Performance Of Operations Research Articles

SPIDARman: System-Level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors

In nuclear power plants (NPPs), anomalies arising from sensors or human errors (HEs) can undermine the performance and reliability of plant operations. Anomaly detection models can be employed to detect sensor errors and HEs. Additionally, physics-informed machine learning models can utilize the known physics of the system, as described by mathematical equations, to ensure that sensor values are consistent with physical laws. Hence, we propose SPIDARman: System-level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors, a holistic physics-informed anomaly detection approach based on generative adversarial networks (GANs) to detect anomalies in both automatically collected sensor data and manually collected surveillance data. We test our approach on data collected from a flow loop testbed, showcasing its potential to detect anomalies. Results demonstrate that the proposed model performs better than the baseline GAN-based models in detecting sensor and surveillance anomalies, suggesting the potential of physics-informed anomaly detection GAN models in NPPs.

Convolutional neural networks to classify human stress that occurs during in‐field sugarcane harvesting: A case study

AbstractAssessing human stress in agriculture proves to be a complex and time‐intensive endeavor within the field of ergonomics, particularly for the development of agricultural systems. This methodology involves the utilization of instrumentation and the establishment of a dedicated laboratory setup. The complexity arises from the need to capture and analyze various physiological and psychological indicators, such as heart rate (HR), muscle activity, and subjective feedback to comprehensively assess the impact of farm operations on subjects. The instrumentation typically includes wearable devices, sensors, and monitoring equipment to gather real‐time data of subject during the performance of farm operations. Deep learning (DL) models currently achieve human performance levels on real‐world face recognition tasks. In this study, we went beyond face recognition and experimented with the recognition of human stress based on facial features during the drudgery‐prone agricultural operation of sugarcane harvesting. This is the first research study for deploying artificial intelligence‐driven DL techniques to identify human stress in agriculture instead of monitoring several ergonomic characteristics. A total of 20 (10 each for male and female) subjects comprising 4300 augmented RGB images (215 per subject) were acquired during sugarcane harvesting seasons and then these images were deployed for training (80%) and validation (20%). Human stress and nonstress states were determined based on four ergonomic physiological parameters: heart rate (ΔHR), oxygen consumption rate (OCR), energy expenditure rate (EER), and acceptable workload (AWL). Stress was defined when ΔHR, OCR, EER, and AWL reached or exceeded certain standard threshold values. Four convolutional neural network‐based DL models (1) DarkNet53, (2) InceptionV3, (3) MobileNetV2 and (4) ResNet50 were selected due to their remarkable feature extraction abilities, simple and effective implementation to edge computation devices. In all four DL models, training performance results delivered training accuracy ranging from 73.8% to 99.1% at combinations of two mini‐batch sizes and four levels of epochs. The maximum training accuracies were 99.1%, 99.0%, 97.7%, and 95.4% at the combination of mini‐batch size 16 and 25 epochs for DarkNet53, InceptionV3, ResNet50, and MobileNetV2, respectively. Due to the best performance, DarkNet53 was tested further on an independent data set of 100 images and found 89.8%–93.3% confident to classify stressed images for female subjects while 92.2%–94.5% for male subjects, though it was trained on the integrated data set. The comparative classification of the developed model and ergonomic measurements for stress classification was carried out with a net accuracy of 88% where there were few instances of wrong classifications.

Ternary particle composite synergistically enhances the fluidity and dielectric properties of filled thermal conductive epoxy resin

Abstract Particle-packed epoxy materials are widely used to improve the thermal conductivity of insulation materials. However, the addition of fillers increases the viscosity of the composite which reduces its operability, and is not conducive to the packaging of power electronic equipment such as electric transformers and reactors. Multi-scale particle compounding is one of the effective methods to enhance the co-processing performance of materials by reducing the friction between particles and epoxy matrix while forming an effective thermal conductivity network. Three types of spherical alumina sized around 4 μm, 38 μm, and 125 μm were used as fillers to study the fluidity, thermal conductivity, and dielectric properties of single-filled and ternary compounds. The results showed that the ternary particle composite reduced the viscosity of the precursor by up to 68.8%, achieving a synergistic improvement in operability, thermal conductivity, and electrical performance.

