Schedule Coordination Research Articles

Build your own all-virtual multidisciplinary kidney stone clinic.

With the dramatic rise of telemedicine in the post coronavirus disease 2019 (COVID-19) pandemic, our objective was to develop a totally virtual multidisciplinary kidney stone clinic and assess patient satisfaction of this format. The virtual multidisciplinary stone clinic began July 2021 and continued monthly. Prior to the beginning of each clinic, providers from the urology, nephrology, and dietitian teams meet virtually to discuss the patients. Patients would then log into WebEx virtual platform and providers would subsequently log into the patient's virtual room, to review radiology, laboratory results, and dietary logs then provide counseling. Patients were then sent a survey via electronic mail regarding their experience. A 5-point Likert scale was used for responses ranging from strongly disagree to strongly agree. Scores were averaged to rank results. A total of 122 patients were sent surveys, and a total of 31 surveys were completed. Sixty-one percent of patients strongly agree and 13% agree that they felt comfortable using the virtual platform. When asked if they prefer using the virtual platform for their visit, 70% of patients agreed or strongly agreed and only 16% of patients disagreed or strongly disagreed. In regards to potential advantages of a virtual visit, the Likert scores were averaged and ranked from most to least important with improved timeliness (3.7) and ease of scheduling into day (3.6) the highest rated advantages. Most patients did not find any concerns using the virtual platform, however the ability to see the provider in-person and connecting personally was of highest concern with an average Likert score of 2.3. Overall, 83% of patients agreed or strongly agreed that the multidisciplinary stone clinic satisfied their kidney stone related questions regarding treatment and prevention. A virtual multidisciplinary kidney stone clinic can be implemented with high patient satisfaction scores and help overcome the limitations of physical clinic space and provider schedule coordination. There are few disadvantages to using the platform.

Aperiodic Coordination Scheduling of Multiple PPLs in Shipboard Integrated Power Systems

Aperiodic Coordination Scheduling of Multiple PPLs in Shipboard Integrated Power Systems

Semiconductor production scheduling and industrial economic coordination based on deep learning and heat loss optimization

With the growing global focus on energy efficiency and environmental impact, the semiconductor industry faces the dual challenge of improving production efficiency and reducing energy consumption. Heat loss is an important source of energy consumption in semiconductor production, so optimizing heat loss is of great significance. This paper aims to explore the optimization method of semiconductor production scheduling based on deep learning technology, with special attention to the reduction of heat loss, so as to achieve the coordinated development of industrial economy. The source of thermal energy loss in semiconductor production is analyzed and a deep learning model is constructed to predict thermal energy loss under different production scheduling schemes. Through deep learning training of production data, the model can identify and optimize the thermal energy consumption patterns of each link in the production process. In order to achieve the balance between minimum heat loss and maximum production efficiency, a variety of production scheduling schemes are optimized using simulated annealing algorithms. The experimental results show that the production scheduling scheme based on deep learning can effectively reduce heat loss and optimize production efficiency. Compared with traditional scheduling methods, the optimized system has reduced thermal energy loss and increased production efficiency. These results demonstrate the potential of deep learning in semiconductor production scheduling, significantly optimizing heat loss and promoting the coordinated development of the industrial economy.

Optimal capacity and multi-stable flexible operation strategy of green ammonia systems: Adapting to fluctuations in renewable energy

The potential of power-to-ammonia is increasingly recognized as a large-scale renewable electrical energy storage technology in the energy-transition landscape. Unlike conventional, continuously operating Haber-Bosch production processes, power-to-ammonia processes face operational challenges stemming from fluctuations in renewable power supply. Systematic strategies that can adapt to these fluctuations are required to improve the operational flexibility and stability of power-to-ammonia processes for industrial applications. This study proposes a multi-stable flexible ammonia operation strategy, which is practical and feasible in engineering. To maximize net profit, a design scheduling coordination optimization model is established and solved using a two-stage algorithmic framework of particle swarm and mixed integer linear programming. Additionally, three flexible scenarios are designed for the simulation analysis of a grid-connected green ammonia system. Compared with the traditional constant inflexible scenario, the multi-stable flexible scenario increases annual net profit by 5.05 M$ and reduces carbon emission intensity by 1.61 kg CO2/kgNH3. Furthermore, compared with the continuous flexible scenario, the volatility of the ammonia production load is reduced by 79.89 %. The multi-stable flexible operation strategy balances the green ammonia system’s economic, safety, and low-carbon attributes. Sensitivity analysis reveals that wind and photovoltaic complementary power generation reduces green ammonia costs and carbon emissions. When the selling price of ammonia exceeds 550 $/t, ammonia production and sales should be prioritized, but at lower ammonia prices, selling electricity to the grid to optimize economic efficiency is advised. This study provides novel insights into the flexible operation of the power-to-ammonia process and is expected to offer guidance for constructing and operating green ammonia systems.

