Setup Time Research Articles

Cholecystectomy with the Hugo™ robotic-assisted surgery system: the first general surgery clinical study in Korea.

The Hugo™ Robotic-Assisted Surgery (RAS) System is an emergent device in the robotic surgery field. This study aims to describe the first general surgery-focused clinical study in Korea using the novel Hugo™ RAS System. This study was a prospective, single-center, single-arm, confirmatory clinical study conducted at Seoul National University Hospital where 20 cholecystectomies were performed. To evaluate the safety and performance of the Hugo™ RAS System the incidence of conversion to laparoscopy or open surgery, major complication (Clavien-Dindo Grade ≥ III) rate, overall complication rate, readmission rate, and reoperation rate were evaluated. All parameters were assessed within 30 days post-procedure. Any device deficiencies encountered during our initial experience and device data such as setup, console, and operative times were also reported. We confirmed that our trial achieved the primary objective with a success rate of at least 95%. This was accomplished with no conversions to other types of surgery due to serious system malfunction and with only one major complication within 24 h post-procedure. The 20 consecutively enrolled patients had a median age and BMI of 58 years old and 23.9 kg/m2, respectively. The major complication rate was 10% (2/20 patients), the overall complication rate was 15% (3/20 patients), the readmission rate was 15% (3/20 patients), and the reoperation rate was 0% (0/20 patients). None of the complications were definitively device related. The median setup, console, and operative times were 16, 17, and 55 min, respectively. The device deficiency rate was 15% (3/20 patients), but all device deficiencies were minor, occurred before the first incision, and did not present a risk to the patient. Based on our initial experience with the Hugo™ RAS System, cholecystectomy is feasible and safe. This trial is registered with ClinicalTrials.gov (NCT05715827).

Time reduction in changing of plastic injection mold part bottom cover injection machine size 380 ton

This study focuses on enhancing efficiency by reducing the time required to adjust plastic injection molds on a 380-ton machine. The setup time for mold change was 61 minutes, exceeding the company's target of 30 minutes. The research employed a quasi-experimental approach, capturing pre-improvement data, measuring the time for each mold change, and documenting sub-work steps using a Flow Process Chart and a Man-Machine Chart. The main activities identified were mold change and Jig robot adjustment using the Quick changeover technique, involving internal and external activities. The study revealed that some mold-changing steps could be prepared in advance, and applying SMED (Single Minute Exchange of Die) and ECRS (Eliminate, Combine, Rearrange, Simplify) principles led to the identification of seven external adjustment steps. Assigning another employee to assist in these steps significantly reduced the average mold adjustment time to 28 minutes, representing a 54.09% improvement.

Serial batching to minimize the weighted number of tardy jobs

AbstractThe $$1\vert \text {s-batch}(\infty ),r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j scheduling problem takes as input a batch setup time $$\Delta $$ Δ and a set of n jobs, each having a processing time, a release date, a weight, and a due date; the task is to find a sequence of batches that minimizes the weighted number of tardy jobs. This problem was introduced by Hochbaum and Landy (Oper Res Lett 16(2):79–86, 1994); as a wide generalization of Knapsack, it is $$\textsf{NP}$$ NP -hard. In this work, we provide a multivariate complexity analysis of the $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j problem with respect to several natural parameters. That is, we establish a classification into fixed-parameter tractable and $$\textsf{W}[1]$$ W [ 1 ] -hard problems, for parameter combinations of (i) $$\#p$$ # p = number of distinct processing times, (ii) $$\#w$$ # w = number of distinct weights, (iii) $$\#d$$ # d = number of distinct due dates, (iv) $$\#r$$ # r = number of distinct release dates. Thereby, we significantly extend the work of Hermelin et al. (Ann Oper Res 298:271–287, 2018) who analyzed the parameterized complexity of the non-batch variant of this problem without release dates. As one of our key results, we prove that $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j is $$\textsf{W}[1]$$ W [ 1 ] -hard parameterized by the number of distinct processing times and distinct due dates. To the best of our knowledge, these are the first parameterized intractability results for scheduling problems with few distinct processing times. Further, we show that $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j is fixed-parameter tractable parameterized by $$\#d + \#p + \#r$$ # d + # p + # r , and parameterized by $$\#d + \#w$$ # d + # w if there is just a single release date. Both results hold even if the number of jobs per batch is limited by some integer b.