The Influence of Loyalty and Productivity on Employee Performance PT. Kreasindo Abadimas Medan Partners

Mitra Kreasindo Abadimas is a private company engaged in the sale and purchase of spare parts and machine repair services that has been operating since 2017. In its operational activities, the company experiences problems and problems that have not been resolved, namely disruption of employee performance due to lack of time to meet needs. The employee and the company are only asked to work continuously. Problems that allegedly cause poor employee performance in company operations are employee loyalty and low employee productivity. Simultaneous hypothesis test results show that simultaneously, loyalty and work productivity have a positive and significant effect on employee performance at PT. Kreasindo Abadimas Partners. The result of partial hypothesis test shows that partially, each variable of loyalty and work productivity has a positive and significant effect on employee performance at PT. Kreasindo Abadimas Partners.

The neuropharmacological profile of interval responding during operant tasks.

Responses occurring during intervals of operant tasks have been subdivided as interim, facultative, and terminal, depending on the time between response onset and reward. Although interval responses, also known as adjunctive responses, have been described in pigeons, rats, mice, monkeys, and humans, most experiments have been conducted in rats. We review the neurochemical basis of interval responses and examine the hypothesis that these responses modulate operant performance. Preliminary experiments indicate the involvement of biogenic amines, acetylcholine, and GABA during interval responding associated with operant tasks. In particular, catecholaminergic deafferentation of the basal ganglia modulated interval responses as did the peripheral injection of catecholamine reuptake blockers. Under the influence of amphetamine, interval responding may either increase or decrease, so that a wide range of responses must be selected to gauge drug effects. In non-drugged pigeons and rats, the expression of interval responses facilitates operant training.

Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration Space

Mainstream LSM-tree-based key-value stores face challenges in optimizing performance for point lookup, range lookup, and update operations concurrently due to their constrained configurations. They typically follow fixed patterns to specify the level capacity and the number of sorted runs per-level. This confines their designs to a restricted space, limiting opportunities for broader optimizations. To address this challenge, we consider a more flexible configuration that enables independent adjustments of the number of runs per-level, size ratio, and Bloom filter settings at each LSM-tree level. By carefully analyzing the cost of each operation based on the new design space, we unveil two critical insights for optimizing the tradeoff among the three operations. Firstly, achieving efficient point lookup requires a large last level. Secondly, there is a specific correlation between the number of runs per level and size ratio that is advantageous for overall update and range lookup performance. Based on these insights, we introduce Moose, a structure delivering an impressive overall performance for point lookup, range lookup, and update concurrently. Furthermore, we also introduce a new framework, Smoose, to navigate the design space for adapting specific workloads. We implemented Moose and Smoose on top of RocksDB and experimental results demonstrate that our proposed approach outperforms state-of-the-art LSM-tree structures across diverse workloads.

Nonlinear time-reversal interferometry with arbitrary quadratic collective-spin interaction

Abstract Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information science. Here we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generated by arbitrary quadratic collective-spin interaction, which could be described by the Lipkin–Meshkov–Glick (LMG) model. We optimize the squeezing process, encoding process, and anti-squeezing process, finding that the two particular cases of the LMG model, one-axis twisting and two-axis twisting outperform in robustness and precision, respectively. Moreover, we propose a Floquet driving method to realize equivalent time reverse in the atomic system, which leads to high performance in precision, robustness, and operability. Our study sets a benchmark for achieving high precision and high robustness in atomic nonlinear interferometry.

Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model

Performance assessment of Intercity Road Freight Transport (IRFT) involve a complex set of considerations to ensure the efficient transportation of goods between their origins and destinations. Notably, Data Envelopment Analysis (DEA) models are often the preferred approach for conducting assessment studies that involve multiple inputs and outputs. Even though the conventional Cross-Efficiency (CE) technique addresses the limitations of DEA in producing comparable results, it still fails to consider the evaluation of internal system operations. The use of Network CE leads to more realistic results in assessing problems. It addresses not only the limitations of the DEA model in producing comparable results but also considers the performance of internal operations and system efficiency. Incorporating CE into a Parallel Network System (PNS), which is known as CE-PNS, has also a significant drawback as it neglects both the optimistic and pessimistic viewpoints based on the efficient and anti-efficient frontiers simultaneously, and may therefore fail to provide a comprehensive analysis. The primary objective of this study is to propose a new double-frontier network CE that takes into account two perspectives simultaneously. Indeed, this study is the first to incorporate the concepts of CE and double-frontier DEA into a PNS to provide an effective tool for assessing Iranian IRFT. For this purpose, the Cross-Inefficiency (CIE) technique is first incorporated into the PNS, called CIE-PNS, which explores the relationship between system inefficiency and internal divisions. The CE-PNS and CIE-PNS may yield different results. Therefore, Double-Frontier CE for a PNS (DFCE-PNS) is derived by combining the efficiency results obtained from CE-PNS and CIE-PNS. This study also examines different inputs and outputs to assess the performance of Iranian IRFT. The results demonstrate that DFCE-PNS yields more comprehensive and reliable results.

Designing of self balancing amplitude modulated five level inverter for reducing voltage stress gradients on converter switches for electric vehicles

The research article endeavors to explore the efficient multi-quadrant operations in Electric Vehicles (EV), which emphasizing among the renewable sources and Energy Storage Management Systems (ESMS). This coordination is facilitated through the latest advancements in converter circuits, aiming to enhance efficiency and performance in EV operations. The primary converting circuit, or inverter, is crucial in optimizing the utilization of energy sourced from renewable sources. The coordination among these systems along with the converter, produces harmonics, due to the shifting of operating modes in EV motors in quick sessions because of dynamic real-time road conditions. This research paper presents the design and implementation of Self Balancing Modulated Five-Level Inverter along with a powerful modulation technique and coordinated with the ESMS of electric car application. The proposed 5-Level inverter shows the reduction in torque distortions in an electric vehicle drives over conventional two-stage boost inverter (2SBI) circuits. The designed Five-Level Inverter (FLI) circuit characterized by reduced switching operations, shows a significant reduction in voltage stress gradients on switches and achieves self-balancing of voltages with reduced operating components. The developed three-phase boost single circuit named as SBAM-FLI is connected to the grid integrated renewable energy application and the system is tested at constant and variable torque conditions. A comparative analysis is conducted between the operation of self-balancing modulated FLI with 2SBI based on the obtained results and power loss analysis is performed for the performance evolution in EV applications. The proposed topology is adaptable to both the Trapezoidal and Sinusoidal Back EMF drive mechanisms, which shows significant improvement over conventional inverters. The experimental validation of proposed self-balancing modulated FLI design is presented in this research. The experimental setup was realized using Xilinx block sets and seamlessly integrated with the Basys-3 Artix-FPGA board, consequently, both the experimental and simulation outcomes of the proposed converter topology were validated and provides substantial impact on EV applications.