Probabilistic Coordination of Optimal Power Management and Voyage Scheduling for All-Electric Ships

Probabilistic Coordination of Optimal Power Management and Voyage Scheduling for All-Electric Ships

Horizontal and Vertical Integration of LOAC Into Military Academy Curriculum

Abstract This article signals the need for an integrated approach to curriculum development and implementation with a focus on a group of disciplines related to the Law of Armed Conflict (LOAC). By highlighting various teaching approaches of LOAC, I argue that there is a risk that LOAC be perceived by many non-legal practitioners, like the cadets, as difficult to grasp, interpreted and corelated with real situations they will face daily in operations, exercises or even during execution of routine peace-time duties. I support the idea that the LOAC, as a horizontal discipline in a military academy curriculum, need to be integrated dynamically, both horizontally (in relation to other operational disciplines) and vertically (along the academic path of a military cadet) into the overall construct of learning the basics of the profession of arms. To operationalise this finding, I propose an integrated educational model that suggests horizontal coordination of the academic schedule and enhanced cooperation among the teaching staff of the relevant disciplines.

An optimized cooperative data transmission scheme in relay‐enabled cellular communication networks

SummaryCooperative transmission is of great significance in data transmission and resource schedule in mobile cellular communication networks. To attain the desired network transmission performance objectives over the whole cellular network system, we propose an appropriate cooperative data transmission scheme to optimize the data delivery efficiency in terms of the transmission time consumption for each and every transmission group. The proposed transmission optimization scheme includes the tasks of determining the transmission group, allocating the channel rate resource, specifying the cooperative transmission mechanism, and performing resource allocation and schedule as well. Firstly, we propose a utility function based rate allocation scheme to optimize the channel rate resource utilization for all those transmission groups over the cellular communication network. Based on it, we further present a cooperative data transmission scheme to deal with the data delivery and reception for all those subgroups with high efficiency over the whole network system. Particularly, we propose a resource efficiency function‐based coordination scheduling (EFCS) scheme to conduct the cooperative data transmission and resource schedule as well for each transmission group within the network. The EFCS scheme facilitates to determine the subgroups for schedule over each scheduling cycle, which can produce the best data transmission efficiency across both the direct and cooperative transmission hop. Both the theoretical analysis and the simulation evaluation results verify the superior performance of our proposed cooperative data transmission scheme.

Revolutionizing Student Accommodation: A MERN-Based Rental System For Seamless Property Management

This web application facilitates the registration of individual homes or apartments to streamline the process of finding ideal rental properties. Users can conveniently search for their next rental in targeted areas through a user-friendly interface. The platform is designed to cater to diverse real estate needs in India, including property buying, selling, and leasing. It serves as a valuable tool for investment by maintaining a comprehensive database of various properties and agent information. Not only does it assist users in accessing updated property information globally, but it also enables agents to manage and update their listings. The platform alleviates the complexities associated with traditional property search methods by offering a simplified online form, minimizing the need for individual agent communication, appointment scheduling, and time coordination

Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode

As the product mix in production changes dramatically, cell reconfiguration is requisite to smoothen the production process. Meanwhile, multiple production modes are simultaneously adopted in the site to promote productivity and assure flexibility, and thus the coordination scheduling among them becomes a challenging problem. To achieve cell reconfiguration and cell scheduling in a cellular manufacturing system in which no-idle flow-line and flexible job-shop production modes are hybridized, a mixed integer linear programming model is formulated and an enhanced adaptive multi-objective evolutionary algorithm is developed. In the proposed algorithm, a decision tree-based rule combination selector is developed to adaptively select the most appropriate rule combination fit for the given production scenario to generate a high-quality initial population. Three types of crossover operators and six objective-oriented local search operators are designed to increase the exploration and exploitation capability. An adaptive balance mechanism of exploration and exploitation is trained by Q-learning to maximize search efficiency. In addition, an adaptive adjustment mechanism of population size is designed to ensure diversity and speed up convergence. The comparative study demonstrates that the three proposed adaptive mechanisms are effective and the proposed algorithm with three adaptive mechanisms significantly outperforms other comparison algorithms in solving the studied problem.

