Service Scheduling Research Articles

Artificial Intelligence-Based Chatbot to Support Public Health Services in Indonesia

The aim of this study is to build an artificial intelligence chatbot application to support public health services. The chatbot acts as an information service that can replace the role of humans. The analysis of functional needs was obtained from information submitted by one of the heads of public health centers in Indonesia. This study uses the Scrum method with pregame stages to produce a plan consisting of functional and non-functional requirements analysis and conceptual design of the chatbot, which will be developed using Unified Modeling Language (UML) diagrams. The process of finding answers uses the matching graph master technique, which is a backtrack matching that utilizes a depth-first search strategy. There are 6 topics of chatbot services, including service schedules, health information, registration, diseases, drugs, and early care services for chatbot users. Tests conducted on these 6 topics showed an average correct answer ratio of 93.1% out of a total of 251 questions. The result of the usability measurement on the chatbot application that has been built obtained a system usability scale value of 80.1, indicating that the developed chatbots are acceptable for use.

Matheuristics for scheduling of maintenance service with linear operation cost and step function maintenance cost

We study a problem of scheduling machine maintenance activities in discretized periods, under the constraint that a maximum of one activity can be scheduled for each period. The scheduled maintenance activities will be repeated in a cyclic policy with a given cycle length.The problem is to find a scheduling policy specifying which activity is executed in each period in order to minimize the total cost.Two kinds of costs are assumed: operation and maintenance costs. Both of them depend on the number of periods since the last machine maintenance service. The operation cost occurs during the periods in which a machine is not maintained and increases linearly. The maintenance cost takes place in the periods in which a machine is maintained and is defined as a step function. The steps in the maintenance cost function could be justified by the fact of considering the machine element's substitution when this machine is run for too long without being maintained. This cost structure reduces the gap between the academic problem and the industrial reality.In this paper, the scheduling problem with linear operation cost and step function maintenance cost is defined and formalized. In addition, different matheuristic algorithms are proposed for its resolution. Lower bounds for the objective function are proposed and the computational results show an average deviation from the optimal solution lower than or equal to 3.19%.

"A very different place from when the pandemic started": Lessons learned for improving systems of care for families of children with medical complexity.

Coronavirus disease 2019 (COVID-19) created unprecedented challenges for everyone, but especially families of children with medical complexity (MC) who rely on a comprehensive range of health and social services in their daily lives. Yet despite this, there are limited studies exploring caregiver perspectives regarding access to health and social services during the pandemic. To address this gap, we aimed to explore how health and social services can better meet the needs of children with MC and their families. Sixteen parents residing with their children with MC (from birth to 18years) in British Columbia, Canada participated in semi-structured interviews between July 2021 and April 2022. Findings revealed two different areas to improve services for families of children with MC, those relating to technology and family-centered care. Parents prioritized expanding the use of digital communication tools to support service navigation and scheduling. Virtual platforms were viewed as being valuable for building connections with other families and their community. In terms of family-centered care, parents emphasized the importance of policies recognizing the physical, emotional, and financial needs of the family. Findings have important implications for improving services to enhance the well-being and quality of life of children with MC and their families.

PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging

Electric vehicles (EVs) are becoming increasingly popular in ride-hailing services, but their slow charging speed negatively affects service efficiency. To address this challenge, we propose PROLIFIC, a deep reinforcement learning-based approach for efficient EV scheduling and charging in ride-hailing services. The objective of PROLIFIC is to minimize passenger waiting time and charging time cost. PROLIFIC formulates the EV scheduling problem as a Markov decision process and integrates a distributed charging scheduling management model and a centralized order dispatching model. By using a distributed deep Q-network, the agents can share charging and EV supply information to make efficient interactions between charging and dispatch decisions. This approach reduces the curse of dimensionality problem and improves the training efficiency of the neural network. The proposed approach is validated in three typical scenarios with different spatiotemporal distribution characteristics of passenger order, and the results demonstrate that PROLIFIC significantly reduces the passenger waiting time and charging time cost in all three scenarios compared to baseline algorithms.

TATA KELOLA REKAM MEDIS BERBASIS ELEKTRONIK DALAM MONITORING PASIEN HEMODIALISA DI RSUD AL-IHSAN

This study aims to analyze the implementation of electronic-based medical record management using the waterfall method in monitoring hemodialysis patients at AL-IHSAN Hospital. The results of our research show that there are still manual hemodialysis patient services, namely dialysis scheduling services for hemodialysis patients, that have previously been scheduled by specialist doctors. What is currently being done is that management is still manual, so it requires time and accuracy to schedule when patients will be scheduled for dialysis. The research method that we use in our current scientific work is qualitative with a descriptive approach using the waterfall software design process. Then distributing questionnaires and interviews. This approach allows stakeholders to clearly understand system requirements and execute each development phase sequentially. Electronic-based medical record management results in increased efficiency in the process of monitoring hemodialysis patients, especially routine scheduling of patients who are already integrated with the system. Information that is well documented and available centrally reduces the need to search physical records or fill out forms manually. This saves medical staff time and effort, allowing them to focus on patient care.

