Feasibility Of Schedule Research Articles

Proactive scheduling of steelmaking-continuous casting with uncertain processing times under carbon emission reduction

To solve the proactive scheduling problem of steelmaking-continuous casting with uncertain processing time, a fuzzy scheduling model is established to minimize the total completion time, maximum elapsed time, and carbon emission, considering machine energy consumption. An improved multi-objective beluga whale optimization algorithm (MOBWO-VNS) is proposed to solve it. In MOBWO-VNS, the process-machine matching rule is used to enhance the quality of initial solutions, an adaptive layered evolution strategy is designed to improve the convergence speed, and a variable neighborhood search strategy is introduced to strengthen the algorithm's local search capability. The diversity and convergence of the algorithm are verified by the benchmark function. A comparison experiment between deterministic scheduling and fuzzy scheduling is carried out using the actual production data of a steel plant, and the feasibility and robustness of fuzzy scheduling are verified. Large-scale scheduling evaluation experiments are designed using large-scale data, and the results show that the proposed model and algorithm reduce the total completion time, maximum elapsed time, and carbon emissions by 7.6 %, 19.2 %, and 2.2 %, respectively. Results show that this study can optimize the time and energy consumption targets, improve the production efficiency of enterprises, and provide strong support for the green transformation of steel enterprises.

NASA’s Top Human System Research and Technology Needs for Mars

NASA is working with industry and international partners to return humans to the Moon and to eventually enable humans to explore Mars. Within NASA, several organizations work together to identify, prioritize, fund, execute, and operationalize the research and technology development (R&TD) that will be necessary to enable crew health and performance (CHP) during these future missions. These organizations include flight programs, the Health and Medical Technical Authority (HMTA), the Human Research Program, the Space Technology Mission Directorate, System Capability Leadership Teams, and other organizations, many of which existed for several years prior to the creation of the Moon-to-Mars (M2M) Program Office in 2023. A variety of constructs, vocabularies, and processes exist for managing risks and supporting strategic planning across these organizations. For example, M2M objectives, program risks, human system risks, human research gaps, capability gaps, and envisioned futures are all constructs currently used within NASA to identify and prioritize R&TD needs. These strategic planning constructs are evolving to allow M2M objectives and R&TD investments to be aligned and traced at a detailed level. A recognized need exists among stakeholder organizations to identify and communicate the highest CHP R&TD priorities in a unified and digestible way that addresses the perspectives of NASA’s CHP community. To achieve this, the HMTA arranged a series of discussions with representatives of NASA’s CHP community, during which the 8 highest priority CHP capabilities that will enable human missions to Mars, referred to as the “top human system capability needs for Mars”, were identified. The list includes Earth-independent human operations; Mars-duration food system; Mars-duration effects on human physiology; risk mitigations for vehicle atmospheres; computational injury and anthropometric models; exploration exercise countermeasures; individual variability in responses to spaceflight; and sensorimotor countermeasures. Existing tools and processes for strategic planning and risk management were evaluated, as well as the technical practicalities, cost, and schedule feasibility associated with potential R&TD investments in different capability need areas. This capability needs report is not owned by any one NASA organization and does not replace existing strategic or program planning processes; rather it aims to complement and inform them with a unified set of community generated priorities. These top capability needs will be re-evaluated periodically based on R&TD progress and the evolving M2M architecture.

Exploring the feasibility of preoperative tumor-bed boost, oncoplastic surgery, and adjuvant radiotherapy schedule in early-stage breast cancer: A phase II clinical trial.