Methodology to Generate Strategic Manufacturing Indicators to Support Decision-Making – Case Study of a Food Company

This study proposes a methodology that selects key performance indicators and measures their influence on the strategic manufacturing objectives of a food company. The proposed hybrid methodology used the focus model centered on a value function to select criteria and classify key performance indicators and the multi-criteria model DEMATEL to identify the level of influence of key performance indicators on strategic manufacturing objectives. This new method constructs a value function with input from all production line members. This allows the degree of direct and indirect influence of selected key performance indicators to be measured to achieve the organization’s strategic objectives. Finally, seven key performance indicators were selected, the most influential being the level of automation, performance, set-up times, and resource use.

Early prehospital mechanical cardiopulmonary resuscitation use for out-of-hospital cardiac arrest: an observational study

BackgroundThe use of mechanical cardiopulmonary resuscitation device has been very prevalent in out-of-hospital cardiac arrest rescue. This study aimed to investigate whether the timing of mechanical cardiopulmonary resuscitation device set-up correlated with the the outcome of cardiac arrest patients.MethodsWe retrospectively reviewed adult nontrauma cardiac arrest cases in New Taipei City, Taiwan, from January to December 2022. Demographic data, intervention-related factors, and the time variables of mechanical cardiopulmonary resuscitation were collected. The outcomes included the return of spontaneous circulation and 24-hour survival. We compared patients who achieved spontaneous circulation and those who did not with univariate and multivariable regression analyses.ResultsIn total, 1680 patients who received mechanical cardiopulmonary resuscitation were included in the analysis. Reducing the time interval from manual chest compression initiation to device setup was independently associated with the return of spontaneous circulation and 24-hour survival, especially in the subgroup of patients of initial shockable rhythm. Receiver operating characteristic analysis revealed that the outcome of patients with an initial shockable rhythm could be predicted by the mechanical cardiopulmonary resuscitation setup time, with areas under the curve of 60.8% and 63.9% for ROSC and 24-hour survival, respectively. The cutoff point was 395.5 s for patients with an initial shockable rhythm.ConclusionA positive correlation was found between early mechanical cardiopulmonary resuscitation intervention and the outcomes of out-of-hospital cardiac arrest patients. The time between manual chest compression and device setup could predict the return of spontaneous circulation and 24-hour survival in the subgroup of patients with initially shockable rhythm with the optimal cutoff point at 395.5 s.

6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

Scheduling Splitable Jobs on Identical Parallel Machines to Minimize Makespan using Mixed Integer Linear

The scheduling of parallel machines with and without a job-splitting property, deterministic demand,and sequence-independent setup time with the goal of minimizing makespan is examined in this work.For simultaneous processing by multiple machines, single-stage splitable jobs are broken into random(job) sections. When a job starts to be processed on a machine, an operator has to setup the machinefor an hour. By creating two Mixed Integer Linear Programming models, this work proposes amathematical programming strategy (MILP). A MILP model takes the job-splitting property intoaccount. Another model, however, does not include the job-splitting property. This study investigatesthe performance of the proposed models using Gurobi solver. These programs' numerical calculationsare based on actual problems in the Indonesian city of Bandung's plastics industry. On four identicalparallel injection molding machines, 318 jobs must be finished in 22 periods. The real schedulingmethod is contrasted with these two MILP models. The maximum workload imbalance, the maximumrelative percentage of imbalance, and the makespan of these three scheduling systems are used toevaluate their effectiveness. Without the job-splitting property, MILP can handle the real issue ofscheduling identical parallel machines on injection molding machines to reduce makespan, resultingin a 36% average decrease. The MILP model's job-splitting property can reduce makespan by anadditional 2.40%. The order of relative ranking is MILP with job-splitting property, MILP withoutjob-splitting property, and actual scheduling based on the makespan minimization, workloadimbalance, and relative percentage of imbalance.

