Short Run Time Research Articles

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory-BPNN.

Sensitivity analysis of influencing factors on frost resistance is carried out in this paper, and a two-stage neural network model based on grey theory and Back Propagation Neural Networks (BPNNs) is established for the sake of predicting the frost resistance of active-admixture recycled concrete quickly and accurately. Firstly, the influence degree of cement, water, sand, natural aggregate, recycled aggregate, mineral powder, fly ash, fiber and air-entraining agent on the frost resistance of active-admixture recycled-aggregate concrete was analyzed based on the grey system theory, and the primary and secondary relationships of various factors were effectively distinguished. Then, the input layer of the model was determined as cement, water, sand, recycled aggregate and air-entraining agent, and the output layer was the relative dynamic elastic modulus. A total of 120 datasets were collected from the experimental data of another author, and the relative dynamic elastic modulus was predicted using the two-stage BPNN prediction model proposed in this paper and compared with the BPNN prediction results. The results show that the proposed two-stage BPNN model, after removing less-sensitive parameters from the input layer, has better prediction accuracy and shorter run time than the BPNN model.

Non-Oil Export Promotion and Manufacturing Capacity Utilization in Nigeria and South Africa: A Tale of Two Countries in Africa

This paper studies the dynamics between non-oil export promotion and manufacturing capacity utilization in Nigeria and South Africa. The empirical analysis applies the asymmetric ARDL cointegration technique proposed by Shin et al. (2011) from 1986 to 2022. The evidence suggests that ignoring the intrinsic nonlinearities may lead to misleading inference. In particular, the results reveal significant differences in the response of manufacturing capacity utilization to positive or negative changes in non-oil export and other explanatory variables in both the long- and short-run time horizons. The evidence of asymmetry could be of major importance for more efficient policymaking and forecasting in the Nigerian and South African manufacturing sectors to grow.

Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions

Abstract. X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid–rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep-learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep-learning approaches, particularly their capacity to efficiently process expansive datasets. Our methodology utilises deep learning to achieve accurate segmentation of time-resolved volumetric images of the gypsum dehydration reaction, a process that traditional segmentation techniques have struggled with due to poor contrast between reactants and products. We utilise a 2D U-net architecture for segmentation and introduce machine-learning-obtained labelled data (specifically, from random forest classification) as an innovative solution to the limitations of training data obtained from imaging. The deep-learning algorithm we developed has demonstrated remarkable resilience, consistently segmenting volume phases across all experiments. Furthermore, our trained neural network exhibits impressively short run times on a standard workstation equipped with a graphic processing unit (GPU). To evaluate the precision of our workflow, we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration. The errors between the predicted and segmented volumes in all time series experiments fell within the 2 % confidence intervals of the theoretical curves, affirming the accuracy of our methodology. We also compared the results obtained by the proposed method with standard segmentation methods and found a significant improvement in precision and accuracy of segmented volumes. This makes the segmented computed tomography images suited for extracting quantitative data, such as variations in mineral growth rate and pore size during the reaction. In this work, we introduce a distinctive approach by using an internal standard to validate the accuracy of a segmentation model, demonstrating its potential as a robust and reliable method for image segmentation in this field. This ability to measure the volumetric evolution during a reaction with precision paves the way for advanced modelling and verification of the physical properties of rock materials, particularly those involved in tectono-metamorphic processes. Our work underscores the promise of deep-learning approaches in elevating the quality and reproducibility of research in the geosciences.

METHOD DEVELOPMENT AND VALIDATION OF RAPID ISOCRATIC RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF PARACETAMOL, CAFFEINE, AND PROPYPHENAZONE IN PHARMACEUTICAL FORMULATION

Objective: The objective is to develop a novel, rapid, simple, precise, accurate, and reproducible RP-HPLC method for simultaneous estimation of paracetamol, caffeine, and propyphenazone in bulk and a pharmaceutical dosage form. Method: Optimized chromatographic conditions were used for isocratic elution with Shimadzu C18 (4.6×250 mm, 5 μm), methanol, and 20mM phosphate buffer (60:40, v/v, pH 2.5) as mobile phase, flow rate 1.2 mL/min, and UV detector at λ max 272 nm. The method was validated for specificity, precision, linearity, accuracy, robustness, and solution stability as per the International Council for Harmonization (ICH) guidelines. Result: It was discovered that the retention time for paracetamol, caffeine, and propyphenazone was found to be 2.6 min, 3.0 min, and 7.5 min. The method proved to be rapid, simple, linear (R2>0.999), precise {relative standard deviation (RSD<2.0%)}, accurate (recovery 98–102%), sensitive, and robust. Conclusion: The proposed novel isocratic RP-HPLC method was found to be rapid (short run time of 10 min), highly selective, accurate, and sensitive. The method has been successfully applied to the simultaneous analysis of paracetamol, caffeine, and propyphenazone in a pharmaceutical formulation.