IAware: Interaction Aware Task Scheduling for Reducing Resource Contention in Mobile Systems

To ensure the user experience of mobile systems, the foreground application can be differentiated to minimize the impact of background applications. However, this article observes that system services in the kernel and framework layer, instead of background applications, are now the major resource competitors. Specifically, these service tasks tend to be quiet when people rarely interact with the foreground application and active when interactions become frequent, and this high overlap of busy times leads to contention for resources. This article proposes iAware, an interaction-aware task scheduling framework in mobile systems. The key insight is to make use of the previously ignored idle period and schedule service tasks to run at that period. iAware quantify the interaction characteristic based on the screen touch event, and successfully stagger the periods of frequent user interactions. With iAware, service tasks tend to run when few interactions occur, for example, when the device’s screen is turned off, instead of when the user is frequently interacting with it. iAware is implemented on real smartphones. Experimental results show that the user experience is significantly improved with iAware. Compared to the state-of-the-art, the application launching speed and frame rate are enhanced by 38.89% and 7.97% separately, with no more than 1% additional battery consumption.

Multi-skilled workforce scheduling with training and welfare considerations

Abstract Flexibility in workforce scheduling in services is necessary to reduce the impact of demand uncertainty, absenteeism, and desertion while maintaining high service levels. This paper studies the workforce scheduling problem, including multiple skill accumulation, training, and welfare, as well as flexibility for employees and the company. All these elements are modelled and included in a mixed-integer linear programming (MILP) model that maximises their accumulated skill level. A real case study based on the scheduling of lab assistants to laboratory practices at a university in Colombia is used to generate numerical experiments. Different experiments were conducted, and the results show that the level of skill achieved is highly sensitive to the number of assistants and the number of allocations. The experiments also showed that, while keeping the same number of lab assistants, it is possible to include flexibility and welfare constraints. Finally, the proposed model can generate schedules that achieve high levels of skills and meet the different constraints of the model, including balance, accumulation, demand and welfare.

Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

A comprehensive analysis of the electronic fare collection systems effectiveness implementation on public transit and prospective directions of its application in Ukraine

The object of research is the effectiveness of the implementation of electronic fare collection systems on public transit. Applying the electronic fare collection systems is a general trend in improving public transport services for users. In the pre-war period, the systems began to be implemented in many cities of Ukraine. At the same time, this activity was not of a systemic nature and at the current stage it is mainly considered as a means of ensuring more convenient conditions for the use of scheduled passenger transport services for passengers. The article focuses on a broader understanding of the effectiveness of the fare collection systems implementation, their role in ensuring the internal integration of the multi-modal public transport system, increasing the operational efficiency, providing the safety of transportation and increasing the attractiveness of public transit services for the population as a real alternative for the private cars to use. The implementation efficiency of the electronic fare collection systems in public transit should be expressed through various aspects. There are 9 aspects to be considered: system integration; comfort ensuring; transportation safety assistance; operational efficiency and passengers' travel time reduction; integration into the management and planning systems; implementation of the flexible fare system; conduction of the flexible fare policy; development of reasonable income distribution system; increase of the scheduled passenger transport services attractiveness. It is suggested to use systemic approach for integrated multimodal public transit system creation. It requires the development of an intelligent transport system that would integrate separate functions of the electronic fare collection system into controlling, managing and planning subsystems. The practical introduction of the solutions proposed regarding the prospects to develop electronic fare collection systems in the cities of Ukraine will make it possible to increase the efficiency of their use and contribute to the improvement of the quality of transport services for passengers.

AI-empowered game architecture and application for resource provision and scheduling in multi-clouds

Current deep learning technologies used a large number of parameters to achieve a high accuracy rate, and the number of parameters is commonly more than a hundred million for image-related tasks. To improve both training speed and accuracy in multi-clouds, distributed deep learning is also widely applied. Therefore, reducing the network scale or improving the training speed has become an urgent problem to be solved in multi-clouds. Concerning this issue, we proposed a game architecture in multi-clouds, which can be supported by resource provision and service schedule. Furthermore, we trained a deep learning network, which can ensure high accuracy while reducing the number of network parameters. An adapted game, called flappy bird, is used as an experimental environment to test our neural network. Experimental results showed that the decision logic of the flappy bird, including flight planning, avoidance, and sacrifice, is accurate. In addition, we published the parameters of the neural network, so other scholars can reuse our neural network parameters for further research.