A Comprehensive Overview on Sustainable Vegetable Gardening: Eco-friendly Approaches to Home Grown Production

Sustainable vegetable gardening is an eco-friendly practice that integrates organic farming principles, resource conservation, and biodiversity promotion to produce nutritious, chemical-free food while minimizing environmental impact. This approach emphasizes soil health through the use of organic amendments, crop rotation, composting, and mulching to improve fertility and water retention. Water conservation techniques, such as drip irrigation and rainwater harvesting, are employed to optimize water use, especially in arid regions. Pest and disease management relies on integrated pest management (IPM), biological controls, companion planting, and the use of organic pesticides to reduce chemical input while maintaining crop health. Sustainable homegrown produce has been shown to have superior nutritional content compared to commercially grown produce, and the avoidance of synthetic chemicals reduces the risk of pesticide residue exposure. Gardening fosters healthy eating habits by connecting individuals to their food sources, encouraging the consumption of fresh, unprocessed vegetables, and contributing to food security. The mental and physical health benefits of gardening, including reduced stress, increased physical activity, and improved mental well-being, further highlight its holistic value. However, challenges such as the initial setup costs, time investment, pest management, and knowledge gaps can hinder widespread adoption. Addressing these challenges requires greater access to resources, educational initiatives, and the development of climate-resilient practices tailored to specific regions. Future trends in sustainable gardening include the adoption of technological innovations, such as smart irrigation systems, automation, and vertical farming, which offer scalable solutions for urban and small-scale gardeners. The COVID-19 pandemic has underscored the importance of resilient, local food systems, and sustainable gardening is poised to play a critical role in addressing future food security concerns.

A Q-learning multi-objective grey wolf optimizer for the distributed hybrid flowshop scheduling problem

Most existing research focuses on a single objective for the distributed hybrid flowshop scheduling problem (DHFSP). This article focuses on a multi-objective DHFSP with sequence-dependent set-up time (DHFSP-SDST). A Q-learning multi-objective grey wolf optimizer (QMOGWO) is designed to optimize the makespan, total energy consumption and total tardiness. A mathematical model for DHFSP-SDST is established. Several initialization strategies and a random method are introduced to improve the quality of the initial population. The new individual is developed by the discrete solution updating mechanism of QMOGWO. Based on the Q-learning, local search strategies are designed to avoid local optima. To verify the performance of the proposed QMOGWO, different scales of instances are tested in various factories and at different stages, and the simulation results show that the QMOGWO outperforms the comparison methods.

Multi-objective scheduling for an energy-efficient flexible job shop problem with peak power constraint