Oncoplastic breast-conserving surgery (OBCS) improves satisfaction in patients who would fare otherwise sub-optimal cosmetic outcome, while brings challenge in tumor-bed identification during adjuvant radiotherapy. The ultra-hypofractionated breast radiotherapy further shortens treatment sessions from moderately hypofractionated regimens. To circumscribe the difficulty in tumor-bed contouring and the additional toxicity from larger boost volumes, we propose to move forward the boost session preoperatively from the adjuvant radiation part. Thus, the present study aims to evaluate the feasibility of a new treatment paradigm of preoperative primary-tumor boost before breast-conserving surgery (BCS) or OBCS followed by adjuvant ultra-hypofractionated whole-breast irradiation (u-WBRT) for patients with early-stage breast cancer. There was a phase II study. Patients younger than 55 years old, with a biopsy confirmed mono-centric breast cancer, without lymph node involvement were enrolled. Preoperative primary-tumor boost was given by a single 10Gy in 1 fraction, and BCS or OBCS was conducted within two weeks afterwards. Adjuvant u-WBRT (26Gy/5.2Gy/5f) was given in 6 weeks postoperatively without any boost, after the full recovery from surgery. Surgical complications and patient-reported outcomes, as assessed via Breast-Q questionnaires, were documented. A propensity score matching approach was employed to identify a control group at a 1:1 ratio for BREAST-Q outcomes comparison. From May 2022 to September 2023, 36 patients were prospectively enrolled. Surgical complications were observed in 7 cases (19.4%), including 3 cases with Clavien-Dindo (CD) grade 1-2 and 4 cases with CD grade 3 complications. All but four patients (11.1%) started the planned u-WBRT within one week after the pre-defined due dates postoperatively (≤49d). Four patients (11.1%) developed grade 2 radiodermatitis after chemotherapy initiation. Compared to the study group, the control patients reported higher scores in chest physical well-being (P=0.045) and in their attitudes towards arm swelling (P=0.01). No significant difference was detected in the other of domains (Satisfaction with Breasts, Sexual and Psychosocial Well-Being, and Adverse Effects of Radiation). With a median follow-up period of 9.8 months (2.4-18.9mo), none had any sign of relapse. This Phase II clinical trial confirmed the technical and safety feasibility of novel radiation schedule in patients undergoing BCS or OBCS. According to the BREAST-Q questionnaire, patients who underwent novel radiation schedules reported lower satisfaction in chest physical well-being. A randomized controlled trial is necessary to further investigate these findings. Additionally, long-term follow-up is required to assess oncological outcomes.

Numerical Simulation as a Tool for the Study, Development, and Optimization of Rolling Processes: A Review

Rolling is one of the most important processes in the metallurgical industry due to its versatility. Despite its inherent advantages, design and manufacturing by rolling still rely on trial-and-error-based optimizations, which reduces its efficiency. To minimize the cost and time spent on the development of new rolling schedules, various analytical and numerical methods have been used in recent years. Among other alternatives, simulations based on the finite element method (FEM) are the most widely used. This allows for the analysis of the feasibility of new rolling schedules considering metal alloys with different characteristics, process conditions, or the creation of new operations, as well as the optimization of existing ones. This paper presents a literature review including the latest developments in the field of numerical simulation of rolling processes, which have been classified according to the type of rolling into the following categories: flat rolling, shape rolling, ring rolling, cross-wedge rolling, skew rolling, and tube piercing.

Abstract 6512: BPI-501836: A highly potent small molecule inhibitor targeting KRASG12D mutation

