Uncertainty In Scheduling Research Articles

Evaluating the Impact of the Level of Robustness in Operating Room Scheduling Problems.

Managing uncertainty in surgery times presents a critical challenge in operating room (OR) scheduling, as it can have a significant impact on patient care and hospital efficiency. Objectives: By incorporating robustness into the decision-making process, we can provide a more reliable and adaptive solution compared to traditional deterministic approaches. Materials and methods: In this paper, we consider a cardinality-constrained robust optimization model for OR scheduling, addressing uncertain surgery durations. By accounting for patient waiting times, urgency levels and delay penalties in the objective function, our model aims to optimise patient-centred outcomes while ensuring operational resilience. However, to achieve an appropriate balance between resilience and robustness cost, the robustness level must be carefully tuned. In this paper, we conduct a comprehensive analysis of the model's performance, assessing its sensitivity to robustness levels and its ability to handle different uncertainty scenarios. Results: Our results show significant improvements in patient outcomes, including reduced waiting times, fewer missed surgeries and improved prioritisation of urgent cases. Key contributions of this research include an evaluation of the representativeness and performance of the patient-centred objective function, a comprehensive analysis of the impact of robustness parameters on OR scheduling performance, and insights into the impact of different robustness levels. Conclusions: This research offers healthcare providers a pathway to increase operational efficiency, improve patient satisfaction, and mitigate the negative effects of uncertainty in OR scheduling.

Digital twin-driven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies.

Non-standard Working Practices and Nurses’ Job Performance: Experience from Public Hospitals in Tanzania

Non-standard working practices are common among nurses because they spend more time directly caring for patients than other healthcare professionals. Thus, nurses’ performance is impacted by how work is organized, whereas various component of the working practice may lead to a decrease in performance. The purpose of this study was to examine the influence of non-standard working practices on performance of nurses in public hospitals in Tanzania. Specifically, the study focused on assessing the influence of schedule variation, schedule uncertainty, and schedule intensity on performance of nurses in Tanzanian public hospitals. The study adopted descriptive survey research design using qualitative and quantitative methods. Stratified random sampling technique was used, whereas a sample size of 381 nurses was randomly chosen from a target population of 404. Quantitative data were analyzed through thematic analysis technique whereas qualitative data were analyzed by the use of multiple linear regression, one-way Analysis of Variance, independent t-test, and Pearson correlation. Qualitative data were entered, coded and analyzed using summative content analysis and presented in percentages, in tables, figures and charts. Correlation analysis was used to test the direction of relationship between the independent variables and dependent variable. Multiple regressions were used to test whether schedule variation, schedule uncertainty, and schedule intensity had any influence on nurses’ performance. The study established that schedule variation, schedule uncertainty, and schedule intensity contributed negatively to nurses’ performance. Furthermore, the study approved all three study hypothesis as it was found that all schedule variation, schedule uncertainty and schedule intensity had significant statistical influence on job performance of nurses. The study concluded that exposure to non-standard working practices were common among nurses in public hospitals in Tanzania. Further, the study concluded that nurses in the study area were more affected by schedule variation and schedule uncertainty. The study recommended the adoption of strategies aimed at minimizing the effects non-standard schedules to promote nurses’ performance.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern-based predictions of the distribution of item sizes in online bin packing. In comparison to traditional heuristics like BestFit(BF) and FirstFit(FF), as well as the more recent PatternPack(PaP) and ProfilePacking(PrP) algorithm based on online predictions, FPP demonstrates competitive and superior performance in solving various benchmark problems. Particularly, it excels in addressing problems with evolving distributions, making it a promising solution for real-world applications where the item sizes may change over time. This research unveils the promising potential of employing fuzzy logic to effectively address uncertainty in scheduling and planning problems.