Controlling electricity costs, which are closely related to the peak power reached, while simultaneously reducing completion time and energy consumption, is crucial for achieving sustainability in the manufacturing industry. However, most existing research on energy-efficient flexible job shop scheduling problems primarily focuses on optimizing completion time and energy consumption, often neglecting the critical aspect of controlling electricity costs. With decreasing resources and increasing energy prices, it is necessary to control electricity costs by limiting the peak power reached during production. To address this gap, this paper investigates an energy-efficient flexible job shop scheduling problem with peak power constraint considering setup and transportation time (EEFJSSP-PPST). We establish a mathematical model for EEFJSSP-PPST and propose an improved non-dominated sorting genetic algorithm II (INSGA-II) incorporating several key improvements. We first introduce three scheduling rules in decoding to optimize objectives and employ heuristic rules to generate a high-quality initial population. Then, we propose an innovative cluster crossover method to accelerate convergence and design an adaptive-based local optimization strategy to further enhance performance. Finally, our experiments explore the impacts of different degrees of the peak power constraint on objectives and evaluate the effectiveness of the three scheduling rules. Additionally, the performance of INSGA-II is tested using 135 benchmark instances. The results indicate that INSGA-II outperformed its variations without improvements, achieving the best hypervolume (HV) indicator in 80% of the instances and the best inverted generational distance (IGD) indicator in 68.89% of the instances. Compared to other state-of-the-art algorithms, INSGA-II achieves the best HV and IGD indicators in 82.2% and 81.5% of the instances, respectively, and demonstrates better convergence and a superior Pareto front. Therefore, we conclude that the improvements in INSGA-II are effective and enhance the algorithm’s performance, making it outstanding in solving EEFJSSP-PPST.

B-025 Revolutionizing NGS-driven Laboratory Developed Tests: The impact of automated liquid handling on plate-based bead clean-up

Abstract Background Automated liquid handling systems are proving to be a transformative technology in the landscape of Laboratory Developed Tests (LDTs), especially those utilizing Next-Generation Sequencing (NGS). By enhancing accuracy, boosting efficiency, and offering scalable solutions, these systems enable labs to meet current and future challenges more effectively. The right system not only improves the quality of diagnostics but also positions labs to be agile and adaptive in a rapidly advancing healthcare environment. firefly® is a well-suited liquid handling solution for diagnostics labs developing LDTs due to its compact footprint, user-friendly software and exceptional versatility. Validated protocol templates that support NGS library preparation are readily available from the firefly cloud and can be adapted to meet specific lab needs. For example, automated bead-based clean-ups designed for 96- and 384- NGS applications are available on firefly. These remove DNA fragments of undesirable sizes, enzymes or primers rom the amplified DNA after a PCR reaction. Automating this process facilitates higher throughput and scaling. firefly has a unique combination of 6 positive displacement dispense heads and a 96/384-well air-displacement pipetting head, making it ideal to distribute the reagents required for bead clean-ups. Methods firefly protocols were developed and optimized to be compatible with a range of elution volumes and bead ratios. Protocol performance was tested using library DNA (∼300bp). The same input DNA was plated out and quantified using the QuantiFluor dsDNA system (Promega) before and after running the protocol. This data was used to assess any reduction in DNA concentration and uniformity across the plate. The protocol's ability to successfully modulate the fragment size distribution was demonstrated using a 50bp DNA ladder and different bead ratios. Data was obtained using a Fragment Analyzer (Agilent, DNF-474 HS NGS Fragment Kit). Well-to-well contamination was assessed by running no-template-controls (NTCs) in wells adjacent to library DNA, then analyzed by qPCR using a LightCycler 480 System (Roche, KAPA Library Quantification kit). Results 96 format has a setup time of 10 minutes, run time of 43 and %CV library concentration of <6%. 384 format has a setup time of 10 minutes, run time of 47 and %CV library concentration of <8%. Uniformity of concentration and fragment sizes was seen across the plates. Comparison of the library concentration before and after the clean-up showed a 5% reduction in DNA concentration after the 96-well plate clean-up and an 11% reduction in library concentration when using the 384-well plate clean-up. Fragment size analysis showed that fragment sizes can be successfully modulated on firefly by changing the bead ratio of the firefly run and no detectable well-to-well contamination was detected by qPCR. Conclusions We have demonstrated that automated bead clean-ups on firefly are uniform show uniformity across both 96- and 384-well plates, with no detectable well-to-well contamination, to deliver the consistent and robust results required for an LDT. Bead-based cleanups on firefly offer a fast, 100% walkaway solution. By running 96-samples at once on firefly, we see approximately a 6-fold increase in throughput than when performed manually by a single user.