Abstract As one of the most notorious un-druggable oncogenes, mutant KRAS impairs the transition of RAS from its active GTP-binding form to the inactive GDP-binding form, leading to sustained activation of downstream MAPK pathway and ultimately tumorigenesis. The recent approved KRASG12C inhibitors have demonstrated clinical benefits in KARSG12C mutant patients. However, the other prevalent KRAS mutations, such as KRASG12D, remain a huge unmet medical challenge. There’s no approved KRASG12D specific inhibitors yet. Here we present BPI-501836, a highly potent and selective small molecular inhibitor for KRASG12D. In vitro, BPI-501836 exhibited very high potency by disrupting the KRASG12D and cRAF interaction in a cell-free assay. In cell-based assays, BPI-501836 inhibited proliferation of KRASG12D mutant cell lines with sub-nanomolar IC50 values, without affecting wide type or non-G12D KRAS mutant cell lines. Moreover, it also attenuated ERK phosphorylation in KRASG12D mutant cells. In vivo, tumor regression was observed following the administration of 6 mg/kg (iv, QW) BPI-501836 in KRASG12D human pancreatic Panc04.03 xenograft mouse models, or in the KRASG12D human pancreatic PK-59 xenograft mouse models with an intraperitoneal injection of 20 mg/kg per week. Noteworthy was the prolonged half-life of BPI-501836 observed across various species, coupled with its high distribution in tumor tissues. Moreover, sustained suppression of ERK phosphorylation in tumor tissues persisted for up to 144 hours after the last dose of BPI-501836 in both xenograft mouse models. This extended pharmacodynamic effect supports the feasibility of a once-weekly dosing schedule in clinical applications. Safety assessments conducted through a 4-week repeated infusion (intravenous, twice weekly) of BPI-501836 in beagle dog demonstrated favorable safety profiles. In conclusion, BPI-501836 emerges as a highly potent and selective KRASG12D inhibitor, exhibiting promising pharmacokinetics, pharmacodynamics, and safety profiles in pre-clinicalstudies. BPI-501836 is currently in IND-enabling stage and holds potential for further development as a therapeutic agent. Citation Format: Xiang Yang, Yuan Lu, Renqi Xu, Xiaoqiang Kong, Yongqing Sun, Xingjie Zhu, Xiaoyun Liu, Zhengyao Zou, Haibo Chen, Yanju Liu, Jialing Sun, Han Han, Cheng Yang, Jing Guo, Hong Lan, Quan Zhou, Lieming Ding, Hao Wu, Jiabing Wang. BPI-501836: A highly potent small molecule inhibitor targeting KRASG12D mutation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6512.

New evidence for flexible psilocybin dosing in patients with treatment-resistant depression

Psilocybin has demonstrated efficacy for treating depression; however, psychiatrically complex patients have been excluded from trials. A recent clinical trial by Rosenblat at al.1 demonstrates feasibility of a flexible dosing schedule of psilocybin in individuals with severely treatment-resistant depression (TRD), including those with co-morbid conditions or bipolar II disorder (BPII), potentially expanding the current treatment paradigm.

Feasibility, effectiveness, and acceptability of an afternoon-evening sleep schedule in older nightshift workers.

To explore the feasibility, effectiveness, and acceptability of an afternoon-evening sleep schedule in older (age 50-65 years) nightshift workers. We used a three-part strategy: a screening survey to identify individuals who said they could adopt an 8-hour afternoon-evening sleep schedule; a field study where daily diary and actigraphy data were collected during a baseline week and intervention week, with randomization to self-selected sleep, 8-hour afternoon-evening time in bed (TIB), or 8-hour self-selected TIB; and follow-up focus groups to understand the acceptability of the intervention. Gender (p < 0.001), Hispanic ethnicity (p = 0.023), the care of children (p = 0.014), and chronotype (p = 0.012), predicted the reported ability to spend 8 hours in bed in the afternoon-evening. Participants assigned to the 8-hour self-selected and 8-hour afternoon-evening groups significantly increased their TIB and sleep duration compared to baseline (p < 0.05), while the control group did not. Although spending 8 hours in bed was feasible for the participants during the study, focus group discussions indicated participants would not continue an 8-hour TIB schedule after the study due to family responsibilities and other activities of daily living. Spending 8 hours in bed between successive night shifts, initiated at both a self-selected time and in the afternoon-evening, increased the sleep duration of older shiftworkers, but most would not continue such a schedule on their own. Additional research is needed to find countermeasures for the reduced sleep duration experienced by most shiftworkers that are not only effective, but also compatible with shiftworkers' lifestyles.

Scheduling‐location problem with drones

AbstractDrone operation, a new driving force for logistics innovation, is struggling to overcome practical challenges. One of the concerns for drone utilization is limited flight ranges, and different concepts of facilities are continually developed to support drone delivery. These new facilities prompt the need to integrate decision‐making across different phases. In particular, the deployment of facilities that complement the physical limitations of drones and the scheduling of drones to perform delivery tasks are closely related. Therefore, we developed a scheduling‐location problem with drones, a new methodology for integrating operational and strategic planning decisions. The integrated decision‐making determines the location of the drone facilities by not only considering the critical distance of facilities but also by taking into account whether the delivery schedule is implemented. In our model, additional drone facilities are sometimes opened considering available drones due to the feasibility of the delivery schedule. An extended formulation and a restricted master heuristic are proposed to solve problems time‐efficiently. Computational results show that the restricted master heuristic outperforms the mathematical model in finding solutions for large‐scale instances. The developed model and heuristic algorithm provide drone delivery services even in areas that are not easily reachable by drones due to being located far from the warehouse and can be effectively applied to humanitarian logistics.

