Scheduling Metric Research Articles

The Impact of AI Adoption on Project Scheduling Efficiency in Real Estate Development Projects in Nairobi

The adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies in real estate project management has resulted to the enhancement of project scheduling efficiency. As such, this study investigated how AI has enhanced the optimization of project schedules within the real estate sector in the Nairobi Metropolitan Area. A mixed research method approach was employed in which the research integrated qualitative data collected through interviews with project managers and quantitative data obtained from project records to evaluate the impact of AI on key scheduling metric. The metrics include Schedule Performance Index (SPI) and the Critical Path Length Index (CPLI). The findings of this study reveal that the adoption of AI can lead to tremendous improvements in scheduling efficiency hence leading to a reduction in project delays and thus leading to effective schedule adjustments. AI tools have also proven to contribute to better resource allocation and cost control which leads to smoother project execution. However, the adoption of AI still face challenges such as high implementation costs, data privacy concerns, and a shortage of skilled personnel. Therefore, this study concludes that while AI offers a greater potential in the enhancement of project scheduling, it is still worth addressing the identified challenges to be able to maximize its benefits. This study recommends that real estate companies should invest in AI training amongst its staff and also a cost-effective AI solutions for smaller firms should be developed to benefit all stake holders in the real estate field. There should also be a robust implementation of data privacy policies to improve the security of those using AI in managing their projects.

Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

One of the critical issues in healthcare management is the operating room (OR) scheduling problem. Solutions to this problem consider surgery durations and allocate elective surgeries to OR sessions in order to create surgical lists of high quality. Determining the quality of a surgical list is a key undertaking within OR scheduling and is the focus of this research. Currently, probability- and/or expectation-based measures of surgical lists are used instead of statistical distributions of surgery lists to measure quality. The use of multiple measures, e.g., a combination of expectation and probability to assess a surgical list, complicates OR scheduling, so we introduce a new single measure – the OR scheduling metric – for evaluating surgical lists before their realisations, i.e., for use within OR scheduling. We apply the OR scheduling metric to an actual elective dataset and use simulation to demonstrate its use, including customised scheduling rules. We recommend the adoption of a benchmarked OR scheduling metric by the elective surgical services in hospitals with expected practical benefits in the long run, i.e., simpler OR scheduling and more desirable room utilisation, to be similar to that observed in our simulations.

Adaptive Perturbation-Aided Opportunistic Hybrid Beamforming for mmWave Systems

In this paper, we propose an adaptive perturbation-aided opportunistic hybrid beamforming (AP-OHBF) scheme for temporally correlated millimeter wave (mmWave) channels. The basic idea is that the proposed scheme perturbs the channel parameters to track the channels of the scheduled mobile stations (MSs), whose channels are in favorable conditions, based on only the signal-to-interference-plus-noise ratio (SINR) information of MSs. Specifically, in each time slot, the proposed scheme makes random perturbations to the “best” known channel parameters in the memory, which corresponds to the maximum sum scheduling metric. Then the base station (BS) constructs the channel estimates from these parameters, which are subsequently used to generate the precoders. For data transmission, the BS adopts the successful perturbation, which achieves performance improvements. By contrast, if a perturbation results in performance degradation, it is discarded, whereas the precoders corresponding to the best-known parameters in the memory are instead used for transmission. Simulation results show that in mmWave channels, the proposed AP-OHBF scheme significantly improves the system throughput with respect to the conventional opportunistic beamforming schemes for the same amount of feedback information.

A Gibbs sampling based coordination scheduling method with fixed I/O constraint

Distributed production technology is the basic technology of modern production. Distributed production needs to consider the fixed input and output constraint, which is different from traditional centralized production. The objective of distributed production scheduling with fixed I/O constraint is to find the optimal scheduling route in production network. In this paper, we proposed the GSS (Gibbs Sampling Scheduling) method to schedule the schedulable jobs, according to a scheduling metric. GSS guarantees each device has opportunities to be allocated, to reduce the load on critical devices. The scheduling metric utilizes PCM (processing cost model) and TCM (transportation cost model) as parameters. The PCM represents the impact of schedulable jobs on the unscheduled jobs. The TCM represents the importance of devices’ positions in the production network. We designed experiments to analyze the parameters of the GSS, and carried out the test on makespan and load balance. These experiments can verify the effectiveness and efficiency of the proposed GSS method.