Is single-port laparoscopy or vaginal natural orifice transluminal endoscopic surgery the better option for salpingo-oophorectomy?

To compare postoperative pain and recovery in patients undergoing oophorectomy with single-port laparoscopic surgery (SPLS) versus vaginal natural orifice transluminal endoscopic surgery (vNOTES). Patients who underwent salpingo-oophorectomy with SPLS or vNOTES between 2016 and 2023 were analyzed retrospectively. Oophorectomy was performed based on the presence of an adnexal mass or breast cancer susceptibility gene mutation. Fifty-two patients underwent oophorectomy with SPLS and 35 underwent vNOTES. Although the mean mass size was slightly larger in the SPLS group than in the vNOTES group (8.0 ± 4.1 vs. 6.8 ± 3.3 cm), the difference was not significant. There was no difference in operating times between SPLS and vNOTES. The mean visual analog scale and faces pain scale scores 2 and 6 h postoperatively were lower in the vNOTES group. The mean quality of recovery-40 (QoR-40) score was higher in the vNOTES group (156 ± 14 vs. 148 ± 11; p = 0.009). This analysis identified vNOTES as an independent predictor of a high QoR-40 score. The vNOTES group experienced less pain during the early postoperative period than the SPLS group. Although the operating and removal times were similar, the port setup time was longer for the vNOTES group.

Overcoming the Learning Curve in Robot-Assisted Spinal Surgery—How Does It Compare to O-Arm Navigation?

Background: Robotic systems have the potential to significantly enhance the accuracy and outcomes of spinal surgery. Adopting this new technology requires an examination of its learning curve and influencing factors. This study analyzes the learning curve associated with using the Mazor X Stealth Edition system for pedicle screw placement and performs a matched-pair analysis to compare operative durations between robot-assisted and navigation-based surgeries, evaluating the efficiency of the robotic system. Methods: We collected retrospective operative data from patients who underwent robot-assisted pedicle screw placements between December 2020 and June 2024 and conducted a cumulative sum (CuSUM) analysis to assess the learning curve, focusing on the robotic system’s setup duration. Additionally, we compared a group of patients who underwent robot-assisted pedicle screw placements with a pair-matched group who underwent O-arm-based navigation-assisted pedicle screw placements. Results: There was a notable decrease in the robotic setup duration, with a significant shift in trend observed after the first 20 cases. While the initial setup time was 24 minutes, it reduced to 17 minutes in later cases, reflecting a marked improvement in efficiency as the surgeon gained more experience with the robot. Conclusion: Our findings indicate there were no added difficulties using the robotic system compared to the navigation system. Moreover, the learning curve for the robotic system can be quickly surmounted, and it offers clear advantages over previous systems, making it a valuable tool for pedicle screw application.

Considering the peak power consumption problem with learning and deterioration effect in flow shop scheduling

This paper investigates the permutation flow shop scheduling problem with peak power constraints under sequence-dependent setup time, learning, and deterioration effects to minimize the makespan, where the peak power consumption satisfies a given upper bound at any time. We establish relevant mathematical models based on the characteristics of the scheduling environment and set up five setup time-based heuristics, including the earliest start time, the latest setup time based on balance job–machine, latest setup time based on balance machine–job, latest setup time insert based on balance job–machine, and latest setup time insert based on balance machine–job. Similarly, a hybrid genetic algorithm combined with simulated annealing is proposed to prevent premature trapping in local optima. The algorithms are evaluated through a large number of data experiments, and the results show that it can effectively solve this scheduling problem.

A Novel Online Position Estimation Method and Movement Sonification System: The Soniccup.