Solving the medical student scheduling problem using simulated annealing

AbstractWe consider the medical student scheduling (MSS) problem, which consists of assigning medical students to internships of different disciplines in various hospitals during the academic year to fulfill their educational and clinical training. The MSS problem takes into account, among other constraints and objectives, precedences between disciplines, student preferences, waiting periods, and hospital changes. We developed a local search technique, based on a combination of two different neighborhood relations and guided by a simulated annealing procedure. Our search method has been able to find the optimal solution for all instances of the dataset proposed by Akbarzadeh and Maenhout (Comput Oper Res 129: 105209, 2021b), in a much shorter runtime than their technique. In addition, we propose a novel dataset in order to test our technique on a more challenging ground. For this new dataset, which is publicly available along with our source code for inspection and future comparisons, we report the experimental results and a sensitivity analysis.

Evolutionary optimization approach based on heuristic information with pseudo-utility for the quadratic assignment problem

Existing data mining-based meta-heuristic approaches have endeavored to efficiently solve the Quadratic Assignment Problem (QAP). However, their captured heuristic information lacks com prehensiveness and usefulness, as they fail to consider various combinations of frequent items and ignore objective information. This deficiency makes it challenging to generate new solutions that balance global and local search. To address these issues, we propose an approach called EOHIPU. EOHIPU efficiently mines and represents more itemsets composed of frequent items, and simultaneously introduces pseudo-utility to quantitatively characterize each item's contribution to the objective, thereby capturing more comprehensive and useful heuristic information. With this heuristic information, EOHIPU designs a mechanism for generating new solutions, balancing global and local search. We conduct experiments on 21 hard benchmark instances from QAPLIB, comparing EOHIPU with five state-of-the-art approaches. Our EOHIPU achieves the best results, with average percentage deviations (APDs) of 0.064 and 0.046 under short and long running times, respectively. Additionally, EOHIPU demonstrates faster convergence compared to other state-of-the-art approaches across all instances. These results highlight EOHIPU's superior solution quality and convergence rate. Moreover, the effectiveness of the captured heuristic information and the new solution generation mechanism is validated.

双电机驱动电动拖拉机能量管理策略的研究

At present, electric tractors experience significant battery energy loss during operation, resulting in a short continuous running time. Therefore, in order to reduce the power consumption of the tractor drive system, minimize battery energy loss, and extend the operating time under various conditions, this paper presents a method for driving an electric tractor based on dual-motor coupling. Based on the characteristics of the transmission structure, an online torque distribution strategy for dual-motor coupling-driven electric tractors using a fuzzy control approach is proposed. First, an enhanced genetic algorithm is utilized to optimize the fuzzy rule table. Simultaneously, it is compared with the offline optimization strategy of dynamic programming. Subsequently, a method that integrates test data models and theoretical models is employed to establish an efficiency model of key components of the electric tractor drive system and a longitudinal dynamics model of the entire machine. The performance of the entire vehicle was simulated and analyzed under plowing conditions. Finally, on the experimental bench, conduct steady-state load tests and dynamic performance tests on the dual-motor coupled drive system. The results show that the State of Charge (SOC) change trends of the fuzzy control strategy based on the improved genetic algorithm and the dynamic programming strategy are similar. The SOC change values are close, which enhances the adaptability of the electric tractor in various operating conditions. Compared with the fuzzy control strategy, the improved strategy reduced average power consumption by 8.8%, demonstrating that the fuzzy control energy management strategy based on the enhanced genetic algorithm is both economical and superior. The bench experiment demonstrated that the dual-motor drive system can adapt to load changes to achieve power distribution between the two motors, meeting the required workload while reducing power consumption.