Повышение ресурса рабочих колес центробежных насосов шахтного водоотлива

Mining The article discusses the main failure causes of centrifugal pumps used in the mine dewatering plants. The factors causing intensive wear of the impellers used in centrifugal pumps of the CNS-33-66...330 type are analysed, as well as the ways of increasing their serviceability. The gas dynamic cold spraying is recognized as the most promising and efficient method of applying functional coatings, which makes it possible to improve the condition of the surface layer and eliminate surface defects of the initial impeller blanks, which have a significant impact on the serviceability of the parts. The main research trends are outlined in the studied area, and practical recommendations are provided on using the gas dynamic cold spraying methid both in the pump manufacturing process and in their overhaul. It is shown that one of the promising trends in development the gas dynamic cold spraying applications is the creation of multilayer coatings on the impeller blades based on compositions of various metal-ceramic powders. Along with the hardening treatment, this combination will allow to provide the impellers with a set of necessary functional characteristics for the hydro-abrasive wear conditions. technological process as a sequence of changing the natural state of the Earth's subsoil to obtain a certain quantity and quality of mineral products is characterized by departures from the specified normative parameter values, which is due to causes of different nature. Most of these causes are man-made, anf they are created during the performance of specific process operations by the personnel. The article presents an original approach to managing the control over the technological process parameters in a coal strip mine. To reduce the magnitude and probability of the process departures from the normative values, the authors propose to create technical and organizational control loops. Each loop is a closed chain of technical means and organizational tools, which secures up-keeping of the normative technological process state. Production risk is proposed to be used as a criterion to assess the efficiency of the control loops. The methodology allowing to calculate the risk is described. The results of the contour approach application to the technological process control at the Solntsevsky coal strip mine are presented.

Формирование контуров контроля технологического процесса угольного разреза

Mining technological process as a sequence of changing the natural state of the Earth's subsoil to obtain a certain quantity and quality of mineral products is characterized by departures from the specified normative parameter values, which is due to causes of different nature. Most of these causes are man-made, anf they are created during the performance of specific process operations by the personnel. The article presents an original approach to managing the control over the technological process parameters in a coal strip mine. To reduce the magnitude and probability of the process departures from the normative values, the authors propose to create technical and organizational control loops. Each loop is a closed chain of technical means and organizational tools, which secures up-keeping of the normative technological process state. Production risk is proposed to be used as a criterion to assess the efficiency of the control loops. The methodology allowing to calculate the risk is described. The results of the contour approach application to the technological process control at the Solntsevsky coal strip mine are presented.

Generalizing the Enhanced-Deep-Super-Resolution Neural Network to Brain MR Images: A Retrospective Study on the Cam-CAN Dataset.

The Enhanced-Deep-Super-Resolution (EDSR) model is a state-of-the-art convolutional neural network suitable for improving image spatial resolution. It was previously trained with general-purpose pictures and then, in this work, tested on biomedical magnetic resonance (MR) images, comparing the network outcomes with traditional up-sampling techniques. We explored possible changes in the model response when different MR sequences were analyzed. T1w and T2w MR brain images of 70 human healthy subjects (F:M, 40:30) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository were down-sampled and then up-sampled using EDSR model and BiCubic (BC) interpolation. Several reference metrics were used to quantitatively assess the performance of up-sampling operations (RMSE, pSNR, SSIM, and HFEN). Two-dimensional and three-dimensional reconstructions were evaluated. Different brain tissues were analyzed individually. The EDSR model was superior to BC interpolation on the selected metrics, both for two- and three- dimensional reconstructions. The reference metrics showed higher quality of EDSR over BC reconstructions for all the analyzed images, with a significant difference of all the criteria in T1w images and of the perception-based SSIM and HFEN in T2w images. The analysis per tissue highlights differences in EDSR performance related to the gray-level values, showing a relative lack of outperformance in reconstructing hyperintense areas. The EDSR model, trained on general-purpose images, better reconstructs MR T1w and T2w images than BC, without any retraining or fine-tuning. These results highlight the excellent generalization ability of the network and lead to possible applications on other MR measurements.

THE PROBLEM OF REGULATING FUNGICIDES ON THE SKIN SURFACE FOR OCCUPATIONAL SKIN DISEASE IN AGRICULTURAL WORKERS’ RISK PREDICTION