Migratory Perception in Edge-Assisted Internet of Vehicles

Autonomous driving technology heavily relies on the accurate perception of traffic environments, mainly through roadside cameras and LiDARs. Although several popular and robust 2D and 3D object detection methods exist, including R-CNN, YOLO, SSD, PointPillar, and VoxelNet, the perception range and accuracy of an individual vehicle can be limited by blocking from other vehicles or buildings. A solution is to harness roadside perception infrastructures for vehicle–infrastructure cooperative perception, using edge computing for real-time intermediate features extraction and V2X networks for transmitting these features to vehicles. This emerging migratory perception paradigm requires deploying exclusive cooperative perception services on edge servers and involves the migration of perception services to reduce response time. In such a setup, competition among multiple cooperative perception services exists due to limited edge resources. This study proposes a multi-agent reinforcement learning (MADRL)-based service scheduling method for migratory perception in vehicle–infrastructure cooperative perception, utilizing a discrete time-varying graph to model the relationship between service nodes and edge server nodes. This MADRL-based approach can efficiently address the challenges of service placement and migration in resource-limited environments, minimize latency, and maximize resource utilization for migratory perception services on edge servers.

Integrated optimization of timetable, bus formation, and vehicle scheduling in autonomous modular public transport systems

This paper presents a joint optimization of the timetable, bus formation, and vehicle scheduling in a flexible public transport (PT) system that utilizes autonomous modular vehicles (AMVs). In this system, AMVs have the capability to detach or join with each other at intermediate stops along the route to dynamically adjust the bus formation (capacity). To increase vehicle utilization, a flexible scheduling strategy is proposed that allows AMVs to detach from one modular bus and join another modular bus in either direction of a bidirectional line. In particular, the penalty cost for each detachment or joining operation, as well as the limited number of available AMVs is explicitly considered. We formulate a unified model for the integrated optimization of the modular bus service (timetable and bus formation) and vehicle scheduling by introducing two types of decision variables. The objective is to minimize overall system costs, including passenger waiting time cost, operational costs, and detachment/joining penalty costs. The two types of decision variables are coupled by a vehicle resource consistency constraint, ensuring the conformity of the modular bus service and vehicle scheduling decisions. To tackle the complexity of our model, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose it into two subproblems, which can be efficiently solved using a customized forward dynamic programming algorithm and a commercial solver. The model is validated using illustrative examples and a real-world instance from the Beijing Public Transport system, and it is compared with two benchmark models. Our results demonstrate the efficiency of the ADMM-based solution framework for solving the integrated optimization model. Furthermore, our findings indicate that the use of AMVs in PT systems can lead to reduced overall system costs and increased vehicle utilization.

Dual-service integrated scheduling of manufacturing and logistics for multiple tasks in cloud manufacturing

To meet the frequent transportation requirements between distributed manufacturing services (MSs), logistics services (LSs) have played an essential role in cloud manufacturing. However, previous studies on service scheduling focused on MSs and simply treated the logistics process as a linear relationship with distance. In fact, the resulting scheduling scheme for MSs is prone to deterioration in practical implementation when the dynamic nature of LSs is neglected. Therefore, this paper presents a multi-objective model for dual-service integrated scheduling of manufacturing and logistics (DISML) and proposes an improved non-dominated sorting genetic algorithm-II (INSGA-II) to solve it. The integration of manufacturing and logistics processes introduces complex constraints, making it challenging to properly represent the solution. To address this challenge, a novel three-layer encoding approach is designed and its feasibility is demonstrated through directed graphs. Additionally, several problem-dependent heuristics are developed to enhance solving efficiency. Experimental results show that INSGA-II outperforms other algorithms in terms of IGD and C metric in 96% and 83% of instances, respectively. The results also demonstrate the advantages of the DISML mode over the decentralized scheduling mode in terms of solution quality and efficiency. Our proposed model and the results presented here provide managers with a new tool to help them schedule MSs and LSs more effectively, thereby improving production efficiency, reducing costs and unexpected delays in execution.

A Blockchain-Based Shared Bus Service Scheduling and Management System

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management.

Examining the Future of Automation in Commercialized Flight and its Impact on Airline Pilots