A genetic algorithm for the personnel task rescheduling problem with time preemption

In this paper, we study the personnel task rescheduling problem with time preemption. We assume a baseline task schedule has been composed some time in advance that has been rendered infeasible due to operational uncertainty. When restoring the schedule feasibility, the personnel planner takes several considerations into account encompassing the provided service level, resource stability, and task adherence. The latter stems from the possibility that in the problem under study tasks can be interrupted and resumed later, better known as time preemption. We propose a genetic algorithm complemented with local search techniques to compose an operational schedule in an effective manner. The genetic algorithm operates on a dedicated decoding mechanism to compose high-quality duties for individual workers efficiently. Computational experiments are conducted to benchmark the performance of the proposed procedure with other exact and (meta-)heuristic solution procedures, and to demonstrate the value of the algorithm design choices. Furthermore, the impact of preempting tasks in time is assessed by changing the objective function structure and the proportion of non-preemptive tasks.

Fast and Robust Resource-Constrained Scheduling with Graph Neural Networks

Resource-Constrained Project Scheduling Problems (RCPSPs) are NP-complete, which makes it challenging to efficiently solve large instances and robustify solutions in the presence of uncertainty. To remedy this, we learn to efficiently mimic the solutions produced by Constraint Programming (CP) solver, using a Graph Neural Network (GNN) architecture designed to capture the structure of RCPSPs. Since the GNN solution may violate constraints, we ensure schedule feasibility at inference time by extracting the task ordering from the GNN schedule and post-processing it with the well-known Schedule Generation Scheme (SGS). We find that SIREN, the resulting algorithm, produces schedules that are of higher quality than those produced by the CP solver within the same computation time budget. The speed and solution quality of SIREN make it suitable as a component of an on-line scenario-based optimisation procedure for RCPSPs with stochastic durations. This leads to the SERENE system, which robustly selects, in real-time, the best next tasks to start in order to minimise the average makespan over the scenarios. Empirically, SERENE achieves better average makespan over different realisations of uncertainty than deterministic algorithms that continuously reschedule on the basis of either the worst, best or average task durations.

Swarm Intelligence-Based Task Scheduling for Enhancing Security for IoT Devices

Due to the great advancement in computation, communication and control technologies, the Internet of Things (IoT) can provide ubiquitous connectivity for anyone and anything at any time and any place, leading to a revolution in information society. Protecting devices against various security threats is one of the most important challenges in IoT since IoT applications are generally security-critical systems while IoT devices are often poorly secured. For IoT devices, employing security services provided by smart gateways or edge/cloud servers to defend against various threats is an effective way to enhance their security. However, the finite battery energy of devices and the limited fund of device users hinder the wide application of security services in IoT. This necessitates the demand for designing new methodologies to tackle the trade-off among security, energy and fund of IoT devices. Therefore, this paper attempts to optimize system security of IoT devices under energy and fund constraints. Specifically, to formulate the energy and fund constrained security optimization problem, we firstly propose a pricing model for the security services provided by the smart gateway. We then formulate the problem as a mixed-integer linear programming (MILP) problem. Since using a solver to address the MILP problem may be time-consuming, we leverage the swarm intelligence technique to design a new task scheduling scheme that can efficiently solve the optimization problem. Extensive experiments are conducted to validate our proposed MILP and swarm intelligence based task scheduling algorithms. Simulation results show that our scheme outperforms two state-of-the-art methods in improving system quality of security and guaranteeing schedule feasibility.

