Optimal Schedule Research Articles

Real time monitoring of Rn-222 in workplaces and estimation of working time correction factor

The potential of electronic monitors for evaluating radon exposure is still not fully harnessed, and tests under real conditions are scarce. Ten workplaces were continuously monitored for a year with the radon measurement network of Sofia University, with calibrated and metrologically assured RadonEye +2 monitors. In 7 of the workplaces the average activity concentration of radon during the working time was significantly lower than the total time average. This difference is attributed to behavioral patterns. The ratio of the working time and the total time average (called working time correction factor kWT) for the whole year ranged from 0.71 to 0.98. Its weekly values varied significantly with coefficients of variation between 9 and 28%. Therefore, using short term estimates of kWT to correct the total time radon average determined by passive detectors could lead to bias.The workplaces also exhibited different seasonal radon patterns, although most were offices with similar schedules. In most pairs of workplaces the monthly averages were correlated very weakly or even negatively. This indicates that seasonal radon variations could be specific to each workplace. The value of kWT also varied with the season, suggesting that seasonal variations of radon during working hours might differ from those observed with passive integrating detectors.Overall, long-term follow-up by metrologically assured electronic monitors could help to improve and personalize the estimate of radon exposure of workers. These monitors could potentially support smart anti-radon systems with optimal operation schedules, thereby contributing to the reduction of the energy impact of buildings.

A NOVEL OPTIMIZATION APPROACH FOR ENHANCING EFFICIENCY IN AIRCRAFT MAINTENANCE PLANNING AND SCHEDULING

The operational side of aircraft maintenance plays a key role in ensuring smooth workflow and the practiced processes' reliability in aircraft maintenance repair and overhaul (MRO). Correct MRO planning effectively reduces downtime and enhances the overall accuracy and dependability of procedures. An optimal schedule of maintenance resources and capacity, which entails using the least amount of ground time and schedule modifications, can be used to ensure that all activities are completed productively and consistently before their deadlines. This paper delves into the creation of an optimization approach that considers the dynamic environment of aircraft maintenance. The approach deals with a constraint programming (CP) model for heavy maintenance resources and capacity scheduling, in which variables such as scheduled maintenance tasks, ground time, workload distribution, and maintenance due dates are bounded by resources and technicians’ availability. The proposed CP model showed a strong ability to satisfy different constraints and conditions that were introduced in the artificial industrial environment within a short solving time of 3.12 seconds. Thus, it showed promising results in the case of further developments.

ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ РОБОТИ МАЛОГО МОДУЛЬНОГО ЯДЕРНОГО РЕАКТОРА НА РИНКУ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ

A mathematical model for constructing an optimal loading schedule of a power unit with a small modular nuclear reactor is presented. The main conditions of operation of small modular nuclear reactors with constructive solutions similar to the development of NuScale are determined, and the architecture of the mathematical model for the search of optimal schedules of their operation is presented. The main attributes of the operating modes of small modular nuclear reactors and a mathematical model for simulating the control functions of the reactor operation schedules have been determined. The main components for displaying the results of purchase-lease/sale of electricity by the power-generating company have been determined. The objective function of maximizing the benefit from the operation of a small modular nuclear reactor and limiting the profitability of its operation is given. The basics of the methodology of using the model for building the optimal daily schedule of the functioning of a small modular nuclear reactor in the tasks of assessing the effect of the construction and operation of such reactors at a nuclear power plant have been formulated. Ref. 15, fig. 4, table.

Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty

This study proposes a robust precast concrete (PC) production scheduling model for PC construction projects with schedule uncertainty by adopting a proactive scheduling approach. The proposed model consists of a PC production simulation module that simulates and evaluates the total tardiness of the schedule at a certain confidence level with contractor schedule uncertainty and a GA-based production schedule optimization module that finds an optimal schedule through iterative schedule generation and evaluation using a PC production simulation module. The experimental study shows that the proposed model can find the best schedule with 303.8 h of tardiness at a 100% confidence level, followed by NEHedd 324.7 h, basic GA 328.5 h, and EDD 335.8 h. The results of this study will help PC production schedulers to perform robustly despite contractors’ schedule changes and will thus contribute to the successful completion of PC construction projects.