The aim. To establish the norms of fungicides on the skin surface and predicting the risk of occupational skin pathology in agricultural workers to develop measures to prevent diseases caused by dermal exposure to pesticides. Materials and methods. The natural experiment was performed in accordance with modern requirements (European Food Safety Authority (2022), and the risk assessment was carried out according to the Recommendations (Approved by the Ministry of Health of Ukraine No. 324 issued on 13.05.2009) with different methods of spraying techniques: rod, air blast fan spraying, pre-sowing, aviation, knapsack treatment. 17 series of natural experiments were performed, and the air of the working zone area (n =56) and dermal exposure (n =56) were analyzed for each worker with different pesticide application methods. Quantitative determination of pesticide content was carried out by gas-liquid and high-performance liquid chromatography methods. The results were statistically processed using a package of licensed statistical programs MedStat v.5.2 (Copyright © 2003-2019) and Microsoft® Excel® for Microsoft 365 MSO. Results. It was established that there is pesticide contamination, mainly of the gloves of tank filling operators when preparing working solutions. No contamination of the skin surface was detected; only pesticide residues were present on the surface of the workers’ overalls. A comparison of the exposure dose in case of percutaneous exposure during the performance of technological operations with different methods of processing, dermal equivalents of Acceptable Operator Exposure Level of pesticide to workers’ skin (DE AOEL) and the allowable dermal dose for professional contingents (ADderm) and the hazard coefficients by dermal exposure (HCderm) showed that that dermal exposure during individual technological operations with different processing methods had a reliable difference between processing methods, but there is no difference within one application method. Conclusions. During the comparison of the hazard coefficients for dermal exposure, calculated according to different models, no significant difference was found between the risks calculated according to DE AOEL and ADderm for individual technological operations in various treatment methods, except for the operator who performed knapsack treatment and the risk calculated according to ADderm is significantly lower compared to the DE AOEL risk.

Multi-objective optimization strategy for regional multi-energy systems integrated with medium-high temperature solar thermal technology

The high proportion and volatility of renewable energy pose a significant challenge to efficient collaboration between photovoltaic/thermal and wind power in multi-energy systems. Therefore, this paper proposes a Regional Multi-Energy System (RMES) based on Medium-High Temperature Solar Thermal (MHTST) technology. The novelty of the solution integrates dispatchable photovoltaic and photothermal conversion pathways, facilitating flexible and controllable utilization of solar energy via installed Thermal Energy Storage (TES). A multi-objective optimization model is developed to ensure the optimal operation of the proposed system under typical seasonal scenarios. The method that combines Branch-and-Reduce with Multi-Learning-Task is used to handle the formulated Mixed Integer Nonlinear Programming (MINLP) and swiftly identify optimal Pareto solutions. Case studies are performed over the improved united communities of the Test Region for Energy Transition and Comprehensive Reform in Taiyuan, China. Compared with the conventional photovoltaic technology, the simulation results show that the proposed solution has superior performance in both winter-heating and summer-cooling operations across 24-hour scheduling periods: operating costs reduced by 12.5% and 18.7%, carbon emissions decreased by 7.4% and 21.4%, with local solar power accommodation increased by 263.89kW and 487.47kW, respectively.

Barriers and Challenges of Sustainable Logistics in Automobile Industry

Study Background and Objectives The automobile industry is a significant contributor to global environmental concerns. Vehicle production, transportation, and end-of-life management all have substantial environmental footprints. Sustainable logistics practices offer a path towards a greener future for industry. However, implementing these practices faces numerous challenges. This study aims to investigate the barriers and challenges hindering the adoption of sustainable logistics practices within the automobile industry. The specific objectives are: · To identify the key barriers faced by automobile manufacturers and their logistics partners in implementing sustainable practices. · To analyse the impact of these barriers on the environmental performance of the industry's logistics operations. · To develop recommendations for overcoming these barriers and promoting sustainable logistics practices within the automobile sector. Research Design This study employs a literature review methodology to analyze existing research on the barriers and challenges of sustainable logistics in the automobile industry. Relevant academic journals, conference proceedings, and industry reports are reviewed to identify key themes and insights. · Conduct a systematic and comprehensive review of relevant academic journals, industry reports, and government publications. · Utilize appropriate search engines and databases to identify relevant literature. · Analyse the collected data to identify key themes, trends, and research gaps. · Synthesize the findings to provide a comprehensive understanding of the barriers and challenges of sustainable logistics in the automobile industry. Major Findings The study reveals a growing consumer interest in the auto industry's commitment to sustainability. While price, performance, and safety remain significant purchase factors, a significant portion of consumers consider a car manufacturer's environmental practices when deciding. However, a knowledge gap exists, with many consumers lacking awareness of the environmental impact of traditional logistics practices. The research also highlights the importance of collaboration between car manufacturers, logistics companies, and policymakers in achieving significant progress towards sustainable practices. Standardized sustainability metrics and government incentives are seen as crucial factors for long-term success.