A Privacy-Preserving Ride Matching Scheme for Ride Sharing Services in a Hot Spot Area

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads to privacy disclosure, e.g., the user’s location and trajectory. Many privacy-preserving solutions for ride sharing services have been proposed, which are based on cryptographic technology and provide accurate matching services. However, these encryption-based algorithms are very complicated and difficult to calculate. In hot spots, such as stations, airports and sport gymnasiums, a large number of users may apply for a ride sharing service in short space of time, which will place huge pressure on the service provider. Using traditional matching methods increases the matching time and leads to a less favorable user experience. To solve these problems, we model them, aiming to maximize the vehicle’s carrying capacity and propose a lightweight privacy-preserving ride matching scheme for selecting feasible partners during busy periods with a large number of requests. To achieve this, we make use of the homomorphic encryption technique to hide location data and design a scheme to calculate the distances between users in road networks securely and efficiently. We employ a road network embedding technique to calculate the distance between users. Moreover, we use travel time instead of space distance, which makes matching more accurate. Further, with the encrypted itineraries of users, the service provider selects potential ride share partners according to the feasibility of time schedules. We use ciphertext packing to reduce overhead, improving the efficiency of ride matching. Finally, we evaluate our scheme with simulation and demonstrate that our scheme achieves an efficient and accurate matching service. It only takes a few seconds to complete the matching, and the matching accuracy is higher than 85 percent in most cases.

Sensors Scheduling for Remote State Estimation Over an Unslotted CSMA/CA Channel

This article investigates the sensor selection problem for remote state estimation. A set of sensors measures a process's state and sends the measurements to a remote estimator over an unslotted carrier sense multiple access/collision avoidance (CSMA/CA) channel in IEEE 802.15.4-based wireless body area networks. This article aims to answer which sensors should be selected according to probability so that the state estimation error covariance and the energy consumption of sensors can be optimized. A common assumption in most existing methods about sensor scheduling is that the packet loss process is unrelated to the channel input. This article analyzes the relationship between channel input and packet loss rate by modeling the communication process. A scheduling scheme is proposed to increase channel utilization from the perspective of sensor information fusion. Then the constraint on the performance metrics is presented. By transforming the sensor selection problem into a constrained optimization problem, the solution to the optimization problem is obtained by a numerical algorithm. Finally, the validity of the model and the feasibility of the sensor scheduling are demonstrated by a numerical simulation.

OTOMATISASI RISET KATA KUNCI

Keyword research is market research for the 21st century. Success in choosing the right and relevant keywords will make the visibility of online stores and selling products better. Keyword research is needed on all social media platforms so it requires repetition of keyword research for quite a long time because you have to open one by one the existing media platforms. Therefore a tool or tools are needed to conduct effective keyword research by combining the process of searching for keywords in one tool. The scope of this research is to create tools based on the iMacross script to increase effectiveness and efficiency in keyword searches on Google, YouTube, Facebook, Facebook Marketplace and Instagram. Research uses the Waterfall system development model, system analysis uses SWOT and Feasibility analysis uses TELOS analysis (Economic, Legal, Operational and Schedule feasibility). The results of the questionnaire (questionnaire) for system users with questions referring to the TELOS Feasibility Study show a feasibility level of 78.64%. This percentage illustrates that the system is feasible to use, easy to learn, easy to understand and effective in helping internet marketers in keyword research.Keywords: keywords, iMacros, Waterfall, SWOT, TELOS, Instagram.

Messaging preferences among Florida caregivers participating in focus groups who had not yet accepted the HPV vaccine for their 11- to 12-year-old child