Dynamic scheduling of decentralized high-end equipment R&D projects via deep reinforcement learning

Distributed research and development (R&D) plays a pivotal role in high-end equipment manufacturing enterprises, significantly impacting interdisciplinary innovation and expediting development cycles. In the uncertain environment characterized by random arrivals of new projects, achieving dynamic scheduling and real-time coordination for decentralized R&D projects is a challenging problem. This paper studies a dynamic decentralized resource-constrained multi-project scheduling problem with product transfers (DDRCMPSP-PT). The objective is to minimize the development cycle after initial local schedules are generated to minimize the individual project makespan. To tackle the problem, we develop a decentralized multi-agent system using the dynamic coordination mechanism for resource assignment (DMAS/DCMRA). An up-to-date deep reinforcement learning (DRL) algorithm, dueling double deep Q-learning (D3QN) with prioritized replay, is employed to select the optimal strategy to resolve the global resource conflicts adaptively. Two priority rules are presented based on the structural properties and make up the action space of the DRL agent together with ten high-quality priority rules. Extensive simulation experiment results based on real enterprise cases reveal that the DMAS/DCMRA outperforms all traditional priority rule-based methods. The proposed priority rules also show competitive performance compared to other priority rules, which validates the applicability and superiority of the proposed method. This research offers a new agile project management technique for organizations running multiple R&D projects and managing crucial bottleneck resources.

Too Many Meetings? Scheduling Rules for Team Coordination

Workers in knowledge-intensive industries often complain of having too many meetings, but organizations still give little thought to deciding when or how often to meet. We investigate the efficiency and robustness of various coordination scheduling rules. We consider workers who are engaged in a common activity (e.g., software programming) that can be divided into largely independent, parallel production tasks, but that necessitates periodic coordination. Coordination enables workers to address issues they have encountered in their independent work but takes time away from production. Using a stylized game-theoretic model, we show that small teams allow a more fluid, that is, worker-driven, approach to scheduling coordination, such as preemptive coordination (or production), under which any worker can impose coordination (or production). In larger teams this becomes inefficient. Several approaches can mitigate this effect. One option is to allocate the decision rights to produce or coordinate to the most productive worker. A more general version is to implement a voting-based scheme, where a minimum number of workers from a predetermined subset choose to coordinate. A third approach is to modify the preemptive coordination and production rules by adding time-based controls, to reserve some minimal amount of productive time or to enforce coordination after some point. Finally, a fixed-interval meeting schedule works well for very large teams. Our research helps formalize the tension between meeting (coordinating) and producing and indicates how to adapt team coordination scheduling rules to the degree of worker heterogeneity and team size. This paper was accepted by Jay Swaminathan, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03227 .

Conversion of metro railway into smart charging zero-emission transportation hub for supporting electric train, shuttle bus, cars and public EV charging station

The unprecedented electrification of the transportation sector and rapid renewable energy penetration create multiple issues on available grid networks. Large-scale integration of various electric vehicle (EV) types into a distributed renewable energy system has not been studied before, and their systematic integration method and quantitative impacts are still unclear in academia. Therefore, this project converted an existing net-zero metro railway network into a smart charging EV hub by transforming the stations to smartly charge all types of EV intersecting at a station, such as public EV charging stations, electric shuttle buses and metro-owned EV cars. Accordingly, the methodology begins with the multiobjective analyses of integrating a public EV charging station into a swappable battery-powered metro station. Secondly, an electric shuttle bus is integrated into the stations without and with bidirectional energy sharing and evaluated for the impact of driving patterns, parking time, and schedule coordination. Thirdly, the metro-owned EV cars are incorporated into the stations and analysed for the number of journeys without and with bidirectional energy sharing. Finally, the importance of control priority while designing the smart charging EV hub is examined. Overall, converting a metro railway into a smart charging EV hub significantly increases the renewable energy consumption from 0.58 to 0.76, improves the net present value from 71.35 × 106 HKD to –35.02 × 106 HKD, and reduces the annual maximum grid import from 1099 kW to 777 kW. The success of vehicle-to-building energy sharing mainly depends on the driving demand ratio to the battery size.

Enhancing Diagnostic Follow-up and Care Coordination for Children with Autism in a Busy Resident Continuity Clinic: Leveraging the Electronic Health Record.

A high-quality primary care clinic should provide clear action points and important care coordination for a child receiving a new diagnosis of autism spectrum disorder (ASD). Unfortunately, a substantial proportion of caregivers report little-to-no post-diagnosis support from their home clinics and primary care providers often report lack of training and resources in providing these supports. We implemented an intervention package to investigate the impact on the frequency and quality of follow-up care for children with ASD in a busy, high-volume resident continuity clinic. The package consisted of a care coordination scheduling pathway and a standardized clinical template-embedded in the electronic health record (EHR)-that guided providers through best-practice recommendations and patient resources. As a result of these interventions, 74% of patients had ASD-specific follow-up, a more than threefold increase from baseline with a majority of providers using the EHR-embedded template to guide their visit. Providers also indicated a high degree of usability for the system and that it aided them in following best-practice guidelines for ASD care. Through explicit scheduling pathways and a novel EHR template, we saw a significant increase in ASD-specific follow-up visits and implementation of best practices for ASD care, demonstrating a new process for training and engaging primary care providers in clear action steps for post-diagnostic care without having to rely on tertiary referrals.