Incentive-based coordination for scheduled delivery in prefab construction

An increasing number of projects are adopting prefabrication to economize on time, labor, and materials in fixed-position layout operations, such as construction, ship building, and aircraft manufacturing. In such contexts, independent contractor and fabricator make interdependent decisions, which calls for prudent supply chain management because performance relies on coordination between their decisions. Many studies have developed integrated systems and propose various algorithms for scheduling efficiency and reliability. Nevertheless, they pay scant attention to conflicting interests amongst independent partners, which may result in subpar performance not only for the supplier but for the contractor as well. Coordination of conflicting interests has been extensively studied in economics and supply chain management; yet, those studies focus on order-quantity decisions under demand uncertainty for profit maximization, while managers in fixed-position operations are more concerned about delivery decisions under scheduling uncertainty for cost minimization. We consider the case of construction and explore a contractual scheme that aligns the agents’ decisions for coordination in a construction supply chain. Specifically, we propose a supplier rebate for coordination: the supplier grants a rebate if the contractor accepts the shipment in accordance with the delivery schedule that the contractor initially chose. We show that the optimal rebate fully coordinates the supply chain to minimize the joint supply chain costs. Thus, both the contractor and supplier benefit from the coordination by negotiating a mutually acceptable way to allocate the savings in joint costs between them. We further show that the rebate motivates the contractor to enhance its work scheduling.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining. But uncertainties, including uncertain demands of crude distillation units (CDUs), might make the production plans made by the traditional deterministic optimization models infeasible. A data-driven Wasserstein distributionally robust chance-constrained (WDRCC) optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling. First, a new deterministic crude oil scheduling optimization model is developed as the basis of this approach. The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands. A cross-validation method is advanced to choose suitable radii for these ambiguity sets. The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets. The proposed WDRCC model is transferred into an equivalent conditional value-at-risk (CVaR) representation and further derived as a mixed-integer nonlinear programming (MINLP) counterpart. Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method. Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

Evaluation of the Train Drivers Mental Workload to Increase Well-being

Indonesia's logistics system is crucial for economic growth, with high costs impacting competitiveness and the economy. Transportation is the most dominant component, ensuring mobility and impacting national development. PT Angkutan Barang (PT AB)provides efficient railway transportation services. Despite a 70% drop in passenger transportation revenue during the COVID-19 pandemic, freight transportation increased by 18.3% in 2021, supporting the company's performance during a crisis. The company's increased coal transportation targets require more train drivers, for smooth operation. However, strict training and tight schedules can disrupt train driver’s fulfillment and increase the likelihood of overtime. Long working hours can cause fatigue and stress and increase the risk of accidents, impacting the company's operations and causing significant losses. Thus, train drivers play a crucial role at PT AB, and it is important to assess their mental workload to ensure their mental health using the RNASA-TLX method. The study examines the mental workload of 181 train drivers at Tanjungenim Freight Train Drivers Unit, finding an average score of 61.03, classified as "Moderate". Freight train drivers' mental workload is significantly influenced by visual, temporal, auditory, and mental demands, with schedule uncertainty and unique work environments being the most influential factors. Train driver's mental workload varies, with 52 having a low workload, affecting boredom, productivity, and human error, and 42 having a high workload, potentially requiring stress management training.

Barriers to vaccination among older adults: Demographic variation and links to vaccine acceptance

BackgroundDespite risks of vaccine-preventable infectious diseases, many older adults are under vaccinated. The present study investigates possible barriers to acceptance of vaccines recommended for older adults and considers demographic variation and links to acceptance for distinct vaccines. MethodsA mailed survey was conducted with 901 older adults (aged 65+) across the state of North Dakota, oversampling for rural regions. The survey addressed demographic characteristics, barriers to vaccination (including needle phobia, cost, lacking doctor recommendation, transportation, and scheduling uncertainty), and vaccine acceptance (including provider guidance acceptance and uptake for influenza, shingles, pneumococcal, and COVID-19 vaccines). ResultsCost (27.2 %) and needle phobia (24.1 %) were the most prevalent barriers. Rural, lower educated, non-white, and living alone older adults experienced increased risks of barriers such as cost, lacking a doctor recommendation, transportation, and uncertainty how to schedule. Cost was associated with shingles vaccine uptake, whereas lacking doctor recommendation was associated with uptake for all vaccines except shingles. Needle phobia was associated with uptake of all vaccines except COVID-19. Transportation barrier was only associated with pneumococcal vaccine uptake. ConclusionsThese findings highlight relatively low prevalence of barriers, yet variability by older adults’ demographic characteristics. Further, the influence of barriers on acceptance varied by vaccine type. By better understanding the nuanced role of barriers to older adults’ vaccination, strategies to increase vaccination rates can be developed.