Fast urban inundation simulation with RIM2D for flood risk assessment and forecasting

IntroductionUrban pluvial flooding is a growing concern worldwide as consequence of rising urban population and climate change induced increases in heavy rainfall. Easy-to-implement and fast simulation tools are needed to cope with this challenge.MethodsThis study describes the development of the parsimonious, GPU-accelerated hydraulic model RIM2D for urban pluvial flood simulations. This is achieved by considering the built-up urban area as flow obstacles, and by introducing capacity-based approaches to consider urban drainage by infiltration on pervious surfaces and sewer drainage from roofs and sealed surfaces. The model performance was analyzed by simulating 8 heavy rainfall events in a test area in the city of Dresden, Germany. For these events detailed discharge measurements of sewer discharge are available, providing a unique dataset for evaluating the sewer drainage simulation, which is of high importance for realistic pluvial inundation simulations in urban areas.Results and discussionWe show that the model simulates the temporal dynamics of the sewer discharge and the sewer volume within acceptable ranges. Moreover, the erratic variation of the simulated to measured sewer discharge suggests that the deviations from the measurements are caused by the precipitation input rather than the model simplifications. We conclude that RIM2D is a valid tool for urban inundation simulation. Its short simulation runtimes allow probabilistic flood risk assessments and operational flood forecasts.

Multiobjective route finding in a multimode transportation network by NSGA-II

Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.

Machine Learning‐Based Wave Model With High Spatial Resolution in Chesapeake Bay

AbstractA high‐resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short‐term memory to simulate large‐scale, high‐resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data‐driven model has root‐mean‐square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data‐driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models.

Combined green analytical principles and quality by design for ultraperformance liquid chromatography analytical method development, the characterization and in-silico toxicity prediction of Ixazomib degradation products using mass spectrometry

Ixazomib citrate (IC) is the first oral selective proteosome inhibitor for treating multiple myeloma. IC is prone to degradation due to its oxidative deboronation and the amide bond, affecting patient health, drug quality, and efficacy. The stability of IC is crucial during drug development as it guides the inherent stability of the molecule, its degradation pathways, packing materials, and formulation development. Following the International Conference on Harmonization (ICH) Q1A (R2) and Q1B, a stability study were performed for both solution and solid-state stress studies. Under oxidative and alkaline conditions, 3 degradation products (DPs) were identified, separated, and method-validated according to ICH Q2 (R1) guidelines. From the Design Expert statistical tool, the Central Composite Design was used to optimize the final analytical method conditions, where the p-values for the model are < 0.05%. Green analytical chemistry has significantly reduced the use of hazardous organic solvents without losing chromatographic performance. The green separation and quantification of DPs and IC on Ultra Performance Liquid Chromatography (UPLC) using an Inert-Sustain C8 (50×3.0) mm 2.0 µm column with gradient elution using 10 mM ammonium acetate buffer (pH 5.0) and ethanol at a flow rate of 0.5 mL/min and detection at 230 nm. The results of green assessment tools like GAPI, AGREE, and Analytical eco-scale found that the method is excellent for the greenness of utilizing ethanol as a solvent, shorter runtime, and lesser waste. The method was validated as per ICH Q2 guidelines, and the results found it is sensitive, precise, accurate, robust, and linear for its intended use. The method is suitable for quantifying IC and its DPs from 2.0 to 150 µg/mL with R2 values of 0.9996 with a detection limit of 1.0 µg/mL. The plausible degradation structures and pathways of DPs were outlined using tandem mass spectra employed on LC-QTOF-MS/MS in both ESI positive and negative modes. The mechanistic explanation for establishing DPs was explained in detail. The ADMET Predictor™ software predicted the physicochemical and ADMET properties. The toxicity profile reveals that DP2 and DP3 are teratogenic, while D1 and D3 show phospholipidosis.

Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization

In order to solve the problem that the traditional building space configuration model cannot meet the optimization of building space characteristics, the author proposes the optimization of building space utilization based on spatialized particle swarm optimization. First, to solve the problem of optimal allocation of space units, the PSO algorithm is modified to encode the space units by means of character coding.Secondly, the maximum standardization method is used for data processing, and the factors affecting space utilization are summarized, the objective function of optimal allocation of architectural space is given from three aspects: economic benefits, social benefits and ecological benefits; Finally, by analyzing the advantages and disadvantages of master-slave parallel model and point-to-point parallel model, a chained parallel structure is proposed. The experimental results show that: The experimental data is based on the utilization of building space in these three regions in 2015, and the vector map is divided into 30 m × A grid of 30 m in size, and all statistical data and spatial data are projected on each grid cell. The difference between the fitness values of the final convergence of the three parallel models is small, and the main difference is the convergence speed. During the run time test, set the three parallel models to run under the conditions of 8, 16, 32 and 64 nodes respectively. Because of the combination of the advantages of master-slave model and point-to-point model, the running time of chained parallel model is significantly lower than that of the other two parallel models. Conclusion: Through data simulation test, it is verified that the chained parallel model has higher fitness, convergence speed and shorter running time, and its performance is better than the other two, indicating that the optimization algorithm proposed by the author has good performance.

Isolation and identification of forced degradation products of Febuxostat

AbstractThe current study explains the degradation behavior of Febuxostat API, a non‐purine xanthine oxidase inhibitor used to treat hyperuricemia. A degradation study was carried out as per the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines, and the study confirms that the Febuxostat is largely stable in thermal, photolytic, oxidative, and basic hydrolytic conditions and labile in acid hydrolysis conditions. There were four different degradation products (DPs) found during acid hydrolysis; of these, DPs 2, 3, and 4 are new and have never been reported before, while DP 1 is known and has already been published. All these DPs were identified using ultra‐high‐performance liquid chromatography‐mass spectrometry (UHPLC‐MS) analysis, purified by using preparative HPLC, and characterized using high‐resolution mass spectrometry (HRMS) and nuclear magnetic resonance (NMR) spectroscopy techniques. The formed DPs are by the hydrolysis of the cyano functional group of Febuxostat and the esterification of DP‐1 under acidic conditions. All DP's structural characterization was carried out using NMR spectroscopy and HRMS. The present study describes concrete confirmation of DP structures and it explains the stability behavior of the Febuxostat. The current method is also used to identify DPs with shorter runtime in the future.

Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems

In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime.

UHPLC-MS/MS Assay for Quantification of Legubicin, a Novel Doxorubicin-Based Legumain-Activated Prodrug, and Its Application to Pharmacokinetic and Tissue Distribution Studies.

Legubicin, a novel prodrug based on doxorubicin, has both albumin-binding and legumain-activating properties. The aim of this study was to develop and validate a UHPLC-MS/MS method for investigating the in vivo pharmacokinetics and tissue distribution profiles of legubicin in rats and tumor-bearing mice following intravenous administration, and to compare this prodrug with the positive control drug doxorubicin. The study employed a UHLC-MS/MS method to determine the levels of albumin-bound of legubicin and two metabolites (free Leu-DOX and DOX) in plasma, tumor, and tissue samples. This method was validated for good selectivity, high sensitivity, excellent extraction recovery, and short run time. The results showed that legubicin was present in the circulation in vivo mainly in a protein-bound form with larger AUC values and lower clearance and distribution, and essentially released small amounts of doxorubicin. Compared to administration of equimolar doses of doxorubicin, legubicin showed increased exposure of the active drug in the tumor and decreased the level of the active drug in the heart and kidney. This study provides valuable information on the pharmacokinetics and tissue distribution of legubicin, implicating its potential as a novel and effective drug candidate for anti-cancer therapies.

Development and application of a predictive model for advanced tokamak scenario design

Advanced tokamak (AT) scenarios applying additional heating during the current ramp (early-heating) usually require many iterations if developed fully empirically. To reduce the required experimental time, a model has been developed in the ASTRA framework, capable of doing predictive simulations of the relevant parameters. As scenario development requires fast iterations and inter-discharge runs, a sufficiently short run-time is required. While using a simplified transport model to achieve this, comparisons to experimental data from ASDEX-Upgrade (AUG) still show good agreement. Using this model, a new high performance early-heating AT scenario has been developed and successfully run on AUG with the results matching the predictions.

A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm

The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.