A Hybrid Downlink Scheduling Approach for Multi-Traffic Classes in LTE Wireless Systems

The developments in wireless technology and applications in recent years have increased the interest in downlink scheduling and resource allocations among researchers. Moreover, fair scheduling and balanced Quality of Service (QoS) delivery for various forms of traffic are needed for Long-Term Evolution (LTE) wireless systems. This paper proposes hybrid QoS-aware downlink scheduling approaches that aim to address different traffic classes and balance the QoS delivery with improvements to the overall system performance under channel and bandwidth constraints. Moreover, this research introduces a taxonomy that classifies the scheduling algorithms into four main classes: delay aware, queue aware, target bit-rate aware and hybrid aware. The latter class is the scheduling class that is proposed in this paper; it considers channel, queue and delay parameters in its scheduling metric. Using simulations, we compare and analyze different downlink scheduling rules for their network-centric performance metrics, e.g., average packet loss ratio, average throughput, average packet delay, system fairness, and system spectral efficiency. The simulation results show that the queue-aware and delay-aware scheduling rules deliver the best QoS performance for video traffic classes, whereas our proposed hybrid scheduling rules deliver balanced QoS for various types of traffic classes. Employing QoS balancing scheduling rules in an LTE downlink is suggested to provide high QoS delivery for different traffic classes.

A Statistical Priority-Based Scheduling Metric for M2M Communications in LTE Networks

Resource allocation, or scheduling, is one of the main challenges that face supporting machine-to-machine (M2M) communications on long term evolution networks. M2M traffic has unique characteristics. It generally consists of a large number of small data packets, with specific deadlines, generated by a potentially massive number of devices contending over the scarce radio resources. In this paper, we introduce a novel M2M scheduling metric that we term the “statistical priority”. Statistical priority is a term that indicates the uniqueness of the information carried by certain data packets sent by machine-type communications devices (MTCDs). If an MTCD data unit is significantly dissimilar to the previously sent data, it is considered to carry non-redundant information. Consequently, it would be assigned higher statistical priority, and this MTCD should then be given higher priority in the scheduling process. Using this proposed metric in scheduling, the scarce radio resources would be used for transmitting statistically important information rather than repetitive data, which is a common situation in M2M communications. Simulation results show that our proposed statistical priority-based scheduler outperforms the other baseline schedulers in terms of having the least number of deadline misses (less than 4%) for critical data packets. In addition, our scheduler outperforms the other baseline schedulers in non-redundant data transmission as it achieves a success ratio of at least 70%.

SMART for mobile health: A study of scheduling algorithms in full-IP mobile networks

As a joint initiative of WHO and ITU, mHealth is becoming a feasible tool to offer data-intensive healthcare services online. In general, each kind of mHealth application has user-specific service-quality requirements that could be satisfied in the cost of bandwidth overconsumption. In any mobile network, bandwidth overconsumption is selfishly competing with limited radio resources. This phenomenon is further magnified by the “best-effort” service pattern inherited from IP networking, and the influence from interference, fading, and other mobility effects on unreliable radio channels. To enrich mHealth applications, we propose a Service-Mapping Adaptive Radio Transmission (SMART) scheduling algorithm, in which system parameters (QCI, CQI, MCS in AMC, etc.) are employed to enable “better than best effort” packet-switching services in full-IP mobile networks (e.g. LTE/LTE-Advanced). In this proposal, the designed scheduling metric is a self-adaptive mechanism that can combine mobile service-quality inspection and transmission pattern recognition dynamically. System-level simulation experiments demonstrate that the proposal can obtain approximately 31.25 percent gain on average, in contrast to existing solutions. Additionally, the runtime complexity of this solution is lowered to O(K log K), where K is the number of mHealth users within a cell.

Incorporating Energy-Aware Mechanism into Workflow Scheduling Policy in Heterogeneous Distributed Systems

In the past decades, plenty scheduling policies have been proposed for improving the execution performance of workflow applications. However, few of them have addressed the issue of energy conservation. In this paper, a novel scheduling metric, namely Minimal Energy Consumption Path, is introduced to reducing the energy consumption when scheduling large-scale workflows. The proposed scheduling metric can be incorporated into current schedulers so as to enhance their capability of energy conservation as well as maintain their scheduling performance. A lot of experiments are performed by using different scheduler and experimental settings to evaluate the proposed scheduling metric and the results indicate it is effective for reducing the storage-related energy consumption, especially when the target workflows are data-intensive.

An Opportunistic, Fast, and Distributed Subchannel and User-Pairing Algorithm for OFDMA

Channel-aware assignment of subchannels to users in the downlink of an OFDMA system requires extensive feedback of channel state information (CSI) to the base station. Since bandwidth is scarce, schemes that limit feedback are necessary. We develop a novel, low feedback, distributed splitting-based algorithm called SplitSelect to opportunistically assign each subchannel to its most suitable user. SplitSelect explicitly handles multiple access control aspects associated with CSI feedback, and scales well with the number of users. In it, according to a scheduling criterion, each user locally maintains a scheduling metric for each subchannel. The goal is to select, for each subchannel, the user with the highest scheduling metric. At any time, each user contends for the subchannel for which it has the largest scheduling metric among the unallocated subchannels. A tractable asymptotic analysis of a system with many users is central to SplitSelect's simple design. Extensive simulation results demonstrate the speed with which subchannels and users are paired. The net data throughput, when the time overhead of selection is accounted for, is shown to be substantially better than several schemes proposed in the literature. We also show how fairness and user prioritization can be ensured by suitably defining the scheduling metric.