BackgroundIn the United States, human papillomavirus (HPV) vaccination rates remain low. The President’s Cancer Panel suggests that effective messaging about the HPV vaccination focus on the vaccine’s safety, efficacy, ability to prevent cancer, and recommendation at ages 11- to 12-years. We aimed to develop messages about HPV vaccine that include the President Cancer Panel’s suggestions and were acceptable to caregivers of adolescents.MethodsFrom August to October 2020, we conducted one-hour, Zoom videoconference focus groups with caregivers who lived in Florida, had an 11- to 12-year-old child, and had not had any of their children receive the HPV vaccine. Focus group moderators asked caregivers to react to three videos of clinician (i.e., MD, DO, APRN, PA) recommendations and three text message reminders. Thematic analysis was conducted using the constant comparative method and led by one author with qualitative analysis expertise. Two additional authors validated findings.ResultsCaregivers (n = 25 in six groups) were primarily non-Hispanic white (84%) and educated (64% had at least an Associate’s degree). Approximately a third of caregivers had delayed (44%) or decided against a vaccine for their child (36%). Caregivers described six preferred message approaches: recognize caregivers’ autonomy, balanced benefits and risks, trustworthy sources, increased feasibility of appointment scheduling, information prior to decision point, and preferred personalized information. Caregivers expressed a desire to have the follow-up doses mentioned in the introduction.ConclusionsHPV vaccine messages, whether delivered by a clinician or via text message, will be more acceptable to caregivers if they approach HPV vaccination as the caregivers’ decision, and include information from trusted sources to help caregivers make an informed choice.

Theoretical Analysis of an Adaptive Periodic Multi Installment Scheduling With Result Retrieval for SAR Image Processing

Processing a large-scale Synthetic Aperture Radar (SAR) image dataset on a distributed computing infrastructure poses a challenging problem. Large-scale load distribution strategies like multi-installment scheduling (MIS) assume that the size of the result is negligible compared to the input workloads and hence ignore it in their design. Similarly, numerical methods like particle swarm optimization and their variants are not practical for real-time applications, given their run-time complexities. As both the results retrieval and completion time are crucial for SAR image data processing, in this article, we attempt to provide a thorough theoretical analysis of an adaptive MIS that includes the result retrieval phase. We use the periodic nature of the internal installments to keep the strategy simple and fine-tune the last installment to avoid any idle times in the processors. We derive a closed-form solution for the load fractions and hence, the overall processing time, schedule feasibility criteria, and certain other properties that lead to adaptive scheduling. Finally, we validate our theoretical findings through rigorous simulation studies using a loosely connected virtual machines (VMs) topology for the SAR dataset.

One-Year Toxicity of Ultrahypofractionated Breast Radiotherapy (+/- Sequential Boost) and a Survey of Patient Experience

Purpose/Objective(s)In March 2020, a 1-week hypofractionated adjuvant breast radiotherapy schedule, 26 Gy in 5 fractions, was implemented to reduce the risk of COVID 19 virus for patients and staff without compromising on cancer outcomes. This prospective observational study aims to report late toxicities at 1-year and to confirm feasibility and acceptability of this new schedule for patients.Materials/Methods– Patient reported outcome measures were recorded at baseline, 3 months, 6 months and 1 year following treatment completion. The presence of breast pain, swelling, hypersensitivity and skin problems, arm or shoulder pain, restricted arm movement and arm swelling were recorded using the EORTC QLQ BR45. Virtual teleconferencing without video was utilized at baseline, 3 months, 6 months and 1-year. Patients were invited to use video at the 1-year assessment for a physician-based assessment. To assess the experience of patients, a survey including 12 questions related to understanding, experiences of and benefits of ultrahypofractionated radiotherapy was developed and patients were invited to participate at the 1-year consultation.ResultsIn total, 135 consecutive patients were enrolled Mar - Aug 2020. 121/135 (90%) patients completed the 1-year toxicity assessment and at least one other assessment. 30/121 (25%) patients received a sequential boost. The majority of patients reported no toxicity or a mild toxicity at all three time points, 76% (83/109) at 3 months, 76% (82/108) at 6 months, and 82% (99/121) at 1-year. At 1 year 17% (20/121) of patients reported a moderate toxicity, the most common being breast pain (6/121). 2% (2/121) of patients reported a marked toxicity, both reported multiple marked toxicities including appearance of breast, firmness and skin changes. For the 30 patients who received a boost a moderate toxicity was reported by 17% (5/30) at 1 year and 3% (1/30) reported a marked toxicity. There was one confirmed case of ipsilateral recurrence at 1-year. Only 30/121 patients were open to using video for the 1-year physician-based assessment. Regarding patient reported experience 90% (101/112) felt informed about the 1-week radiotherapy schedule, and 88% (99/112) felt informed about possible side effects and 94% (105/122) felt supported by the medical team throughout their treatment course. Only 49% (55/112) reported they would have been open to video consultations.ConclusionMost patients undergoing ultrahypofractionated breast radiotherapy experience no toxicities or mild toxicities. These data suggest that adding a sequential boost following 26 Gy in 5 fractions to the whole breast is safe and does not lead to unacceptable 1-year toxicity. Patient satisfaction with ultrahypofractionated treatment and virtual consultations without video was high. However, less than half of patients may be open to virtual consultations and breast examination using video.