MixedSCNet: LiDAR-Based Place Recognition Using Multi-Channel Scan Context Neural Network

In the realm of LiDAR-based place recognition tasks, three predominant methodologies have emerged: manually crafted feature descriptor-based methods, deep learning-based methods, and hybrid methods that combine the former two. Manually crafted feature descriptors often falter in reverse visits and confined indoor environments, while deep learning-based methods exhibit limitations in terms of generalization to distinct data domains. Hybrid methods tend to fix these problems, albeit at the cost of an expensive computational burden. In response to this, this paper introduces MixedSCNet, a novel hybrid approach designed to harness the strengths of manually crafted feature descriptors and deep learning models while keeping a relatively low computing overhead. MixedSCNet starts with constructing a BEV descriptor called MixedSC, which takes height, intensity, and smoothness into consideration simultaneously, thus offering a more comprehensive representation of the point cloud. Subsequently, MixedSC is fed into a compact Convolutional Neural Network (CNN), which further extracts high-level features, ultimately yielding a discriminative global point cloud descriptor. This descriptor is then employed for place retrieval, effectively bridging the gap between manually crafted feature descriptors and deep learning models. To substantiate the efficacy of this amalgamation, we undertake an extensive array of experiments on the KITTI and NCLT datasets. Results show that MixedSCNet stands out as the sole method showcasing state-of-the-art performance across both datasets, outperforming the other five methods while maintaining a relatively short runtime.

Coupling multi-fluid dynamics equipped with Landau closures to the particle-in-cell method

The particle-in-cell (PIC) method is successfully used to study magnetized plasmas. However, this requires large computational costs and limits simulations to short physical run times and often to set-ups of less than three spatial dimensions. Traditionally, this is circumvented either via hybrid-PIC methods (adopting massless electrons) or via magneto-hydrodynamic-PIC methods (modelling the background plasma as a single charge-neutral magneto-hydrodynamical fluid). Because both methods preclude modelling important plasma-kinetic effects, we introduce a new fluid-PIC code that couples a fully explicit and charge-conserving multi-fluid solver to the PIC code SHARP through a current-coupling scheme and solve the full set of Maxwell's equations. This avoids simplifications typically adopted for Ohm's law and enables us to fully resolve the electron temporal and spatial scales while retaining the versatility of initializing any number of ion, electron or neutral species with arbitrary velocity distributions. The fluid solver includes closures emulating Landau damping so that we can account for this important kinetic process in our fluid species. Our fluid-PIC code is second-order accurate in space and time. The code is successfully validated against several test problems, including the stability and accuracy of shocks and the dispersion relation and damping rates of waves in unmagnetized and magnetized plasmas. It also matches growth rates and saturation levels of the gyro-scale and intermediate-scale instabilities driven by drifting charged particles in magnetized thermal background plasmas in comparison with linear theory and PIC simulations. This new fluid-SHARP code is specially designed for studying high-energy cosmic rays interacting with thermal plasmas over macroscopic time scales.

Customized GRASP for rehabilitation therapy scheduling with appointment priorities and accounting for therapist satisfaction

Physical therapy in acute care hospitals plays an important role in the rehabilitation of patients. Nevertheless, the profession must deal with staff shortages caused by a lack of potential employees and absenteeism which are results of high physical and mental workloads. The therapist shortage negatively affects the total number of daily appointments the department can fulfill. For appointments that can be successfully scheduled, continuity of care with the same therapist cannot be guaranteed for individual patients. Lack of continuity of care negatively influences the therapist's satisfaction. Therapist preferences for individual appointments in general cannot always be guaranteed when designing schedules, which also hurts satisfaction. This paper develops a multi-criteria model for the daily therapy appointment-scheduling problem. The primary objective is to minimize the total sum of priority violations for unscheduled appointments. To improve therapist satisfaction, we consider therapist preferences including continuity of care as a secondary objective. Here, our integer programming formulation aims to minimize the total sum of preference violations for scheduled appointments. We are dealing with an operational planning problem with a daily planning horizon. The operational objective is to achieve therapist schedules in at most two hours. The therapists’ schedules together need to include several hundred appointments for a planning day. Due to intractability, the developed integer program cannot provide schedules for such problem sizes. Therefore, we develop a customized Greedy Randomized Adaptive Search Procedure (GRASP) with six innovative local search operations to improve an initially constructed solution. We test the heuristic algorithm on realistic data instances. The metaheuristic provides high-quality schedules for various problem sizes in short runtimes, i.e., within minutes. Comparisons with the optimal solutions for small problem instances show very good results of the GRASP with a similar number of scheduled appointments and good adherence to continuity of care and therapist preference requirements.