Enhancing high-speed CNC milling performance of Ti6Al4V alloy through the application of ZnO-Ag hybrid nanofluids

Abstract This research investigates the performance of high-speed CNC milling operations on Ti6Al4V alloy by employing a novel ZnO-Ag hybrid nanofluid. The study involves the preparation and characterization of nanofluids with varying concentrations of nanoparticles, focusing on thermal conductivity and stability. The machining experiments encompass four critical input parameters: Minimum Quantity Lubrication (MQL) flow rate, cutting speed, nanofluid concentration, and feed rate. Performance evaluation is based on average surface roughness (Ra) and cutting temperature. Key findings include a remarkable 21.05% improvement in thermal conductivity for the ZnO-Ag-based sunflower oil at 0.2% volume concentration compared to 0.05% concentration. The prepared nanofluids exhibit good stability. Moreover, cutting speed and MQL flow rate emerge as significant contributors to Ra, accounting for 35.62% and 34.82%, respectively. Interestingly, MQL flow rate is identified as the most influential factor, surpassing even cutting speed. SEM images for tool wear reveals that the ZnO-Ag based sunflower oil reduced tool wear significantly. In conclusion, the proposed ZnO-Ag-based sunflower oil at 0.2% concentration emerges as the good option for sustainable high-speed machining of Ti6Al4V alloy, showcasing

Sensitivity, linearity, and noise evaluation of L‐shaped dielectrically modulated label free tunnel field‐effect transistor biosensor

AbstractThis research article examines a L‐shaped dielectrically modulated label free TFET (L‐DM‐TFET) biosensor for the purpose of detecting different biomolecules using a label‐free biosensing detection technique. The proposed structure allows for the recognition of biomolecules by modulating various electrical properties, such as the drain current, transconductance, and linearity parameters. The source region of the proposed TFET incorporates a SiGe (source)/Si (channel) heterojunction, utilizing a low bandgap material of SiGe. This heterojunction is employed to enhance the ON‐state current of the devices. The materials used and the fabrication steps involved in our proposed device are compatible with complementary metal‐oxide‐semiconductor (CMOS) technology. This analysis is conducted using a calibrated Silvaco technology computer‐aided design (TCAD) simulator. Additionally, by considering a dielectric constant range of 1–12, we calculate various figure of merits (FOMs) parameters for the device. These include evaluation of linearity, sensitivity, and noise characteristics. Furthermore, we have conducted an analysis of linearity FOMs, such as VIP2, VIP3, IIP3, and IMD3 for the proposed device under study. Additionally, the linearity analysis of the presented tunneling FET (TFET) indicates the device's excellent performance in distortionless switching operations. Consequently, the L‐shaped dielectrically modulated biosensor holds potential suitability for high‐speed circuit designs.

Laparoscopic vs robotic inguinal hernia repair: a comparison of learning curves and skill transference in general surgery residents.