Existing methods to obtain position from inertial sensors typically use a combination of multiple sensors and orientation modeling; thus, obtaining position from a single inertial sensor is highly desirable given the decreased setup time and reduced complexity. The dead reckoning method is commonly chosen to obtain position from acceleration; however, when applied to upper limb tracking, the accuracy of position estimates are questionable, which limits feasibility. A new method of obtaining position estimates through the use of zero velocity updates is reported, using a commercial IMU, a push-to-make momentary switch, and a 3D printed object to house the sensors. The generated position estimates can subsequently be converted into sound through sonification to provide audio feedback on reaching movements for rehabilitation applications. An evaluation of the performance of the generated position estimates from a system labeled 'Soniccup' is presented through a comparison with the outputs from a Vicon Nexus system. The results indicate that for reaching movements below one second in duration, the Soniccup produces positional estimates with high similarity to the same movements captured through the Vicon system, corresponding to comparable audio output from the two systems. However, future work to improve the performance of longer-duration movements and reduce the system latency to produce real-time audio feedback is required to improve the acceptability of the system.

A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

Production and distribution are both crucial components of supply chains. Integrated production and distribution scheduling (IPDS) in the context of flexible assembly flow shop scheduling and batch delivery problems is often overlooked. A realistic problem inspired by the production and distribution processes of a dishwasher factory can be modeled as a resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery (RCFAFSP-BDD). In this problem, order requirements are decomposed into several production tasks processed at different stages of the workshop, and are then delivered in batches via a third-party logistics provider to regional distributors at various locations. Auxiliary resource restrictions, hierarchical coupling constraints, machine eligibility restrictions and sequence-dependent setup times, are incorporated into the problem as operational constraints. To the best of our knowledge, this is the first attempt to solve this problem. This work formulates a mixed-integer linear programming (MIP) model to minimize total costs, including tardiness, inventory, and delivery costs. Given the problem’s strong NP-hard nature, the focus is on developing an efficient solution approach using constraint programming (CP). A CP model is proposed and enhanced with multiple redundant constraints. To reduce runtime, two branching strategies are designed for the CP model. Numerical experiments with varying instance scales reveal that the proposed CP model outperforms the MIP model in accuracy and efficiency within the given time limit. The redundant constraints and search strategy can reduce CP model runtime by up to 263.83%. Compared to manual scheduling at the studied factory, the CP model can cut costs by up to 26.59% for real data, offering viable alternatives for factory planners.

Investigation of the driving power of the barriers affecting BIM adoption in construction management through ISM

Building Information Modelling (BIM) is a rising digital medium that is gaining popularity in the construction sector. Although BIM usage has been mandated in the public construction sector in Saudi Arabia, its adoption remains limited. This paper aims to investigate the driving power of the barriers affecting BIM adoption in construction management through Interpretive Structural Modeling (ISM). A literature review and expert meetings were conducted to identify the potential barriers faced by contractors and consultants in BIM usage in the construction management sector. Two different techniques were utilized to rank the identified barriers to adopting BIM in construction management. The first technique, the Relative Importance Index (RII), evaluates and assesses the barriers from both the contractors and consultants' perspectives. The second technique, Interpretive Structural Modeling (ISM), identifies the driving power of barriers and understands the correlations among them. A total of sixty-nine responses were analyzed using the RII. Cronbach's alpha was utilized to assess the collected data's reliability. The findings from the ISM were validated by experts to establish relationships among the identified barriers. The ISM reveals that the top driving barrier to BIM adoption is the “Lack of skilled and experienced personnel”, followed by “Lack of awareness”, “Communication issues” and “Longer setup time”. The findings of this study could assist local authorities develop an incentive program to encourage contractors and consultants to adopt BIM in construction management.

