Operant Schedule Research Articles

Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples

This paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure.

Optimal operation of an electricity-hydrogen DC microgrid with integrated demand response

Uncertainties introduced by the high penetration of renewable sources, plug-in-hybrid-electric vehicle load demand, and limited capacity of firm generation available in a DC microgrid make the energy scheduling task rather challenging. This paper proposes a decentralized energy management scheme with a real-time pricing-based demand response implementation for a DC microgrid, considering the sectoral coupling between electricity and hydrogen energy. The objectives of the scheduling strategy are to maximize the profit of the DC microgrid operator and reduce the cost of energy use by the consumers, considering the interaction and interdependence of the electrical and hydrogen systems with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the upstream grid, microturbines, battery energy storage, hydrogen storage, electrolyzer and fuel cell) and sets real-time prices. The consumers set their consumption patterns according to the real-time price. The DC microgrid operator side flexibilities are coordinated with the consumer side flexibilities (thermostatically controlled load like air-conditioner and plug-in-hybrid electric vehicle) using the decentralized “Alternating Direction Method of Multipliers” approach. The probabilistic Copula theory models correlated input uncertainties. Simulation results on a six-bus DC microgrid test system reveal that the operating cost of the DC microgrid operator reduces by ∼11.06% while the energy use cost of consumers reduces by ∼4.80% using the proposed approach for the system under study.

Adaptive Scheduling of Continuous Operators for IoT Edge Analytics

In this paper, we address the problem of adaptive scheduling of data stream processing and analytics (DSPA) applications in a shared edge fog cloud (EFC) continuum with response time constraints. The focus is on handling the dynamic workload of DSPA applications caused by the variability of their input data stream rates generated by mobile IoT devices, and the dynamically available resource capacity in the EFC continuum. To address these challenges, we characterise the different types of resources in the EFC continuum, as well as the operators that make up a DSPA application. Based on this characterisation, we propose models to evaluate the response time and the cost of using the resources in the always dynamic EFC continuum. We then formulate the problem of adaptive scheduling of a DSPA application in the EFC continuum with the objective of minimising the cost of using the shared resources subject to the constraints of the response time and the available capacity of the EFC resources. We propose a heuristic algorithm that dynamically computes a new scheduling of the DSPA application, taking into account its current deployment state and the current state of the shared resources in the EFC continuum. Experimental results, using simulation, show the effectiveness of our proposed algorithm against algorithms of related work.

Increased patient body mass index is associated with increased surgeon physiologic stress during total hip arthroplasty.

While increased body mass index (BMI) in patients undergoing total hip arthroplasty (THA) increases surgical complexity, there is a paucity of objective studies assessing the impact of patient BMI on the cardiovascular stress experienced by surgeons during THA. The aim of this study was to assess the impact of patient BMI on surgeon cardiovascular strain during THA. We prospectively evaluated three fellowship-trained arthroplasty surgeons performing a total of 115 THAs. A smart-vest worn by the surgeons recorded mean heart rate, stress index (correlate of sympathetic activation), respiratory rate, minute ventilation, and energy expenditure throughout the procedures. Patient demographics as well as perioperative data including surgical approach, surgery duration, number of assistants, and the timing of the surgery during the day were collected. Linear regression was utilized to assess the impact of patient characteristics and perioperative data on cardiorespiratory metrics. Average surgeon heart rate, energy expenditure, and stress index during surgery were 98.50 beats/min, 309.49cal/h, and 14.10, respectively. Higher patient BMI was significantly associated with increased hourly energy expenditure (P = 0.027), mean heart rate (P = 0.037), and stress index (P = 0.027) independent of surgical approach. Respiratory rate and minute ventilation were not associated with patient BMI. The number of assistants and time of surgery during the day did not impact cardiorespiratory strain on the surgeon. The physiologic burden on surgeons during primary THA significantly increases as patient BMI increases. This study suggests that healthcare systems should consider adjusting reimbursement models to account for increased surgeon workload due to obesity. Further surgeons should adopt strategies in operative planning and case scheduling to handle this added physical strain. III.