There is no consensus on whether laparoscopic experience should be a prerequisite for robotic training. Further, there is limited information on skill transference between laparoscopic and robotic techniques. This study focused on the general surgery residents' learning curve and skill transference within the twominimally invasive platforms. General surgery residents were observed during the performance of laparoscopic and robotic inguinal hernia repairs. The recorded data included objective measures (operative time, resident participation indicated by percent active time on console or laparoscopy relative to total case time, number of handoffs between the resident and attending), and subjective evaluations (preceptor and trainee assessments of operative performance) while controlling for case complexity, patient comorbidities, and residents' prior operative experience. Wilcoxon two-sample tests and Pearson Correlation coefficients were used for analysis. Twenty laparoscopic and forty-four robotic cases were observed. Mean operative times were 90min for robotic and 95min for laparoscopic cases (P = 0.4590). Residents' active participation time was 66% on the robotic platform and 37% for laparoscopic (P = < 0.0001). On average, hand-offs occurred 9.7 times during robotic cases and 6.3 times during laparoscopic cases (P = 0.0131). The mean number of cases per resident was 5.86 robotic and 1.67 laparoscopic (P = 0.0312). For robotic cases, there was a strong correlation between percent active resident participation and their prior robotic experience (r = 0.78) while there was a weaker correlation with prior laparoscopic experience (r = 0.47). On the other hand, prior robotic experience had minimal correlation with the percent active resident participation in laparoscopic cases (r = 0.12) and a weak correlation with prior laparoscopic experience (r = 0.37). The robotic platform may be a more effective teaching tool with a higher degree of entrustability indicated by the higher mean resident participation. We observed a greater degree of skill transference from laparoscopy to the robot, indicated by a higher degree of correlation between the resident's prior laparoscopic experience and the percent console time in robotic cases. There was minimal correlation between residents' prior robotic experience and their participation in laparoscopic cases. Our findings suggest that the learning curve for the robot may be shorter as prior robotic experience had a much stronger association with future robotic performance compared to the association observed in laparoscopy.

Sustainable vehicle allocation decisions under a vertical logistics collaboration setting

Enhancing vehicle utilization through a collaborative logistics approach allows businesses to contribute to the three pillars of sustainability by reducing fuel consumption and emissions, enhancing profit margins, and increasing customer satisfaction. This paper aims to introduce an Integer Linear Programming model for solving a vehicle allocation problem within a proposed vertical logistics collaboration setting. In this collaboration setup, production capacity allocation decisions are jointly made by collaborating customers and the logistics service provider, in order to create a more balanced and profitable logistics plan for all involved parties. Through the application of the proposed mathematical model, our numerical analyses illustrate the effects of the vertical logistics collaboration on the performance of transportation operations, including improved vehicle utilization rate and increased satisfied demand amount. Besides, we analyze the outputs of this operation related to the economic (by focusing on profit growth), environmental (by measuring the reduction in emissions), and social (by assessing the discounted transportation costs for customers) aspects of sustainability. To the best of our knowledge, no current study in the literature has established the vehicle allocation problem in the context of vertical logistics collaboration across all three sustainability pillars at the same time. The model can also serve as a tool for evaluating the potential economic, environmental, and social benefits arising from vertical collaboration and joint projects with customers, along with the support offered to logistics decisions.

Crowd Evacuation Management with Optimal Time‐Multiplexed Exit‐Choice Recommendations through Simulation Optimization

AbstractExit‐choice behavior significantly influences the performance of egress operations in crowded spaces. Thus, strategically issuing active exit‐choice recommendations that provide clear and concise guidance to pedestrians can help improve evacuation safety and efficiency. This study investigates how to optimally regulate pedestrian density at the exits by generating time‐multiplexed exit‐choice recommendations. The study develops a simulated crowd evacuation scenario with multiple exits, where pedestrians receive periodic sequences of exit‐choice instructions. The optimal period for each instruction is obtained through simulation‐optimization processes with three minimization objectives: evacuation time, cumulative density, and cumulative density variance. First, the effect of collective behaviors on evacuation performance is studied, which justifies the need for active mechanisms to improve exit‐choice decision‐making coordination. Then, several optimal plans for the different objectives are obtained and evaluated regarding evacuation time and cumulative density performance. Finally, the influence of adoption rate and collective commitment to instructions are evaluated. This study suggests that static plans, defined as time‐multiplexed exit‐choice instructions obtained through simulation‐optimization, reasonably represent a first step in the real deployment of intelligent crowd management systems.