Multiuser Diversity in a MIMO System With Opportunistic Feedback

An opportunistic feedback protocol with multiple thresholds and multiple access probabilities is proposed for multiuser diversity in multiple-input-multiple-output (MIMO) broadcast channels. Users compete to report the channel state information (CSI) via contention-based feedback channels if their scheduling metric exceeds a threshold. The users are divided into two classes according to their scheduling metric. For each class, the users have different random access probabilities and different chances to access feedback minislots. The performance of the proposed feedback protocol is evaluated in a MIMO system with zero-forcing (ZF) receivers in terms of the sum-rate capacity, the feedback load, and fairness. The simulation results show that the proposed feedback protocol significantly reduces the feedback load without sacrificing the sum-rate capacity too much. The proposed feedback protocol can be operated with existing scheduling algorithms without modification.

Power-aware scheduling for makespan and flow

We consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric. For makespan, we give a linear-time algorithm to compute all non-dominated solutions for the general uniprocessor problem and a fast arbitrarily-good approximation for multiprocessor problems when every job requires the same amount of work. We also show that the multiprocessor problem becomes NP-hard when jobs can require different amounts of work. For total flow, we show that the optimal flow corresponding to a particular energy budget cannot be exactly computed on a machine supporting exact real arithmetic, including the extraction of roots. This hardness result holds even when scheduling equal-work jobs on a uniprocessor. We do, however, extend previous work by Pruhs et al. to give an arbitrarily-good approximation for scheduling equal-work jobs on a multiprocessor.

Proportional fair scheduling with reduced feedback

An opportunistic feedback technique is proposed for proportional fair (PF) scheduling in a downlink multi-user system. This technique requires each mobile station, the scheduling metric of which is higher than a threshold, to send one-bit feedback for PF scheduling. The scheduler determines the optimal modulation and coding scheme for a scheduled user based on channel quality indicator (CQI) update, which is sent on a longer period than the scheduling period. The simulation results indicate that PF scheduling with the proposed update scheme can achieve similar performance as PF scheduling with full CQI feedback. The feedback overhead can be reduced in comparison with typical PF scheduling provided that an optimal CQI update period is chosen.

Capacity and Fairness in Multiuser Diversity Systems with Opportunistic Feedback

An opportunistic feedback protocol is proposed for multiuser diversity systems with proportional fair scheduling and maximum-throughput scheduling. The uplink feedback period is divided into two subperiods and users randomly access the feedback minislots according to their scheduling metric. An integrated analytical model is developed for evaluating the proposed feedback protocol.

Wireless sensor networks scheduling for full angle coverage

Many current studies in tracking and surveillance assume that a target can be monitored by a single sensor. However, there are situations where a sensor can only monitor a certain portion of the object. Examples include image capturing and coastline monitoring. In our previous work, we develop the Minimum Cost Cover algorithm to identify a set of sensors which preserve 360° coverage of a target with minimum cost, such that when different cost functions for the sensors are used, covers with different optimization objectives can be identified. In this work, we study the scheduling problem to monitor a target continuously with full angle coverage. To increase network lifetime, we develop several algorithms by adopting different cost functions in selecting the sensors. We evaluate the performance of our schemes through extensive simulations. The simulation results show that our proposed Conditional Scheduling metric can help to improve the network lifetime as well as the time to the first node failure.

Power-Saving Schedulers for a WLAN With Task-Linking Topology Awareness

This paper proposes task-scheduling algorithms designed to save energy in ad hoc networks by using a novel concept of as the scheduling metric. This notion of affinity is used to represent the task-linking topology. For one-hop ad hoc networks, we define three types of affinity measures: node affinity, task affinity, and weighted affinity. Node affinity is defined as the weighted sum of incoming and outgoing task flows at each node, and task affinity as the sum of node affinities of source and destination nodes for each task. Based on these affinity measures, we consider three heuristic scheduling algorithms that are executable in real time: 1) affinity-first shortest task scheduling Qat; 2) task-size-first affinity-based scheduling Qta; and 3) task-size weighted-affinity scheduling Qwa . These algorithms have the advantage of making the total duration of the doze state of all the nodes in the network longer than in other conventional schemes, thereby achieving significant power saving (PS). Our simulation experiments suggest that the PS performance of Qwa is comparable to optimal scheduling (which is not implementable in practice for realistic link topologies). We also present a framework for a PS medium-access-control (MAC) protocol in which our scheduling algorithm can be deployed, and we describe how to enhance the existing 802.11 PS MAC to implement our framework. Simulation results demonstrate that our enhanced PS MAC protocol combined with affinity-based scheduling improves power efficiency and throughput significantly over other existing schemes