Research on Key Technology of Edge-Node Resource Scheduling Based on Linear Programming

With the arrival of the 5G era, centralized cloud computing is gradually shifting to edge computing. However, due to the characteristics of distributed multi-deployment of edge computing, it is difficult to ensure that all edge-node clusters have enough resources. Faced with the limited resources of edge-node cluster, how to carry out reasonable resource scheduling and provide users with high-quality services (high quality and low delay) has become the focus of current research. Aiming at this problem, in this paper, the edge-node resource scheduling based on linear programming is the key technology being studied. It is compared with conventional algorithm-based edge-node resource scheduling system. In the five deployments of the edge node resource scheduling system under the conventional algorithm, 8 virtual machines are used. The edge node resource scheduling system under the linear programming algorithm uses 7 virtual machines for 3 deployments and 8 virtual machines for 2 deployments. It can be seen that the results affirm the feasibility of edge node resource scheduling based on linear programming, and make due contributions to current research.

Data-based Approach to Predict Feasibility and Computational Requirement for Chemical Production Scheduling

Online scheduling requires frequent re-optimization to generate a schedule repeatedly accounting for updated information. However, if the time between re-optimizations is too short, then finding good, and in some cases even feasible, solutions can become challenging. This work proposed an approach, based on supervised learning techniques, to predict whether a given instance is feasible and, given that it is feasible, what is the computational requirement to solve the instance. Towards this goal, we introduce various types of features related to problem size, scheduling horizon, and processing times and costs that can be derived based on domain knowledge. Logistic regression and random forests models are trained as feasibility classifier and computational time regressor, respectively, using the dataset obtained from a wide variety of instances. Both show good predictive performances: F1 score ∼0.90 and AUC ∼0.98 for the feasibility classification and MSE ∼0.5 for the computational time prediction. Finally, we discuss the features that are shown to be significant in the cases of makespan minimization and cost minimization.

Technical Options and Cost Estimates for Spent Nuclear Fuel Management at the Barakah Nuclear Power Plants

In the planning and management of the interim storage of spent nuclear fuel, the technical and economic parameters that are involved have a significant role in increasing the efficiency of the storage system. Optimal parameters will reduce the total economic costs for countries embarking on nuclear energy, such as the UAE. This study evaluated the design performance and economic feasibility of various structures and schedules, to determine an optimal combination of parameters for the management of spent nuclear fuel. With the introduction of various storage technology arrangements and expected costs per unit for the storage system design, we evaluated eight major scenarios, each with a cost analysis based on technological and economic issues. We executed a number of calculations based on the use of these storage technologies, and considered their investment costs. These calculations, which were aligned with the net present value approach and conducted using MS Project and MATLAB software programs, considered the capacities of the spent fuel pools and the amount of spent nuclear fuel (SNF) that will be transferred to dry storage facilities. As soon as they sufficiently cool, the spent nuclear fuel is to be stored in a pool storage facility. The results show that applying a centralized dry storage (CDS) system strategy is not an economically feasible solution, compared with using a permanent disposal facility (PDF) (unless the variable investment cost is reduced or changed). The optimal strategy involves operating a spent fuel pool island (SFPI) storage after the first 20 years of the start of the permanent shutdown of the reactor. After 20 years, the spent fuel is then transferred to a PDF. This strategy also results in a 20.9% to 26.1% reduction in the total cost compared with those of the other strategies. The total cost of the proposed strategy is approximately 4,307 million USD. The duration of the fuel storage and the investment cost, particularly the variable investment cost, directly affect the choice of facility storage.