Scheduling a two-stage assembly problem with separated setup time to minimise total tardiness

The two-stage assembly scheduling problem (TASP) is widely existed in our real-life. Minimising the total tardiness of all jobs is important for increasing customers’ satisfaction. Although a few researchers have focused on such optimisation criterion, the performance of proposed algorithms depends on four or more fine-tuned parameters. This work focuses on TASPs with separated setup time. There are multiple and one machine at stages one and two, respectively. A branch and bound algorithm and a variable neighbourhood search (VNS) are proposed to minimise the total tardiness of all jobs. Both of them contain only one parameter, i.e. maximum CPU time, which needs no turning. In order to guide the search of the branch and bound algorithm, lower and upper bounds and dominance rules are proposed. In order to avoid VNS falling into local optima, three neighbourhoods and a novel shaking subroutine are proposed. Experimental results show that the proposed VNS outperforms all existing algorithms on thousands of large scale TASPs generated randomly. Branch and bound algorithm can not only ensure the optimal schedule for small scale TASPs but also enhance the search ability of the proposed VNS to some extent.

Multi-objective rescheduling of job shop scheduling problems in manufacturing using Quantum Annealing

Unforeseen events in manufacturing necessitate rescheduling to minimize lead times and maintain continuous production. Reducing rescheduling effort is also crucial otherwise additional setup times are incurred. This paper extends a dynamic rescheduling workflow, previously demonstrating success with Quantum Annealing through a multicriterial optimization. It introduces a second objective function considering rescheduling effort alongside makespan. The study addresses job arrivals and machine failures, employing a binary quadratic model for optimization. Results highlight the superiority of Quantum Annealing for larger problems compared to classical metaheuristics. Nevertheless, parameter selection complexities urge further exploration such as exploring deep learning techniques to tackle this challenge.

Evaluation of a Novel Needle Decompression Device Against Standard of Care: A Cadaveric Model

ABSTRACT Introduction Pneumothorax (PTX) incidence in patients arriving to a trauma center can be as high as 20%. The severity of PTX can range from insignificant to life-threatening. Five percent of combat casualties sustaining thoracic trauma have tension PTX (tPTX) at the time of death. Rapid diagnosis and decompression, traditionally with a needle decompression in the prehospital setting, is essential. However, high iatrogenic injury rates reveal a need for a device with the potential to decrease injury rate without compromising decompression success. The Donaldson Decompression Needle (DDN) is a 10-gauge × 3.25 inch needle with a locking mechanism designed to prevent over-insertion. During insertion, a spring-loaded blunt tip retracts, releasing the lock. After penetration of the parietal pleura, the blunt tip projects forward, which in turn locks the device in place on the chest. The device also contains an integrated 1-way valve (OWV) to prevent causing iatrogenic PTX, if placed into a healthy lung cavity. Materials and Methods We compared the DDN against a standard-of-care (SOC) needle (E-ARS 10 gauge × 3.25”) in a cadaveric randomized crossover design study. Tension pneumothorax was created by inserting a needle adjacent to the sternum and insufflating to 15 mmHg. Data collected included participant demographics/experience, cadaver measurements, and procedure data such as intrathoracic pressures and decompression time. Participants also completed a postprocedure survey. A secondary objective was evaluation of decompression rate with the OWV on vs. off. Results Twenty participants were enrolled in the study. Five participants required exclusion. While there were no differences in set-up times for the 2 procedures (DDN vs. SOC, 33 seconds vs. 28 seconds, P = .63), the decompression times were significantly different between the 2 groups (DDN vs. SOC, 2:06 vs. 1:06, P = .019). Evaluation of the secondary outcome resulted in 18 repetitions. The average decompression time for the OWV on group (n = 9) vs. OWV off group (n = 9) was 44 seconds vs. 10 seconds (P=<.05). Conclusions Despite the similar length and gauge of the DDN compared to the standard of care (SOC), the success rate of thoracic decompression was lower for the DDN when compared to the SOC (46% vs. 87%, P = .077) although statistical noninferiority was not established. Additionally, intradevice comparisons indicated decompression with the OWV on significantly prolonged decompression time when compared to when it was removed. It could be appropriate to consider removing the OWV after placement to decrease the decompression time, followed by reattachment for transport. Further research into the ability of the DDN to decrease iatrogenic injury will follow validation of decompression capabilities.