Performance of district energy system under changing climate: A case study of Shenzhen

The impacts of climate change on the economic performance of different district energy systems (DES) are rarely evaluated. In this research, the performance of both conventional and combined heat and power systems (CCHP) are simulated and compared by modeling and analyzing the DES. Building simulation is conducted by using the downscaled future hourly weather data in the period of 2050–2060 under two future climate scenarios. Optimal sizing and operation schedule of the DES are determined in both current and future climate. Lifetime annualized heating and cooling per area cost (HC) is used to compare the economic performance of the district system. It is found that the annul cooling load in RCP4.5 and RCP8.5 increases by 6 % and 9.68 %. The HC of the conventional system ranges from 122.72 Yuan/m2 to 141.1 Yuan/m2 with a range of profit rate from 5 % to 20 % under various climate scenarios, and that number for the CCHP is from 72.36 Yuan/m2 to 95.24 Yuan/m2. Compared with the conventional system, the optimal CCHP system charges 32.5 % to 41 % less on the building end users if the lifetime profit rate is to be maintained between 5 % to 20 %.

Single-Machine Rescheduling with Rejection and an Operator No-Availability Period

In this paper, we investigate a rescheduling problem with rejection and an operator non-availability period on a single machine. An optimal original schedule with the objective of minimizing the total weighted completion time has been made in a deterministic production scheduling system without an unavailable interval. However, prior to the start of formal job processing, a time interval becomes unavailable due to the operator. No jobs can start or complete in the interval; nonetheless, a job that begins prior to this interval and finishes afterward is possible (if there is such a job, we call it a crossover job). In order to deal with the operator non-availability period, job rejection is allowed. Each job is either accepted for processing or rejected by paying a rejection cost. The planned original schedule is required to be rescheduled. The objective is to minimize the total weighted completion time of the accepted jobs plus the total penalty of the rejected jobs plus the weighted maximum tardiness penalty between the original schedule and the new reschedule. We present a pseudo-polynomial time dynamic programming exact algorithm and subsequently develop it into a fully polynomial time approximation scheme.

Optimal roadworks schedule in multi-agent transportation models

In this paper we consider the problem of the optimal roadworks scheduling in the road traffic network optimization. The main goal of this research is to find the optimal order of roadworks and minimize their negative impact on the transportation network throughput. In order to model the dynamics of a transportation system, we propose a large-scale, multi-agent vehicle routing modelling framework for traffic simulation and impact of roadworks on traffic throughput. We present a quick and effective heuristic algorithm for streamlining the road repairs. Finally, the numeric experiment in a real-world setting is conducted. The obtained results indicate the flexibility of the proposed algorithm, which can be effectively applied by decision makers who need to carry out roadworks with limited time and resources.

Charging scheduling optimisation of battery electric buses with charging setup time

Battery electric buses (BEBs) are recognised as sustainable modes of transportation. Because of its increasing range, efficient and convenient overnight charging has become crucial. The limited number of charging stations and variability in setup times require the optimisation of BEB charging schedules. This study proposes an optimal overnight centralised charging schedule that considers setup time and battery-degradation costs. We model this as a multi-travelling salesman problem with sojourn time to minimise operating costs, including electricity, setup time, and battery wear, while adhering to the bus-schedule constraints. We introduce a local search grouping genetic algorithm with a 2-opt operator local search to address the complexities of public-transport networks. Our extensive numerical analysis, grounded in real-world data, shows a 4.48% reduction in operating costs using our optimised strategy compared with current methods. Moreover, our charging-station allocation analysis provides insights for resource optimisation, advancing sustainable public transport, and charging strategies. This study contributes to the field of sustainable public transportation and charging optimisation.

A new analytical technique for obtaining the optimal sizing, location, and scheduling of BESS in a grid-connected microgrid with multiple cases of heavy DG penetration

This paper aims to provide an optimal location, power, and energy rating for a battery energy storage system (BESS) in a grid-connected microgrid. The microgrid is pre-installed with heavy renewable distributed generations like solar and wind power plants. The paper suggests a straightforward optimal operating schedule for BESS, considering real-time data on solar irradiance, wind speed, load profile, and electricity tariff obtained from the US Energy Information Administration. The proposed method analytically identifies the optimal size and location of the storage system using the modified Q-PQV load flow technique. The method also proposes incorporating seasonal variations of the real-time data to obtain the optimal BESS size. A detailed cost-benefit analysis is exhibited to validate the economic feasibility. The methodological superiority is established by comparing the proposed algorithm with other soft computing techniques like PSO, GA, and JAYA algorithms. The novel technique is executed on 33-bus and 136-bus test systems with multiple cases of solar and wind-distributed generations. The proposed method provided a significant reduction in annual energy loss with a definite improvement in the voltage profile and overall profit of the system.