Predictive modelling of total operating room time for Laparoscopic Cholecystectomy using pre-operatively known indicators to guide accurate surgical scheduling in a critical access hospital

The financial margin of rural and critical access hospitals highly depends on their surgical volume. An efficient operating room is necessary to maximise profit and minimise financial loss. OR utilisation is a crucial OR efficiency metric requiring accurate case duration estimates. The patient's age, ASA, BMI, Mallampati score, previous surgery, the planned surgery, the surgeon, the assistant's level of experience and the severity of the patient's disease are also associated with operative duration. Although complex machine-learning models are accurate in operative prediction, they are not always available in resource-limited hospitals. Laparoscopic cholecystectomy (LC) is one of the most common surgical procedures performed and is one of the few procedures performed at critical access and rural hospitals. The accurate estimation of the operative duration of LC is essential for efficient OR utilisation. We hypothesise that a multivariate linear regression prediction model can be constructed from a set of pre-operatively known, easily collected variables to maximise OR utilisation and improve operative scheduling accuracy for LC. We further hypothesise that this model can be implemented in resource-limited environments, such as critical access hospitals.

Modeling the Integrated Flexible Job-Shop and Operator Scheduling in Flexible Manufacturing Systems

This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically, Mixed-Integer Programming (MIP) and Constraint Programming (CP) perform on this intricate scheduling problem. We test the effectiveness of both the MIP and CP models on an illustrative example as well as on a set of larger instances and draw conclusions, which indicate the need to integrate these two models into some approximation scheme to tackle large-scale instances for this complex problem.

Research on the Influence of Operator Fatigue Factors on Port Service Capability Based on Discrete System Simulation

With the continuous and stable development of the global economy, ports play a vital role as cargo transhipment hubs. Among them, the human factor of the operators in the port plays a decisive role in the port service ability. In order to explore the specific effects of different fatigue states of the quay bridge, yard bridge, and internal truck operators in the traditional container terminal on the port’s service capacity, seven test schemes are proposed, and computer simulation methods are used to build a simulation model of the terminal. Through a large number of simulation tests, it is concluded that the fatigue of different equipment operators has different effects on the operation efficiency of the terminal. If the three operators are fatigued at the same time, the operation efficiency of the terminal will be reduced by 17.27%, which will have a very negative impact on the overall operation of the terminal. Therefore, optimizing the scheduling of operators and avoiding fatigue operations will improve the port’s service capacity.

Enhancing LoRA Model Serving Capacity via Adaptive Operator Scheduling for Multi-Tenancy on GPU

Enhancing LoRA Model Serving Capacity via Adaptive Operator Scheduling for Multi-Tenancy on GPU

Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

Hyperdopaminergia in rats is associated with reverse effort-cost dependent performance.

Dopamine is implicated in the effort-based control of motivational processes; however, whether tonic dopamine regulates the effort-cost impact on motivation, is still debated. The rats lacking the dopamine transporter (DAT), which have dramatically increased levels of the synaptic dopamine, were used in the present study to elucidate the role of the synaptic dopamine in motivational processes. To study the reward-related processes, the progressive ratio 3 (PR3) operant schedule of food reinforcement (the ratio increases by 3 after each earned reinforcer) was performed in adult male rats (DAT knockouts (DAT-KO), heterozygotes (DAT-HT) and wild-types (DAT-WT)). During the PR3 session, the response rate of DAT-KO rats was gradually increased following the augmented required number of responses. In contrast, the local response rate of DAT-WT and DAT-HT decreased. d-Amphetamine sulfate salt (3 mg/kg, i.p.) altered the local response rate dynamics in DAT-WT, which became similar to that of DAT-KO. Interestingly, the reduction in response rate at low effort demands was associated with decreased rate of entries into the magazine tray in DAT-WT rats treated with amphetamine (3 mg/kg) but not in DAT-KO rats. Our results suggest that the elevated tonic synaptic dopamine can strongly affect motivation/effort-cost relation in rodents.

