Search Results (23)

The increasing electrification of Ground Support Equipment (GSE) vehicles promotes sustainable airport operations but introduces new challenges in task scheduling, energy management, and hybrid fleet coordination. To address these issues, we develop an end-to-end Deep Reinforcement Learning (DRL) framework and evaluate it under three representative deployment scenarios with 30%, 50%, and 80% electric fleet proportions through case studies at Singapore’s Changi Airport. Experimental results show that the proposed approach outperforms baseline models, achieves more balanced state-of-charge (SoC) distributions, reduces overall carbon emissions, and improves real-time responsiveness under operational constraints. Beyond these results, this work contributes a unified DRL-based scheduling paradigm that integrates electric and fuel-powered vehicles, adapts Proximal Policy Optimization (PPO) to heterogeneous fleet compositions, and provides interpretable insights through Gantt chart visualizations. These findings demonstrate the potential of DRL as a scalable and robust solution for smart airport logistics. Full article

Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. Full article

Production scheduling and machine tool intermittent state control separately influence a workshop’s machining and intermittent energy consumption. Effective scheduling decisions and intermittent state control are crucial for optimizing the overall energy consumption in the workshop. However, the scheduling scheme determines the machine tool intermittent durations, which imposes strong constraints on the decision-making process for intermittent state control. This makes it difficult for intermittent state control to be used in providing feedback and optimizing scheduling decisions, significantly limiting the overall energy-saving potential of the workshop. To this end, a workshop energy-saving scheduling method is proposed integrating machine tool intermittent state control. Firstly, the variation characteristics of workshop machining energy consumption, machine tool intermittent durations, and intermittent energy consumption are analyzed, and an energy-saving optimization strategy is designed. Secondly, by incorporating variables such as intermittent durations, intermittent energy consumption, and variable operation start time, a multi-objective integrated optimization model is established. Thirdly, the energy-saving optimization strategy is integrated into chromosome encoding, and multiple crossover and mutation genetic operator strategies, along with a low-level selection strategy, are introduced to improve the NSGA-II algorithm. Finally, the effectiveness of the proposed method is verified through a machining case. Results show that the generated Gantt chart reflects both production scheduling and intermittent state control decision outcomes, resulting in a 1.51% reduction in makespan, and 3.90% reduction in total energy consumption. Full article

Scheduling is essential in managing projects. ‘Schedula Anima’ is a new software designed to provide a comprehensive view of schedules between early and late dates for construction project managers. Capturing the dynamic nature of projects, it offers improved visualization through an animation process that creates incremental frames of bar charts and the corresponding resource graphs. As activity delays are simulated, it is observed that delays earlier in the schedule have more significant effects on project completion. A new prioritization method is introduced to evaluate the ease of rescheduling activities. A metric for monitoring resource usage float is presented, and the search space for resource utilization is delineated. As resource smoothing is studied in the resource usage graph and the time domain, a correlation is discovered between resource smoothness and the float consumption rate. It is shown that the schedule and resource usage graph comprises five sub-areas representing different risk exposures. Animation also improves communication in project teams and is beneficial in education. Finally, it is discovered that the permutations of activities in the simulation form a group. Enhancing our perception of resource utilization and the management of delays, ‘Schedula Anima’ brings a renewed perspective to project scheduling. Full article

This research develops and applies a tool that allows the breakdown of time objectives to the same level of detail traditionally applied to cost, while also providing a favorable production scheme to ensure the project quality. This tool introduces an innovative approach to planning and execution monitoring through cascading dashboards, representing production packages and activities across designated project zones. This approach reinterprets the Last Planner System for jobs on-site in conjunction with the Location-Based Management System. The primary dashboard facilitates the management of complex work structures—typically involving hundreds of rows in Gantt chart representations or numerous lines in Line of Balance diagrams—while enabling the easy identification of activity cycles and gaps between activities in each zone. The tool offers a four-dimensional planning visualization—what, where, when, and who—enhancing the understanding of activity sequences and workflows across project zones, while also contributing to product quality improvement. Furthermore, it has been demonstrated through its application that the tool provides reliable, real-time information that supports decision-making, optimizes resource allocation, and improves overall project competitiveness. Full article

Effective scheduling in construction is crucial for ensuring timely project completion and maintaining budget control. Scheduling programs play an important role in this process by providing digital tools to develop, monitor, and adjust project timelines effectively. In the industry’s current method of practice, tabular scheduling programs are utilized that require users to enter task information and their relationships in a tabular format. Recently, a new scheduling program approach called visual scheduling has emerged that requires users to draw the network diagram to create the schedule. This paper presents an experimental study that evaluated the efficiency of schedule creation using a visual scheduling program compared to two tabular scheduling programs. The results show that the time spent creating a schedule using the visual scheduling program was significantly shorter than using tabular scheduling programs. Participants found visual scheduling easier to define tasks, define correlations, spot mistakes in the schedule, make changes to the schedule, and understand the overall schedule. The majority of the participants reported visual scheduling as a tool that allowed them to create schedules faster. They also found it to be a more intuitive scheduling tool and a method that can reduce the possibility of making mistakes during scheduling. Full article

Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many of the toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes. Full article

Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, which increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges have been acknowledged; however, a comprehensive understanding of the status and the remedies has remained unexplored. This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history taking, and attitudes toward adopting digital solutions for managing patient histories; 92.67% of the physicians face high workloads, 88.46% struggle in medical history taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. A novel visualization system, the Smart Health Gantt Chart (SHGC), has been introduced for their instant feedback. A total of 93.27% of physicians expressed their willingness to use such a system. The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care. Full article

This paper presents the development and implementation of Integrated Multilevel Planning Solution (IMPS) a solution adhering to Industry 4.0 and 5.0 standards. Today, companies face challenges in understanding how new orders would impact existing production plans when there is limited traceability and information flow in their manufacturing process. The digital transformation of the production planning system enables a company to overcome the current challenges; however, to overcome the usual barriers of digital transformation a specialized solution for each company should be developed. IMPS was developed by first understanding the problems in the existing production planning process through a gemba (jap. for “actual place”) walk and interviews with stakeholders. The solution was designed with a human-centric approach and consists of seven components (Design System App (DSA), SAP (Systems Applications and Products in Data Processing), Microsoft Project, Microsoft Project Server, The Project Group (TPG) PSLink software, TPG ProjectLink, Tableau, and Smart Digital Assistance), which are well connected and integrated into the existing design. The system is accessible to the end user to find information, as the principles of Industry 5.0 require. A multivariant and multiuser planning capability was achieved with an interconnected Gantt chart of the master project with the ability to drill down into individual projects and custom views for various types of internal users. Most of the production planning solutions found in the literature were optimization-oriented, related to the improvements of the calculation methods within the planning activities in order to achieve a better efficiency of the planning system. Here, the goal was to achieve a system architecture that enabled a unique solution for design-to-order manufacturing without complex interventions into the existing system, which overcomes the most common barriers in Industry 4.0 implementations which are the human resistance to change, high investments, a lack of needed skills and knowledge for its implementation and use, and challenges of the adaptability to the new system. IMPS (ver 1.0) is a hybrid solution for SMEs, which aims to advance their planning system from the most commonly used Excel sheets towards a more advanced system but has financial and knowledge limitations from its implementation of highly complex software (ver. 1.0). Full article

Construction activities in healthcare settings potentially expose building occupants to life-threatening waterborne pathogens, including Legionella. The lack of a building water quality commissioning (BWQC) process has been identified as a substantial construction risk factor associated with disease cases and deaths. A BWQC schedule method was developed as a technical note to address gaps between the construction, commissioning, and operation phases of work to establish water quality and safety for a building water distribution system. The BWQC schedule method enables healthcare organizations to meet commissioning criteria set forth in guidelines and regulatory requirements for implementing a water management program (WMP) prior to initiating patient care operations. The authors used Office Timeline^® Pro+Edition V7.02, Office Timeline LLC, Bellevue WA 98004, USA to depict a Gantt chart as a BWQC schedule listing key project tasks and milestones of construction and water management activities. Design and construction professionals, in conjunction with healthcare organizations, should examine the BWQC construction schedule method and customize it for project-specific implementation. Additionally, building owners should consider incorporating the method into an organization’s construction policies for a standardized approach to BWQC practices. Full article

The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments. Full article

In this research, we present a hybrid algorithmic framework and its integration into the precise production scheduling system of a Greek metal forming factory. The system was created as a decision support tool to assist production planners in arranging weekly production orders to work centers and other manufacturing cells. The functionality offered includes dispatching priority rules, bottleneck identification for capacity planning, production order reallocation to alternate work centers and planning periods, interchangeable scheduling scenarios, and work-in-process availability checks based on bill of materials (BOM) precedence constraints. As a consequence, a solid short-term production plan is created, capable of absorbing shop floor risks such as machine failures and urgent orders. The primary design ideas are simplicity, ease of use, a flexible Gantt-chart-based graphical user interface (GUI), controllable report creation, and a modest development budget. The practical application takes place in a make-to-stock (MTS) environment with a complicated multi-level production process, defined due dates, and parallel machines. A critical component is the integration with legacy applications and the existing enterprise resource planning (ERP) system. The method adopted here avoids both overburdening the existing information system architecture with software pipeline spaghetti, as is common with point-to-point integration, and overshooting implementation costs, as is often the case with service-oriented architectures. Full article

Black soldier flies have been studied as an alternative animal feed. On the other hand, they could be used to yield an abundance of fatty acids. Their omnivorous diet and low space requirements allow for the mass breeding of black soldier flies, using widely available food wastes as feedstock. This study simulates the industrial upscaling of an extraction process for black soldier fly larvae using SuperPro™ 9.5 simulation software. This software contains an extensive material library that regulated physical data for the chemical composition of the larvae and the products. It also bundled several types of bioreactors utilized in bioprocessing. The scheduling of the plant was aided by SchedulePro, which allows for the generation of batch durations and Gantt charts. Four fatty acids were chosen as the main revenue source, with simulated proteins assigned as by-products of the plant. Ash and cellulose were the wastes of the plant, and were separated through multiple filters. The plants were later assessed for their economic feasibility. The kitchen waste plant was the most profitable, and the control variable was the only unprofitable plant. These results may have been impacted by the waste content found in the control variable and the abundance of revenue products in the kitchen-waste-fed larvae. Full article

An unrelated parallel machine scheduling problem motivated by the scheduling of a printed circuit board assembly (PCBA) under surface mount technology (SMT) is discussed in this paper. This problem involved machine eligibility restrictions, sequence-dependent setup times, precedence constraints, unequal job release times, and constraints of shared resources with the objectives of minimizing the makespan and the total job tardiness. Since this scheduling problem is NP-hard, a mathematical model was first built to describe the problem, and a heuristic approach using a non-dominated sorting genetic algorithm (NSGA-II) was then designed to solve this bi-objective problem. Multiple near-optimal solutions were provided using the Pareto front solution and crowding distance concepts. To demonstrate the efficiency and effectiveness of the proposed approach, this study first tested the proposed approach by solving test problems on a smaller scale. It was found that the proposed approach could obtain optimal solutions for small test problems. A real set of work orders and production data was provided by a famous hardware manufacturer in Taiwan. The solutions suggested by the proposed approach were provided using Gantt charts to visually assist production planners to make decisions. It was found that the proposed approach could not only successfully improve the planning time but also provide several feasible schedules with equivalent performance for production planners to choose from. Full article

Targeted at the machining production line balancing problem, based on the precedence constraint relation of the present machining task, this article suggests adding practical constraints such as advanced station preparations, post-auxiliary tasks, and tool changing. The study introduced ‘tight’ and ’or’ constraints to bring the problem definition closer to the actual situation. For this problem, a mixed-integer programming model was constructed in this study. The model redefines the machining and auxiliary processing tasks and adds new time constraints to the station. The model considers two optimisation objectives: the number of stations and the machining line balancing rate. In view of the complexity of the problem, heuristic task set filtering mechanisms were designed and added to the ant colony optimisation, to satisfy the above compound and complex constraints. The processing task chain was constructed using the rules of ant colony pheromone accumulation and a random search mechanism. The study designed a Gantt chart generation module to improve the usability and visibility of the program. Ultimately, through an actual case study of a complex box part including 73 processing elements and realising the design and planning of machining production lines that meet complex constraints by substituting algorithms, the balance rates of several groups of optimisation schemes were higher than 90%, which showed that the algorithm is effective and has a good economy and practicability. Full article