The purpose of this study was to focus on the perceptions of an automated (unmanned) flight deck and its intended implementation in the U.S. airline industry. The objective was to understand fully autonomous aircraft’s influence on commercial pilots, as well as investigate the effects on the U.S. airline industry and human-machine interaction. Commercial airline pilots, including ranks of both Captains and First Officers (FO) in Part 121 scheduled service, were the research population for this study. The sample population number was 15 U.S. airline pilots offering various diversity in age, gender, years of service, airline employer, and military or civilian flight experience. The semi-structured qualitative research consisted of developing a deeper understanding of the pilots’ perceptions of the three-pronged approach: (1) the psychological barriers and motives of the pilot, (2) the differences in aircraft ground-based safety and training, and (3) pilots’ understanding of the timeliness of entry for unmanned flight deck operations to reach FAA standards. The study consisted of participant interviews utilizing a series of ten structured and semi-structured interview questions. The findings concluded that pilots are aware of the workload changes that are present with implementing fully autonomous aircraft in the Part 121 category. Two themes became evident when discussing workload changes. First, the role of the pilot changes from a single flight focus to a multi-flight monitor. Secondly, as technology advances, the pilot’s physical workload of hand flying an aircraft will constantly deteriorate. Furthermore, the advancement of a fully automated flight deck and its effects on the U.S. airline industry can potentially have significant impacts on the psychology of commercial airline pilots. While pilot responses varied based on individual experiences, attitudes, and adaptability, there were several psychological responses identified in this study, including (1) loss of control, (2) uncertainty toward technology, (3) job insecurity, and (4) adaptability. Last, the advancement of a fully autonomous flight deck will obviously create additional concerns for U.S. airline passengers. The most significant concerns for passengers included (1) safety, (2) trust, (3) passenger-pilot relationship, and (4) understanding of technology. Passengers will always question if modern technology has been thoroughly evaluated and if it can react to unexpected situations as safely and effectively as a human.

Joint multi-objective dynamic scheduling of machine tools and vehicles in a workshop based on digital twin

Dynamic job-shop scheduling is vital for improving job-shop efficiency and reducing energy consumption. A workshop is a complex combination of multiple functions. Previous studies regarding workshop scheduling showed several problems, such as delayed rescheduling response, single influencing factors, and the separation of machine tools and vehicles. Therefore, to address these issues, this paper constructs a multi-factor scheduling service system with various factors, such as machine failure, tool wear, and product quality. Combined with the international background of rising energy prices, the scheduling scheme is evaluated while having two goals to achieve: minimizing the completion time and achieving energy consumption flexibly by controlling the different parameters’ values. Introducing digital twin theory, machine fault prediction, tool wear prediction, and product quality monitoring can achieve the timeliness and predictability of the workshop scheduling, the joint scheduling of machine tools, and the vehicle transport tasks. Finally, the superiority of this method is verified by processing some critical parts in a marine diesel engine workshop.

Design and Development of Laundry Booking System with SMS notification

This study presents a novel Laundry Booking System integrated with SMS notification, aiming to enhance laundry service efficiency and customer satisfaction. The research focuses on developing a user-friendly online platform for easy scheduling of laundry services and implementing SMS notifications to provide real-time updates on the laundry status. The system aims to streamline operations for laundry service providers while offering convenience and transparency to customers. Although the study is limited to one laundry service provider catering to residential customers, the findings have broader implications for the industry. This technology-driven solution has the potential to transform the laundry service landscape, offering a seamless and efficient experience for both customers and service providers

The Incomplete Basic Immunization for Infants During the Covid-19 Pandemic in the Work Area of the Bandar Petalangan Health Center, Pelalawan Regency

Achievement of complete basic immunization at Puskesmas bandar Petalangan was only 77.7%. There are various causes for the low achievement of Complete Basic Immunization such as remote and difficult to reach immunization service locations, irregular service schedules and incompatible with community activities, unavailability of immunization cards (MCH handbook), low public awareness and knowledge of benefits. The research objective was to know the timeliness of the implementation of basic immunization in infants during the Covid-19 pandemic in the work area of the Bandar Petalangan Community Health Center, Pelalawan Regency in 2020. The research method used a cross sectional design with a population of mothers who had babies aged 3-12 months, totaling 257 people with a sample. totaled 161 people. The sampling technique used accidental sampling. Data analysis was performed using univariate, bivariate and multivariate multiple logistic regression. The results showed that there were 54 mothers (33.5%) who were not on time in basic immunization, variables related to the timeliness of basic immunizations were maternal education (POR = 2.9), affordability of immunization sites (POR = 3.6) and family support (POR = 3.3). It is recommended that BLUD Puskesmas bandar Petalangan be able to increase and equalize posyandu service facilities, increase information and education communication (KIE) efforts to mothers and families about the importance of basic immunization in infants and increase the role of volunteer health.

Optimizing dag scheduling and deployment for Iot data analysis services in the multi-UAV mobile edge computing system

Optimizing dag scheduling and deployment for Iot data analysis services in the multi-UAV mobile edge computing system

Determining efficacy of dynamic multimedia bowel preparation instructions versus standard instructions on adenoma detection and patient reported measures (DIGICLEAN trial): a study protocol for a multicentre, colonoscopist-blinded, randomised controlled trial

IntroductionColonoscopy plays important roles in bowel cancer screening and treatment. Poor bowel preparation occurs in 20–25% of colonoscopies. This negatively impacts adenoma and sessile serrated lesion detection rates, procedural time,...