Online correction of the transient voltage security region boundary based on load parameter variations in power systems

The transient voltage security region (TVSR) is an essential part of the dynamic safety region of a power system, which represents the safe operating region of the transient voltage where there is no loss in stability. The power system load significantly affects the transient voltage stability. However, the load model in the existing power systems, which considers the security region of the dynamic process, is too simple to comprehensively characterize the load characteristics of the dynamic process. Furthermore, it neglects the influence of the load model parameters on the dynamic process security region. In this study, a composite load model with distributed photovoltaic power is used as the research object. The main load parameters affecting the voltage stability and the generator nodes that are sensitive to the load parameters are calculated using the trajectory sensitivity method. The TVSR of the system is constructed based on the load and generator’s active powers. The correlations between different load-leading parameter combination scenarios and TVSR boundary points are mined using the CatBoost learning framework. Thus, the TVSR boundary can be promptly corrected online based on the changes in the load parameters, and the system security boundary can be described more accurately. The proposed method is verified using the IEEE39 and IEEE118 node systems. It is observed that the proposed method can correct the TVSR boundary online with high precision corresponding to real-time changes in the load model parameters. This provides a more accurate TVSR boundary for the power system operation scheduler, which helps in guiding it to control the system more accurately.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm

The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system’s complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. However, current DRL algorithms fail to enforce rigorous operational constraints (e.g., power balance, ramping up or down constraints) limiting their implementation in real systems. To overcome this, in this paper, a DRL algorithm (namely MIP-DQN) is proposed, capable of strictly enforcing all operational constraints in the action space, ensuring the feasibility of the defined schedule in real-time operation. This is done by leveraging recent optimization advances for deep neural networks (DNNs) that allow their representation as a MIP formulation, enabling further consideration of any action space constraints. Comprehensive numerical simulations show that the proposed algorithm outperforms existing state-of-the-art DRL algorithms, obtaining a lower error when compared with the optimal global solution (upper boundary) obtained after solving a mathematical programming formulation with perfect forecast information; while strictly enforcing all operational constraints (even in unseen test days).

Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system

With the increasing uncertainty of energy supply side output, fully encouraging users to participate in demand response through different types of demand response incentive mechanisms has become one of the effective ways to deal with the uncertainty of integrated energy system operation and improve the overall energy efficiency. However, in existing studies, the coordination of uncertainty handling, optimization of demand response incentive strategies, and demand response measures at different time scales have not been adequately considered in the operation of integrated energy systems. Based on these considerations, this paper proposes a multi time-scale game optimization scheduling model for Park-level Integrated Energy System considering multiple types of demand response models. In the day-ahead stage, a Park-level Integrated Energy System optimization game scheduling model based on the demand response comprehensive incentive mechanism is established, and the uncertainty of the predicted value of distributed renewable energy and multi-type energy load was characterized based on the fuzzy chance-constrained programming method. In the intraday and real-time stages, a rolling optimization scheduling model is established with the minimum cost of Park-level Integrated Energy System operator scheduling. For the proposed model, an improved particle swarm optimization algorithm and an iterative solution strategy of CPLEX solver are introduced. Finally, the simulation results of an actual case show that the proposed model can effectively improve the Park-level Integrated Energy System operator and user economy while ensuring reliability.

PartitionTuner: An operator scheduler for deep‐learning compilers supporting multiple heterogeneous processing units

AbstractRecently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep‐learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep‐learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep‐learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator‐scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type‐based operator‐scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling‐based operator‐scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

Complicated Laryngotracheal Stenosis Occurring Early after COVID-19 Intubation

Background: Airway stenosis is a known complication of prolonged intubation in hospitalized patients. With the high rate of intubations in patients with COVID-19 pneumonia, laryngotracheal stenosis (LTS) is a complication of COVID-19 that drastically reduces quality of life for patients who may remain tracheostomy-dependent. Methods: Patient medical history, laryngoscopy, and CT imaging were obtained from medical records. Results: We report four cases of complicated LTS following intubation after COVID-19 pneumonia and explore the current literature in a narrative review. Four patients developed LTS following intubation from COVID-19 pneumonia. Three patients remain tracheostomy-dependent, and the fourth required a heroic operative schedule to avoid tracheostomy. Conclusion: Intubation for COVID-19 pneumonia can result in severe LTS, which may persist despite endoscopic intervention.