Robust optimization modelling of passenger evacuation control in urban rail transit for uncertain and sudden passenger surge

ABSTRACT Effectively addressing the surge in passenger flow caused by emergencies represents a critical challenge in the emergency management of urban rail transit operations. This article introduces a comprehensive control strategy for passenger evacuation, employing robust optimization methods to tackle the uncertainties associated with sudden increases in passenger numbers. Initially, a comprehensive mathematical optimization model for train scheduling and passenger flow coordination is established to ensure efficient passenger transport while maximizing the reduction of operational costs for the operating company’s emergency response. Subsequently, the impact of uncertainty factors on the evacuation model is considered. The fluctuation in passenger flow is represented using a range of intervals, and a robust corresponding model is formulated by introducing an uncertainty budget coefficient. Furthermore, small-scale numerical examples are utilized to discuss passenger evacuation plans, conduct robustness analysis, and assess demand sensitivity. The practical case of the Beijing subway demonstrates that, in contrast to the most optimistic scenario, the robust passenger plan, accounting for a 1% fluctuation in passenger flow, exhibits a reduced cost increase from 10.96% to 9.13%, as compared to the most conservative situation.These findings contribute to enhancing the emergency response capabilities of operational management departments.

Optimization Techniques in Electric Vehicle Charging Scheduling, Routing and Spatio-Temporal Demand Coordination: A Systematic Review

Optimization Techniques in Electric Vehicle Charging Scheduling, Routing and Spatio-Temporal Demand Coordination: A Systematic Review

Path coordination scheduling method of handling robot considering three-dimensional cargo space of intelligent warehouse

Path coordination scheduling method of handling robot considering three-dimensional cargo space of intelligent warehouse

Path coordination scheduling method of handling robot considering three-dimensional cargo space of intelligent warehouse

Path coordination scheduling method of handling robot considering three-dimensional cargo space of intelligent warehouse

Optimizing Cloud-Fog-Edge Job Scheduling Using Catastrophic Genetic Algorithm and Block Chain-Based Trust: A Collaborative Approach

Collaborative edge-cloud features improve job scheduling. Cloud job scheduling is crucial. Pending delay completion. A cloud-edge mixed system replaced centralized cloud computing. Combining resource levels reduces terminal user service call latency. Decentralization, regionalization, and node dispersal autonomy increase ambiguity, unreliability, and instability. This paper will plan cloud-migrating tasks on edge devices or the cloud to achieve a global optimum. The objective of this research is to enhance the efficiency of job scheduling in cloud-fog edge environments through the integration of the Catastrophic Genetic Algorithm (CGA), a genetic algorithm inspired by natural evolution. Additionally, Berger's theory will be employed to develop a trust-enabled interaction framework based on blockchain technology. The CGA fitness function incorporates load balancing and reasonability in the coordination of services and scheduling of tasks, with the goal of maximizing performance. This article presents proposed improvements to the CGA, which involve the incorporation of mutation and crossover operators, roulette selection, and cataclysm. These changes aim to expand the search area and potentially discover schedules that are more optimal. The approach also effectively deals with the problem of premature convergence, guaranteeing ample time for the algorithm to comprehensively explore the solution space prior to reaching a final solution. The experimental findings indicate that the strategy put forward in this study yields a substantial reduction in task completion time, surpassing 97%. Furthermore, it effectively addresses the best local problem, hence showcasing competing options.

Features of legal regulation of international mixed transportation in modern conditions

Introduction. The paper is devoted to the study of the peculiarities of the legal regulation of international mixed (multimodal) transportation of goods, passengers and luggage in a special period. In the conditions of war, the country's transport and logistics system plays the role of ensuring Ukraine's humanitarian, food, and energy security. The purpose of the paper is to analyze the problematic aspects of the legal regulation of international mixed (multimodal) transportation of goods, passengers and luggage, and to determine directions for improving the legislation in this area in the modern conditions of the development of transport corridors. Results. The author pays attention to the expansion of international multimodal transport corridors TEN-T on the territory of Ukraine and their influence on the intensification of international mixed transportation. It has been established that a successfully integrated transport system involves not only the combination and interaction of all participants in mixed transportation, but also the creation of a single legal and informational field, coordination of technical and operational characteristics of vehicles, infrastructure facilities, coordination and optimization of work schedules of various types of transport and all it is within different legal systems. Conclusions. The author concludes that due to the fact that international mixed transportation has become the only possible option for the delivery of cargo, passengers and luggage both from Ukraine to the countries of other continents and to Ukraine, the issue of unification of international legal regulation of multimodal transportation and the introduction of digital transport corridors is becoming urgent more and more acute.

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Abstract As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.