A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

Flat Dose Intravenous Immunoglobulin Primary Infection Prophylaxis in Multiple Myeloma Patients on Bispecific Antibody Therapy: Vanderbilt Experience

BACKGROUND Bispecific antibodies (BsAbs) are novel immunotherapeutic agents with prolific anti-multiple myeloma (MM) efficacy in highly relapsed/refractory disease (RRMM). They have been associated with profound and prolonged hypogammaglobinemia which represents a unique challenge regarding increased risk of serious (sometimes fatal) infections. Though IVIG has historically been dosed at 0.4g/kg for secondary prophylaxis, there is still uncertainty in optimal timing, schedule and frequency of supplementation in MM patients. At Vanderbilt University Medical Center, we have adopted a flat dose 10g IVIG prophylaxis schedule, which on average delivers ~36% of historical, weight-based dosing. At present, expert recommendations advise IVIG replacement every 4 weeks in recipients of BsAb starting the second month of therapy and continuing until the end of therapy or until serum IgG levels reach > 400 mg/dL, whichever is longer ( Mohan M et al. Br J Haematol.2023;00:1-11). Considering that MM BsAbs are currently used with continued dosing schedule, adoption and anticipated new BsAb approvals are increasing, IVIG has previously been on national/global shortage (occasionally unavailable), IVIG replacement during hypogammaglobulinemia represents a crucial aspect of adequate infection prophylaxis in MM, we explored the impact of our institutional 10g IVIG prophylaxis on infection rates in RRMM patients receiving BsAbs. Prior studies with comparable BsAbs reported Grade (G) ≥3 infection rate of 32-45% and 18%, in anti-BCMA and anti-GPRC5D BsAbs, respectively. In these trials, overall, 14-53% of patients received IVIG prophylaxis. METHODS We performed a retrospective review using electronic medical record of RRMM patients receiving anti-BCMA or anti-GPRC5D BsAbs from Jan 2019 to Dec 2022, either on clinical trials or as a standard of care at our institution. The inclusion criteria were minimum of two months of BsAb therapy, and the use of at least one month of monthly IVIG prophylaxis for serum IgG < 400 mg/dL. All patients were also on baseline valacyclovir, trimethoprim-sulfamethoxazole and levofloxacin prophylaxis. Routine disease and treatment specifics as well as infection rates and grades were collected. Infections were defined as mild (infections treated outpatient), moderate (required hospital admission) or severe (hospital admission with intensive care unit level of care), next to also being graded by CTCAE v5 with the rest of hematologic and non-hematologic adverse events (AEs). RESULTS A total of 30 patients with RRMM were identified. Table 1 illustrates the demographic and disease specifics: 79% received a median (range) of 4 (2-7) lines of prior therapy, over half were penta-class exposed, 27% were triple and quad refractory, 67% received a prior autologous stem cell transplant and 17% underwent prior treatment with BCMA targeting CAR-T therapy. All except 2 patients received BsAb therapy on clinical trials. Anti-GPRC5D BsAb was used to treat 60% of patients, while the rest (40%) received anti-BCMA BsAb, all of which were given for a median of 27 (12-45) months (Table 1). Overall, 30% of patients had one infectious episode, while 43% had ≥2. Viral infections (57%) were twice as common as bacterial (30%) (Table 2). Infections were mild in 60%, moderate in 23%, and severe in 6.6% of patients. We observed 27% rate of G ≥3 infectious complications. Patients had a median (range) IgG at diagnosis of 1284(196-5431), and 499(210-2246) at the time of onset of first infection (IgG values at the start of BsAb therapy, at first and at ≥2 infections are outlined in Table 2). The rate of G ≥3 neutropenia and lymphopenia was 6.6% and 30% respectively. There were 2 (6.6%) sepsis related deaths, one each with anti-BCMA and anti-GPRC5D BsAbs. Table 2 also describes severity and the rates of cytokine release syndrome and neurotoxicity. CONCLUSIONS We observed relatively lower rate of severe or ≥ G3 infectious events with 10g flat IVIG dosing schedule. While our results represent a small, single-institutional cohort of predominantly anti-GPRC5D treated RRMM patients and require prospective validation in a randomized (IVIG 10g vs 0.4g/kg body weight) fashion, they demonstrate feasibility of reduced level dosing of a critical resource such as IVIG, in the fast expanding, national/global BsAb-based MM armamentarium.

Bi-objective Optimization for Resource-constrained Robust Construction Project Scheduling

Construction projects are often executed in complex environments, and various uncertain factors (e.g., bad weather, machine breakdown) prolong activity duration in practice, which may cause project completion delays or profit reduction. Moreover, previous studies often optimized net present value or robustness in project scheduling separately, they neglected the linkage between the value of obtaining NPV and the capacity of tackling uncertainties in a project schedule. To achieve optimal profit and enhance the stability of a project schedule simultaneously, this study proposes a bi-objective optimization for resource-constrained robust construction project scheduling problem (RCRCPSP), which aims at optimizing NPV and robustness, and builds a bi-objective optimization model for the RCRCPSP based on five typical cash flow models. Then an ε-constraints procedure embedded with a genetic algorithm is proposed to solve the model. The ε-constraints procedure performs better than the multi-objective genetic algorithm proposed by the previous study through case study. Furthermore, the findings demonstrate that a trade-off relationship exists between NPV and robustness, and cash flow models have little impact on robustness, while the relaxed deadline can improve a project schedule with high robustness. The fruits of this study can help contractors balance the NPV and the robustness in an indeterministic environment.

Uncertainties in scheduling and execution of trackwork in Sweden

Trackwork planning and scheduling are demanding because they require strategic foresight and must be completed well in advance. In Sweden, trackwork is performed by maintenance contracting companies during an operation period free from trains. In the contractors’ practice, once the maintenance plan is authorised, some unexpected events might interrupt the plan’s execution, leading to uncertainties. The purpose of this study is to identify and classify uncertainties and strategies applied to manage uncertainties in the contractors’ everyday planning and scheduling of trackwork. This work presents semi-structured interviews with foremen and planners at railway maintenance contracting companies in Sweden. The main findings show that in trackwork planning and scheduling, contractors deal with two types of uncertainties: internal and external. We categorised uncertainties and strategies to deal with uncertainties and described them on tactical and operational levels. The majority of the revealed uncertainties led to trackwork rescheduling. Furthermore, we suggest that current strategies to manage uncertainties applied at contracting companies can be improved by revising organisational design strategies for maintenance projects. This work increases the understanding and supports the management of uncertainties in trackwork planning and scheduling.

Injury prevention strategies at the 2019 FIFA Women's World Cup display a multifactorial approach and highlight subjective wellness measurements

ObjectiveTo report the injury prevention programs utilised by top-level female footballers competing internationally. MethodsAn online survey was administered to physicians of the 24 competing national teams at the 2019 Federation Internationale de Football Association (FIFA) Women's World Cup. The survey included 4 sections regarding perceptions and practices concerning non-contact injuries: (1) risk factors, (2) screening tests and monitoring tools, (3) preventative strategies, and (4) reflection on their World Cup experience. ResultsFollowing responses from 54% of teams, the most common injuries encountered included muscle strains, ankle sprains, and anterior cruciate ligament ruptures. The study also revealed the most important injury risk factors during the FIFA 2019 World Cup. Intrinsic risk factors include accumulated fatigue, previous injury, and strength endurance. Extrinsic risk factors include reduced recovery time between matches, congested match schedule, and the number of club team matches played. The 5 most used tests for risk factors were flexibility, joint mobility, fitness, balance, and strength. Monitoring tools commonly used were subjective wellness, heart rate, minutes/matches played, and daily medical screening. Specific strategies to limit the risk of an anterior cruciate ligament injury included the FIFA 11+ program and proprioception training. ConclusionThe present study revealed multifactorial approaches to injury prevention strategies for women's national football teams at the FIFA 2019 World Cup. Challenges to injury prevention program implementation reflect time limitations, schedule uncertainties, and varying club team recommendations. Level of evidenceIV.

Probabilistic recovery resilience model of mooring failure accident based on correlated schedule-uncertainty analysis

Floating production system (FPS) may face various risks during their service life, among which mooring failure is one of the most disastrous events. In order to assess and quantify the resilience of FPS under mooring failure, it is essential to use reliable restoration fragility models. But for now, there are no available specific recovery models. The main barrier is uncertainty of nature and complexity of recovery actions. This paper aims to take uncertainty of recovery schedule into account and propose recovery models for mooring failure accident. Based on literature review and a detailed questionnaire that elicits knowledge from experts, the restoration schedule and uncertainty are defined and quantified. Through correlated schedule uncertainty analysis model (CSUAM), uncertainty influence on recovery model is measured. And after Monte Carlo Simulation (MCS), Gaussian mixture model (GMM) and normal distribution model are introduced to establish probabilistic recovery model of different damage level. Recovery models are then presented and validated for a certain FPS in South China Sea, including restoration task scheduling, seasonal influence, spare availability and two-mobilizations for different damage levels.