Lack of Workplace Support for Obstetric Health Concerns is Associated With Major Pregnancy Complications: A National Study of US Female Surgeons.

We sought to assess whether lack of workplace support for clinical work reductions during pregnancy was associated with major pregnancy complications. Surgeons are at high risk of major pregnancy complications. Although rigorous operative schedules pose increased risk, few reduce their clinical duties during pregnancy. An electronic survey was distributed to US surgeons who had at least 1 live birth. Lack of workplace support was defined as: (1) desiring but feeling unable to reduce clinical duties during pregnancy due to failure of the workplace/training program to accommodate and/or concerns about financial penalties, burden on colleagues, requirement to make up missed call, being perceived as weak; (2) disagreeing colleagues and/or leadership were supportive of obstetrician-prescribed bedrest. Multivariate logistic regression determined the association between lack of workplace support and major pregnancy complications. Of 671 surgeons, 437 (65.13%) reported lack of workplace support during pregnancy and 302 (45.01%) experienced major pregnancy complications. Surgeons without workplace support were at higher risk of major pregnancy complications than those who had workplace support (odds ratio: 2.44; 95% confidence interval: 1.58-3.75). Bedrest was prescribed to 110/671 (16.39%) surgeons, 38 (34.55%) of whom disagreed that colleagues and/or leadership were supportive. Of the remaining surgeons, 417/560 (74.5%) desired work reductions but were deterred by lack of workplace support. Lack of workplace support for reduction in clinical duties is associated with adverse obstetric outcomes for surgeons. This is a modifiable workplace obstacle that deters surgeons from acting to optimize their infant's and their own health. To ensure the health of expectant surgeons, departmental policies should support reduction of clinical workload in an equitable manner without creating financial penalties, requiring payback for missed call duties, or overburdening colleagues.

D1 Dopamine/Mu Opioid Receptor Interactions in Operant Conditioning Assays of Pain‐Depressed Responding and Drug‐Induced Rate Suppression: Assessment of Therapeutic Index in Rats

In vivo receptor interactions vary as a function of behavioral endpoint. Our laboratory has begun investigating the effects of D1 dopamine receptor agonists and D1 dopamine/mu opioid receptor interactions. This decision is based on studies that have shown (1) dopamine D1 receptors produce pain relief in rodent models of acute pain, (2) a mixed literature indicating D1 agonists either do or do not possess abuse liability, perhaps due in part to behavioral endpoint selection, and (3) co‐localization of D1 and mu receptors in brain. This is the first report of D1/mu receptor interactions using an assay of pain depressed behavior. A multiple‐cycle FR10 operant schedule and a simple FR10 schedule were utilized in the presence of (antinociception) and in the absence of (rate suppression / sedation) a lactic acid inflammatory pain‐like manipulation, with antinociception defined as the therapeutic effect, and sedation defined as a side effect. SKF82958 and methadone were used as selective/high efficacy D1 dopamine and mu opioid agonists, respectively. Both SKF82958 and methadone alone produced dose‐dependent restoration of pain‐depressed responding and response rate suppression in the mult‐cycle schedule, but SKF82958 was ineffective in restoring pain‐depressed responding in the simple FR10 schedule. Equal potency 1:1 SKF82958/methadone mixtures and 1:3 and 3:1 mixtures are currently being tested in both operant schedules to determine the nature of the interaction (additive, superadditive, subadditive). Results will indicate if therapeutic index varies as a function of operant schedule, and the degree to which the addition of SKF82958 produces opioid‐sparing effects.