A novel framework for optimizing job rotation schedules across industries

Implementing job rotation (JR) can yield positive or negative impacts depending on the firm characteristics and industry type. As a result, a generic framework for its implementation is difficult to develop and presently does not exist in literature. Existing models/frameworks have limited application as they are either too specific or generic, but imprecise. Consequently, managers often rely on their intuition to develop JR schedules which sometimes leads to suboptimal/unwanted outcomes. In this article, we propose a generic JR framework, which can be used by managers to develop optimal JR schedules in firms across many industries, such as IT, manufacturing, banking, medical, construction, chemical, aviation, etc. The proposed framework contributes to theory by addressing important questions, such as when to implement JR, and how to quantify some of its most important impacts. We also demonstrate the utility of the proposed framework through an illustrative industrial example.

Ongoing prospective studies on reirradiation: A systematic review of a clinical trials database

IntroductionReirradiation has gained increasing interest, as advances in systemic therapy increase the survival of patients with cancer, and modern radiation techniques allow more precise treatments. However, high-quality prospective evidence on the safety and efficacy of reirradiation to guide clinical practice remains scarce. This systematic review evaluates ongoing prospective studies on reirradiation to identify research gaps and priorities. MethodsA systematic review of ClinicalTrials.gov was conducted on July 11, 2024, using search terms related to reirradiation. Inclusion criteria were prospective studies that were “recruiting,” “not yet recruiting,” or “active, not recruiting.” Studies with published results, retrospective, and in-silico studies were excluded. The review followed PRISMA 2020 guidelines and recommendations for systematic searches of clinical trial registries. ResultsAmong 1026 identified studies, 307 were screened, 99 were included. Fourty (40%) focused on central nervous system (CNS), 23 (23%) head and neck, and 17 (17%) on pelvic reirradiation. Most studies (90%) were interventional, with 32 (32%) phase II and 4 (4%) phase III trials. Sixteen trials were randomized (RCTs), including the 4 phase III trials for recurrent glioblastoma, rectal and nasopharyngeal cancer. Ten dose escalation trials focus on recurrent prostate, rectal, and non-small cell lung cancer as well as glioma. Modern high-precision radiotherapy techniques were frequently used, with 21 (21%) studies using stereotactic radiotherapy and 17 (17%) using particle therapy. Combinations with systemic therapies were investigated in 41 (41%) studies. ConclusionOngoing studies most frequently focus on CNS, head and neck, and pelvic reirradiation. There remains a critical need for RCTs, in particular for lung, breast, and gynecological cancers. Dose escalation trials, application of precision radiation techniques and combinations with modern systemic therapy may help define the optimal multimodality treatment schedules.

The Platform trial In COVID-19 vaccine priming and BOOsting (PICOBOO) booster vaccination substudy protocol

BackgroundCoronavirus-2019 (COVID-19) vaccination in Australia commenced in February 2021. The first vaccines recommended for use were AZD1222 and BNT162b2, both delivered as a two-dose primary schedule. In the absence of sustained immunity following immunisation, recommendations for booster vaccination have followed. It is likely that periodic boosting will be necessary for at least some Australians, but it is unknown what the optimal booster vaccines and schedules are or for whom vaccination should be recommended.MethodsThe Platform Trial In COVID-19 priming and BOOsting (PICOBOO) is a multi-site, multi-arm, randomised, Bayesian adaptive platform trial evaluating different booster vaccine interventions in immunocompetent children and adults, stratified by their primary vaccination schedule and age. Participants are randomised to receive one of three licensed COVID-19 booster vaccines available for use in Australia. PICOBOO aims to generate evidence about the immunogenicity, reactogenicity, and cross-protection of different booster vaccine strategies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants/subvariants. The protocol structure specifying PICOBOO is modular and hierarchical. We have previously published the PICOBOO core (master) protocol. Here, we detail the substudy protocol which outlines the study processes which are specific to PICOBOO participants enrolled in the booster vaccination substudy.DiscussionPICOBOO is an adaptive platform trial evaluating different COVID-19 booster vaccination strategies to generate evidence to inform immunisation practice and policy. The modular and flexible protocol structure is intended to enable investigators to respond with agility to new research questions as they arise, such as immunogenicity targeting emergent virus variants, and the immunogenicity and reactogenicity of new vaccines as they become available for use.Trial registrationAustralian and New Zealand Clinical Trials Register ACTRN12622000238774; registered on 10/02/2022. Protocol V8.0_23112023.

Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps

The flexibility of electrical heating devices can help address the issues arising from the growing presence of unpredictable renewable energy sources in the energy system. In particular, heat pumps offer an effective solution by employing smart control methods that adjust the heat pump’s power output in reaction to demand response signals. This paper combines imitation learning based on an artificial neural network with an intelligent control approach for heat pumps. We train the model using the output data of an optimization problem to determine the optimal operation schedule of a heat pump. The objective is to minimize the electricity cost with a time-variable electricity tariff while keeping the building temperature within acceptable boundaries. We evaluate our developed novel method, PSC-ANN, on various multi-family buildings with differing insulation levels that utilize an underfloor heating system as thermal storage. The results show that PSC-ANN outperforms a positively evaluated intelligent control approach from the literature and a conventional control approach. Further, our experiments reveal that a trained imitation learning model for a specific building is also applicable to other similar buildings without the need to train it again with new data. Our developed approach also reduces the execution time compared to optimally solving the corresponding optimization problem. PSC-ANN can be integrated into multiple buildings, enabling them to better utilize renewable energy sources by adjusting their electricity consumption in response to volatile external signals.

Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile.

Community health programs are gaining relevance within national health systems and becoming inherently more complex. To ensure that community health programs lead to equitable geographic access to care, the WHO recommends adapting the target population and workload of community health workers (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The goal of this study was to demonstrate how geographic optimization can be applied to inform community health programs in rural areas of the developing world. We developed a decision-making tool based on OpenStreetMap mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from mapping on OpenStreetMap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We found that personnel-day requirements ranged from less than 15 to over 60 per CHW catchment for mass distribution campaigns, and from less than 5 to over 20 for proCCM programs, assuming 1 visit per month. To illustrate how these VRPTW algorithms can be used by operational teams, we developed an "e-health" platform to visualize resource requirements, CHW optimal schedules and itineraries according to customizable intervention designs and hypotheses. Further development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with WHO recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.

Polynomial time algorithms to find Pareto optimal schedules of bicriteria lot scheduling problems with splitable jobs on a single parallel-batch machine

This study focuses on solving the scheduling problem associated with a single bounded parallel-batch machine, where jobs have equal lengths but different sizes. The size of each job can be arbitrarily split into two parts, which are processed in consecutive lots. Two different definitions of job completion time are considered: Generic Completion Time, the common definition in classical parallel-batch scheduling, and Percentage Completion Time, calculated as the sum of the products of completion times and the percentages of job size in two lots. The primary contribution of this research is the development of an O(n3) time algorithm to optimize simultaneously total completion time and maximum cost, considering both completion time definitions separately. The algorithms can provide all Pareto optimal points and corresponding schedules for two criteria.

Distributed solution of the day-ahead pump and valve scheduling problem for dynamically adaptive water distribution networks with storage

This paper investigates the computation of daily schedules of pumps and boundary valves for the minimization of energy costs in water distribution networks (WDN) with dynamically adaptive configurations. The considered problem combines integer (“on”/“off”) pump control variables, non-convex energy conservation constraints and time-coupling mass conservation constraints. For operational WDNs, the resulting non-convex mixed-integer non-linear program (MINLP) is too large to be solved using available methods. We propose a tailored heuristic solution method based on the Alternating Direction Method of Multipliers which distributes and coordinates the solution of smaller problems corresponding to individual time steps of the original MINLP. The proposed method is applied to a large-scale WDN from the UK. The daily schedule of pumps and boundary valves obtained for the dynamically adaptive network configuration, computed in 12 min, is shown to be at most 6% suboptimal and nearly 5% cheaper than the globally optimal schedule corresponding to the traditional (sectorized) network configuration. The proposed algorithm outperforms alternative off-the-shelf and tailored approaches, providing a scalable method to compute good solutions to the complex day-ahead pump and valve scheduling problem in operational dynamically adaptive WDNs.

