maximum throughput even with setups, without requiring knowledge of arrival rates and average traffic loads. During prolonged traffic bursts, the queues may become overloaded and the issue becomes how to reasonably distribute the growing backlog under MWM-H. It is shown that by appropriately selecting the MWM-H parameters, one can control the backlog among the individual queues in order to achieve a desired balance.

Resource Supply-Demand Matching Scheduling Approach for Construction Workface Planning

AbstractThis paper discusses a novel approach for resource-based scheduling that builds upon existing analytical models to achieve practical allocation of resources that are constrained by supply a...