A Refined Method for Studying Foraging Behaviour and Body Mass in Group-Housed European Starlings.

Simple SummarySmall birds such as European starlings respond rapidly to environmental challenges by losing or gaining weight. Laboratory studies of these birds are therefore useful for understanding how the environment affects body weight. However, practical constraints including the need to catch birds frequently for weighing has meant that birds are often housed alone in small cages for such studies. Such conditions are unnatural and are likely to cause stress. Consequently, the data obtained from these studies are unrepresentative of wild birds. Here, we describe a novel technology based on smart feeders that permits continuous recording of foraging behaviour and body masses from starlings housed in groups in large indoor aviaries that permit more natural behaviour. We show that the birds quickly learn to use the feeders and that the system delivers detailed real-time data on foraging behaviour and body mass, without the need for frequent catching. The data obtained allowed us to study how the foraging decisions that a bird makes within a single day affect its body weight that day. These improvements in the quality of the data that we are able to collect will help inform our understanding of the environmental causes of weight gain and obesity.Laboratory experiments on passerine birds have been important for testing hypotheses regarding the effects of environmental variables on the adaptive regulation of body mass. However, previous work in this area has suffered from poor ecological validity and animal welfare due to the requirement to house birds individually in small cages to facilitate behavioural measurement and frequent catching for weighing. Here, we describe the social foraging system, a novel technology that permits continuous collection of individual-level data on operant foraging behaviour and body mass from group-housed European starlings (Sturnus vulgaris). We report on the rapid acquisition of operant key pecking, followed by foraging and body mass data from two groups of six birds maintained on a fixed-ratio operant schedule under closed economy for 11 consecutive days. Birds gained 6.0 ± 1.2 g (mean ± sd) between dawn and dusk each day and lost an equal amount overnight. Individual daily mass gain trajectories were non-linear, with the rate of gain decelerating between dawn and dusk. Within-bird variation in daily foraging effort (key pecks) positively predicted within-bird variation in dusk mass. However, between-bird variation in mean foraging effort was uncorrelated with between-bird variation in mean mass, potentially indicative of individual differences in daily energy requirements. We conclude that the social foraging system delivers refined data collection and offers potential for improving our understanding of mass regulation in starlings and other species.

Postpartum Depression in Surgeons and Workplace Support for Obstetric and Neonatal Complication: Results of a National Study of US Surgeons.

Postpartum depression has well-established long-term adverse effects on maternal and infant health. Surgeons with rigorous operative schedules are at higher risk of obstetric complications, but they rarely reduce their workload during pregnancy. We evaluated whether lack of workplace support for work reductions during difficult pregnancies or after neonatal complications is associated with surgeon postpartum depression. An electronic survey was sent to practicing and resident surgeons of both sexes in the US. Female surgeons who had at least one live birth were included. Lack of workplace support was defined as: (1) disagreeing that colleagues/leadership were supportive of obstetric-mandated bedrest or time off to care for an infant in the neonatal intensive care unit; (2) feeling unable to reduce clinical duties during pregnancy despite health concerns or to care for an infant in the neonatal intensive care unit. Multivariate logistic regression was used to determine the association of lack of workplace support with postpartum depression. Six hundred ninety-two surgeons were included. The 441 (63.7%) respondents who perceived a lack of workplace support had a higher risk of postpartum depression than those who did not perceive a lack of workplace support (odds ratio 2.21, 95% CI 1.09 to 4.46), controlling for age, race, career stage, and pregnancy/neonatal complications. Of the surgeons with obstetric-related work restrictions, 22.6% experienced loss of income and 38.5% reported >$50,000 loss. Lack of workplace support for surgeons with obstetric or neonatal health concerns is associated with a higher risk of postpartum depression. Institutional policies must address the needs of surgeons facing difficult pregnancies to improve mental health outcomes and promote career longevity.