Two-time-scale dispatch strategy for networked micro-grids considering uncertainties and demand response

Under the vision of Carbon Neutrality, multi-energy networked micro-grids (MGs) becomes a reliable and clean solution to achieve this goal. High penetration of renewable energy resources (RERs) brings great uncertainties to stable operation. Uncertainties in day-ahead scheduling must not be ignored. Also, demand response (DR) is getting increasing attentions in modern electricity market. In this regard, this study proposes a two-time-scale dispatch strategy for networked MGs considering multiple uncertainties and DR events. Both electric and thermal demands are taken into account. This strategy encompasses two stages, uncertainties considered day-ahead stage and DR considered intraday stage. The former stage combines scenarios analysis and budgeted robust methods to provide a day-ahead scheduling plan with robustness. The latter stage is an online model to deal with the newly updated forecast data and DR events. Adjustment cost is employed to measure the rescheduling cost. Emission governance cost has already been considered in the both stages, which contributes to realisation of carbon-neutral society. Four cases are simulated to verify performances of the two-time-scale dispatch strategy. Results show this strategy can effectively provide stable energy supply in networked mode. There are 12.0 MWh electricity and 11.8 MWh heat transmitted among MGs to guarantee reliability. Compared with nominal dispatch, 4.34% in average surplus electricity will be generated for robustness. And 19, 13 and 9 times constraint violation for each MG can be avoided in the robust day-ahead scheduling plan. Moreover, the purchased electricity from the grid also has a distinct reduction. It can be seen from the numerical simulation results that one MG can reduce 15.1% and 11.1% of the agreed electricity in the DR intervals. This proposed strategy does not only help MGs decrease the operation cost but also aid utility company relieving the peak demands.

Route choice estimation in rail transit systems using smart card data: handling vehicle schedule and walking time uncertainties

Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.

Evaluation of Interprofessional Practice Education for Master of Social Work Students

As interprofessional education (IPE) continues to grow as part of social work programs, it is important to evaluate how best to engage students in understanding this material and explore possible barriers. With this in mind, researchers conducted qualitative interviews and focus groups to explore perceptions of three cohorts of social work students (n=29) participating in short-term interprofessional practicum experiences. Perceived benefits included improvements in communication and client care. The study also uncovered various barriers with implementing IPE-focused practicums such as time, scheduling and role uncertainty. Students shared the impact their experience had on knowledge and perceptions of the social work role in interprofessional collaboration. Even so, students were able to observe important elements of interprofessional practice from these relatively short practicums, indicating they may be a viable option for programs looking to expose students to real world interprofessional experience.

Mental health and psychological wellbeing of maritime personnel: a systematic review

BackgroundSeafaring has frequently been reported to be a ‘risky occupation’ in terms of both physical and mental health. Individuals working in seafaring professions are exposed to various stressors in the workplace, including social isolation, exposure to poor physical conditions and long work hours. This systematic review aimed to update previous reviews by collating recent literature (published between 2012 and 2021) on the factors associated with mental health and wellbeing in seafaring personnel.MethodsFour electronic databases were searched in April 2021 for primary peer-reviewed studies on factors associated with the mental health and psychological wellbeing of seafarers or interventions to improve the wellbeing of seafarers, published in English in or after the year 2012. Thematic analysis was used to synthesise the data and standardised measures of quality appraisal were used to assess risk of bias.ResultsSixty-three studies were reviewed. Risk factors for poor mental health among seafarers appear to be younger age; being single; poor physical health; exposure to noise/vibration; feeling unsafe; high job demands; long working hours; night/irregular shifts; poor sleep; poor team cohesion; poor perception of management; poor social support; lack of autonomy; scheduling uncertainties; long duration at sea; and over-commitment.ConclusionsThere are numerous steps that maritime managers could take to improve the wellbeing of their personnel, including increased monitoring of the potential for poor mental health in their staff, increasing crew numbers and provision of education and support.

Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of the prediction findings, five benchmark methodologies are used: Persistence model, LMNN (Multilayer Perceptron with LM learning methods), NARX (Nonlinear autoregressive exogenous neural network model), LMRNN (RNNs with LM training methods) and LSTM (Long short-term memory neural network). Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model, LMNN, NARX network, and LMRNN, and the 23-steps forecasting accuracy has improved by 19.61%.

Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network

With considering sewage pipe network upgrading projects in the “villages” in cities, the optimization of construction resources and the assessment of delay risks could be achieved. Based on the schedule-cost hypothetical theory, the mathematical model with constraint indicators was established to obtain the expression of optimal resource input, and conclude the method to analyze the schedule uncertainties. The analysis showed that cyclical footage of pipe could be regarded as a relatively fixed value, and the cost can be regarded as a function that depending on the number of working teams. The optimal number of teams and the optimal schedule occurred when the minimum total cost achieved. In the case of insufficient meteorological data, the Monte Carlo simulation method and uncertainty analysis method can be applied to assess the impact of rainfall on the total construction period, correspondingly the probability of such risk could be derived. The calculation showed that the risk of overdue completion varied significantly according to the construction starting time. It was necessary to take rainfall risk into consideration and make corresponding strategies and measures.