Decentralization of renewable energy sources based optimum scheduling and management through a virtual power plant

Abstract In this paper, renewable resources and co-generation units are grouped together to perform optimized operational scheduling in a virtual power plant (VPP) system. Renewable sources consist of solar photovoltaic, wind, and fuel cell units that are equipped together with a cogenerating unit to intensify the system's dependency in case of power shortage from renewable sources. The uncertainty aspect is investigated for the developed system as renewables are associated, and its impact on network operation is discussed. Storage provision is facilitated in the form of flexible and spinning reserves in which electric vehicle (EV) and energy storage systems (ESS) increase the system reliability by ensuring continuity of power supply. Optimal scheduling is carried out from both economic and environmental perspectives in which the net profit of the system is improved and the generated emissions are reduced by considering dual scheduling i.e., day-ahead (DA) and proposed 15 min interval. Moreover, a nature-inspired metaheuristic red fox optimization (RFO) is employed to handle the VPP problem and the results with the context to net profit and emission are also compared with work available in the literature. Finally, the proposed approach is promising in terms of higher computational efficacy and reveals the suitability of the VPP system.

Phase I/II Investigator-Initiated Study of Olaparib and Temozolomide in SCLC: Final Analysis and CNS Outcomes.

Temozolomide plus PARP inhibition has shown promise in small cell lung cancer (SCLC). We previously reported outcomes from the first 50 patients (cohort 1) of a phase I/II trial of olaparib/temozolomide in recurrent SCLC. Here, we report a final analysis of this trial, including a second cohort with an alternate dosing strategy and an exploratory analysis of CNS-specific outcomes. This was an open-label phase I/II trial testing the combination of olaparib and temozolomide in relapsed SCLC. The primary endpoint was ORR. Secondary endpoints were safety, PFS, and OS. We tested escalating doses of olaparib/temozolomide across two cohorts, both of which had temozolomide dosed on D1-7 of each 21-days cycle. In previously published cohort 1, olaparib was dosed on D1-7; in cohort 2 olaparib was dosed continuously. Sixty-six patients were enrolled across the two cohorts, 50 in cohort 1 and 16 in cohort 2. The confirmed ORR of cohort 1 was 41.7% (20/48 evaluable), and the confirmed ORR of cohort 2 was 7% (1/14 evaluable; closed after dose escalation to enrollment for lack of observed efficacy). Among 15/66 patients (22.7%) with untreated brain metastases at enrollment, best overall intracranial response was CR in 6/15 patients, PR in 4/15 patients, and SD in 3/15 patients for a CNS disease control rate of 87% (95% CI: 59.5-98.3%). Olaparib/temozolomide may be effective in relapsed SCLC, especially for patients with CNS disease. Ongoing analyses regarding optimal dosing schedule will inform potential for future use of this combination in SCLC.

Development of an estimation formula for preparation time of anesthesia induction and surgery accounting for clinical department factors in optimal surgery schedule management

Efficient operating room management is essential and requires precise surgery scheduling. We hypothesized that an estimation formula for the preparation time for anesthesia induction and surgery could be developed by incorporating anesthesia and surgical factors, as well as the ‘clinical department,’ into the formula. This retrospective observational study analyzed 12,528 scheduled surgical cases. A regression analysis that included the clinical department, six anesthesia factors, and five surgical factors was conducted. This analysis aimed to develop both an analytical framework and an equation for estimating the time required for both anesthesia induction and surgical preparation. Our estimation formula wielded high accuracy (R2 = 0.801). Particularly, there was only a difference of less than 3 min for surgeries under general anesthesia. In addition, modeling preparation time using “medical interventions performed in the operating room” as a factor instead of patient characteristics was found to be beneficial. It was possible to develop a highly accurate formula for estimating preparation time of anesthesia induction and surgery by analyzing the anesthesia factors and the surgical factors and incorporating the clinical department as an estimation factor. However, this study represents the development phase of the estimation formula. A multicenter study is essential to validate its generalizability and robustness across different settings before broader application.

Including Lifetime Hydraulic Turbine Cost into Short-Term Hybrid Scheduling of Hydro and Solar

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine’s working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs.