Search

Earth observation satellite (EOS) scheduling is critical for improving the performance of Earth observation system. Most of the existing studies on satellite observation scheduling problems focus on static tasks and real-time dynamic tasks also known as emergency tasks while few attempts have been made for common dynamic tasks yet. The common dynamic tasks are characterized by uncertain arrival times and delayable observations while the emergency tasks require immediate observations. The number of common dynamic tasks is rapidly increasing with the expansion of satellite observation applications, which poses great challenges to EOS observation scheduling. To address this issue, this paper investigates the satellite observation scheduling problem with common dynamic tasks. Firstly, a centralized onboard dynamic scheduling framework based on a periodic-triggered rolling horizontal optimization (RHO) strategy is proposed and a novel two-stage mathematical model is established for the periodic rescheduling problem. Secondly, we propose a low-complexity onboard periodic rescheduling algorithm (OPR), which consists of a greedy-based task allocation algorithm, a pointer network (Ptr-network) based task scheduling algorithm and an iterative local search algorithm. In the greedy-based task allocation algorithm, we define a Task-EOS Fitness Indicator (TEFI) and each task is greedily allocated to the EOS with maximum TEFI. In the Ptr-network based task scheduling algorithm, the allocated task sets of all EOSs are fed into the Ptr-network in parallel to generate the observation scheduling result. Afterward, an iterative local search algorithm is proposed to further improve the quality of the observation scheduling result. Finally, extensive experiments are conducted to demonstrate the superiority of the OPR algorithm for the satellite observation scheduling problem with common dynamic tasks. [ABSTRACT FROM AUTHOR]

The space time-sensitive multi-target collaborative observation scheduling problem extends the traditional Earth Observation Satellite (EOS) scheduling by incorporating the time dimension. Multiple effective and collaborative observations over time are required to collect relevant information on time-sensitive space targets in high-speed motion. Furthermore, changes in target spatial relationships cause conflicts in resource allocation. Due to the urgency of the time-sensitive targets, the observation system must respond promptly and efficiently upon receiving a task. This study proposes a mathematical model and a Time-Sensitive Observation Scheduling (TSOS) algorithm to address these problem characteristics. Firstly, a mathematical model for scheduling is established by introducing the completion quality index and resource switching frequency index, given the requirement for collaborative multiple satellites in this problem. The TSOS algorithm consists of two parts: task pre-processing and a circular scheduling strategy. In the task pre-processing module, the entire scheduling is divided into multiple observation decision intervals. This enables the candidate satellite set corresponding to each target to remain stable, which reduces the complexity of the problem. An action mapping method is used in the circular scheduling module, and a decision network based on Deep Reinforcement Learning (DRL) called the Observation Pair Decision Network is proposed. Experimental results demonstrate that the trained TSOS algorithm can effectively schedule resources in scenarios with different conflict degrees and exhibits strong robustness. [ABSTRACT FROM AUTHOR]

This paper addresses to the problem of flowshop scheduling under no-idle constraints with respect to the total completion time criterion. A branch and bound algorithm is proposed and a machine based lower bound is developed for the first time in the literature of this problem. Moreover, an Iterated Local Search (ILS) algorithm is used to provide the upper bounds of the problem. The computational results are promising, and the proposed algorithm appears able to find optimal solutions to problems with up to 20 jobs and 4 machines. [ABSTRACT FROM AUTHOR]

Today's agile production systems face an ever-increasing complexity due to individualized mass production, a volatile customer demand and dynamic events which disrupt the baseline schedule. Robustness and optimality of the schedule affect the efficiency of the production systems, which in turn negatively affects resource consumption and waste generation. To achieve near-optimal performance, while adhering to due dates and robustly handling disturbances, predictive-reactive scheduling is often proposed. In the current state-of-the-art research on predictive-reactive scheduling there still exist some open issues. One issue to address is a current deficit in generalization, that is, when handling new, previously unseen data. Arguably the main culprit is the reactive (re)scheduling component. Designing such highly complex systems imposes many difficulties. We identify existing problems which ultimately limit the applicability of predictive-reactive scheduling and investigate and evaluate a design approach for predictive-reactive scheduling. We propose predictive-reactive scheduling, which uses match-up scheduling and promising decentralized multi-agent reinforcement learning, which allows for fast and flexible real-time decision-making. The aim of this study is to provide insights and improve the applicability of research on predictive-reactive scheduling. [ABSTRACT FROM AUTHOR]

• A graph neural network is proposed for learning multi-satellite scheduling policy. • It analyzes the differential impact of various constraints in scheduling. • It models the multi-satellite scheduling problem as a heterogeneous graph. • It designs a variant transformer to learn various relations within graph. • It designs a two-stage model to learn a multi-satellite scheduling policy. In this paper, we investigate the multi-satellite scheduling (MSS) problem, a combinatorial optimization problem (COP) that aims to enhance satellite utilization. Numerous scholars have conducted in-depth studies on this matter. Handcrafted policies are commonly employed in various algorithms. Despite the use of graphical representations, designing policies manually becomes increasingly difficult, particularly as the number of satellites and tasks grows. Consequently, we propose a novel approach for automatically summarizing graph structures and learning policies for MSS problem. The proposed approach is developed based on graph neural network, Transformer, and sequence to sequence framework. First, MSS problem is modeled as a heterogeneous graph following various constraints. Next, the encoder, which is based on GNN and Transformer, is designed to summarize crucial information from the aforementioned graph. Finally, a two-stage decoder is proposed taking into account dynamic information in stepwise solution generation. Since obtaining optimal solutions in practical applications is challenging, we train the model using reinforcement learning. The results of the experiment demonstrate that our proposed method performs effectively in various scenarios. The quality of the solutions generated through this method transcends that of conventional construction methods. Additionally, the method generates solutions quickly, which are comparable in quality to those produced by meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Demand for imaging of large area targets by space-based assets such as constellations of Earth Observation Satellites (EOS) is on the rise for different applications. The area targets coverage cannot be achieved by a single satellite in a single observation opportunity, as the region for imaging spreads wider than the coverage swaths of imaging sensors. Hence, the imaging satellites are scheduled in a cooperative way to achieve maximum coverage of the area in a given planning time horizon. The scheduling algorithms also aim to maximize or minimize some specific objectives of interest while simultaneously complying with all resource constraints for an efficient use of precious space resources. As the current generation satellites are highly agile, there are many ways of choosing an imaging location in the target area for each single observation opportunity of a satellite. Even though the possible imaging region in continuous space is discretized into a finite number of strips for each observation, the huge combinatorial search space formed with all satellites' imaging opportunities makes the problem intractable. Thus, the area target imaging scheduling is a complex combinatorial NP-hard optimization problem. Many exact and heuristic methods were evolved to provide a solution to this scheduling problem. Even though the exact methods solve the problem to optimality, they are limited to smaller problem instances because of the high computational complexities involved in those methods. More focus is seen in the literature on heuristic approaches as they guarantee scheduling performance with feasible solutions within an acceptable computational complexity. This paper presents our efficient heuristic approach proposed for the scheduling problem for imaging a large area target using a constellation of multiple satellites. Our approach is an extension of a widely used greedy approach in combination with insights from dynamic programming. In this paper, first we addressed the scheduling problem by describing it in detail and formulating it into a non-linear integer programming problem, considering the multi-objectives of achieving maximum coverage and minimum imaging resolution. The solution to the problem comprises two phases, namely the area decomposition phase and the scheduling phase. In the area decomposition phase, the area target is divided into strips dynamically for each observation opportunity of a satellite. Then exact observation period of each strip is computed using a simplified semi-analytical computational method. In the scheduling phase, we applied our heuristic search strategy, namely Forward and Backward Heuristic Search (FBHS) to obtain a near optimal solution within an acceptable computational time. Through the extensive simulations conducted under various scenarios, the effectiveness of the proposed method is verified in comparison with the baseline greedy approach. • The large area target imaging scheduling for multiple satellites is a complex combinatorial NP hard optimization problem. • Greedy algorithm, which guarantee the scheduling performance, is a widely used method. • Forward and Backward Heuristic Search method is proposed for area target image scheduling. [ABSTRACT FROM AUTHOR]

The amount of space debris is rising rapidly, and the number of satellites launched by humans is also growing. This poses a serious threat to the security of many important space assets including the International Space Station and the China Space Station. Due to the frequent occurrence of hazardous space conjunction (potential collisions) events, we aim to use a constellation composed of a few optical satellites to observe these space dangerous conjunction events, thereby improving the observation accuracy and timeliness of key conjunction events. We consider a variety of factors to establish an optical nanosatellite constellation design model, develop constellation observation efficiency evaluation criteria, construct a constellation configuration evaluation function, and design the performance parameters of space-based optical telescopes. The optimal orbit parameters and constellation configuration are obtained by using the genetic algorithm for optimization calculation. It is determined that a constellation of 6 satellites can track and observe more than 96% of potentially dangerous conjunction objects 24–48 h before a dangerous conjunction occurs. According to the observation data, high-precision orbit information of space objects can be obtained, which can reduce the collision false alarm rate by 99%. As a result, operators are notified to take collision avoidance measures only when the space object is truly in danger. This approach greatly reduces the number of avoidance operations caused by false early warnings, lowers the operating cost of space assets, and ensures the safety of space assets. [ABSTRACT FROM AUTHOR]

The five daily prayers are an obligation of worship for Muslims in the world. Prayers have a predetermined time schedule according to the teachings of the Prophet Muhammad SAW and have different schedules according to each region. As Muslims, it is obligatory to pray five times a day. However, there are a lot of start-up company which forgot about the employee need such as praying. One of them is called byPulsa Start-up company which wants anticipates delays in praying. Because of this reason, the Ceo at byPulsa need an application to remind the employee about praying. So, this company wants to improve the additional feature into its application and develop its application more less interaction and efficient. So, this research aims to develop the previous application into an application that can remind employees to pray. In this research, Kiss Principle Method and User Centered Design were used to this application to make it more efficient and usable for user. The results show that the user feels more satisfied with the application because it has prayer schedule feature based on user's position. The CSAT score from evaluation has score 80% which higher than previous application. This application can help and guide users in exploring their religion by following online studies and using features such as prayer beads, Islamic book recommendations, even Islamic podcasts. As well as the Kiss principle method used here, it functions to make it easier for users to interact with the application. [ABSTRACT FROM AUTHOR]

In a construction environment, project scheduling is an essential tool to measure the success of all projects. This research paper will use project crashing of Activity to decrease the project completion schedule through the CPM method of time-cost trade-off and to minimize project cost. It includes linear programming with the aid of Microsoft Excel Solver to determine the result. And at the end of the research paper, it will show the impact of crashing the activities for the HVAC mechanical installation in a project for both normal and crash time, which displays that the selected Activity that had been crashed generated an increase of 9.8 % total cost from the total project cost. [ABSTRACT FROM AUTHOR]

Scheduling is essential in manufacturing to maintain machinery and increase revenue. Machines that are routinely maintained will boost production, whereas scheduling based on total weighted completion time (TWCT) can improve the performance of production equipment. The Lovebird Algorithm (LBA), Constructive Heuristics (CH), and combination of LBA-CH are used in this study to schedule a single machine with equal processing times. LBA, a population-based algorithm, represents job parts as birds for jobs that can be preempted. The experiments, which used ten jobs divided into dozens of job parts, show that LBA-CH can effectively reduce TWCT compared to CH by 18.16%. [ABSTRACT FROM AUTHOR]

Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient Kmeans (Ekmeans) and the second technique is called Kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks. We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000). [ABSTRACT FROM AUTHOR]

The satellites are assigned to perform scanning tasks by maneuvering sensors in roll and pitch directions within resource constraints. However, these task regions may be larger than the scanned region by the satellites in a single attempt, so multiple attempts are needed to complete the task. Previous literature has proposed strip-based segregation for larger task regions with parallel strips. However, satellite trajectories may not be parallel to each other, and multiple visits from multiple satellites may be required to complete a scanning task. This results in higher usage of resources due to overlapping strips in strip-based segregation. Therefore, a novel three-stage scheduling methodology is proposed for better performance in terms of the size of the scanned region and resource utilization. The initial stage assigns satellites to the tasks irrespective of task segmentation for each scan. The second stage optimizes the roll and pitch angles of assigned satellites to maximize the scanned region with parallel computation since the tasks are independent. The third stage handles uncertainty associated with task failures. An elitist mixed-coded evolutionary solution technique with a constrained non-linear optimization and Markov decision process is proposed. The efficiency of the proposed methodology in small and large-scale scenarios is illustrated with simulations that show up to 20% improvement in the reward collection and 16% improvement in resource utilization compared to the traditional strip-based method. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing overall system performance. This research introduces a groundbreaking approach to task scheduling in cloud environments through the implementation of a novel Modified Shortest Job First (SJF) algorithm within the CloudSim simulation framework. The primary objectives of this study are to address existing challenges in traditional scheduling algorithms, mitigate resource bottlenecks, reduce task completion times, and improve overall system efficiency. [ABSTRACT FROM AUTHOR]

Large-scale hydrogen production through water electrolysis is recognized as a promising way to promote the utilization of renewable energy. However, the integration of massive electrolyzers will lead to low computational efficiency in their scheduling problem, thus an aggregation modeling method for electrolyzer clusters is urgently needed. In this paper, a refined operation model of alkaline water electrolyzers is established to accurately describe their operation characteristics. Moreover, to solve 'the curse of dimensionality' in their large-scale applications, an approximated aggregated model for electrolyzer clusters which can improve the computational efficiency is proposed. The proposed model is incorporated into the optimal scheduling of power systems, and its effectiveness is validated via numerical experiments on the modified IEEE 118-bus test system. The results show that the proposed method can improve the computational efficiency by at least 1–2 orders of magnitude while ensuring high modeling accuracy. • A refined operation model for alkaline water electrolyzers is established. • An approximated aggregated model for electrolyzer clusters is proposed. • The proposed model is incorporated into the optimal scheduling of power systems. • The computational efficiency can be improved by at least 1–2 orders of magnitude. [ABSTRACT FROM AUTHOR]

Sheltered workshops face the challenge of meeting their manufacturing objectives while considering the individual competencies and assistance needs of persons with disabilities. Moreover, work processes in sheltered workshops are weakly structured, allowing for frequent task interruptions and changes based on the preferences of impaired work persons. While the Industry 4.0 literature provides many real-time scheduling algorithms for incorporating multiple objectives and constraints, these algorithms fall short of the characteristics of sheltered workshops. The resource-constrained project scheduling problem (RCPSP) is an optimization problem for computing manufacturing plans considering multiple objectives and resource constraints. Among many different RCPSP variants proposed in the literature, the multi-skill RCPSP (MSRCPSP) variant considers the individual skills of work persons when generating manufacturing plans. With the ongoing digital transformation of enterprises, new assistance systems enter the market, providing individual support to impaired work persons and compensating for lacking skills and abilities. This paper proposes an MSRCPSP variant that incorporates assistance systems and learning tasks compensating for competence gaps in the skill matrix of impaired work persons. Furthermore, it decomposes tasks into individual work objects, which accounts for frequent task interruptions and task preferences of work persons. The algorithm is described and demonstrated using a manufacturing data set from an actual sheltered workshop. In a small evaluation study, the algorithm is tested by scheduling two impaired work persons in the assembly department of a medium-sized manufacturing company in the primary labor market. The evaluation study proves its real-world applicability and the suitability of scheduling algorithms for participation and inclusive work. [ABSTRACT FROM AUTHOR]

In the large-scale production of Diethyl Oxalate (DEO), numerous control and optimization challenges are faced. These issues are targeted in this study by implementing a gain scheduling approach to control a catalytic fixed bed multi-tubular reactor designed for DEO production. A novel inferential modeling method, developed using Python and response surface methodology, was applied to simulate the dynamics of the reactor, and the inlet coolant temperature was identified as the most influential parameter. An inferential control system was implemented that utilized gain scheduling for Proportional Integral Derivative (PID) controllers. The results showed that the proposed PID controller gains offered fast and stable responses, even at high conversion setpoints. However, minimization of response overshoot at high amplitudes of step changes proved challenging, leading to a decrease in conversion estimator accuracy. The use of a quadratic model from response surface methodology for the regression of the inferential estimator was proposed, showing improved prediction accuracy over the linear model. These findings provide valuable insights and guide future optimization in the design and operation of reactors for large-scale DEO production. [Display omitted] • Reactor Modeling : Deep insights into the coupling reactor dynamics via Simulink. • Most Effective Parameter : Coolant temperature identified through statistical analysis. • Novel Estimation Method : Python-based approach for inferential control's estimator. • Enhanced Prediction Accuracy : Quadratic model improves estimator precision. • Gain Scheduling Control: Implemented gains ensured stability and speed of responses. [ABSTRACT FROM AUTHOR]

This work addresses the quasi-synchronization of delay master–slave BAM neural networks. To improve the utilization of channel bandwidth, a dynamic event-triggered impulsive mechanism is employed, in which data is transmitted only when a preset event-triggered mechanism or a forced impulse interval is satisfied. In addition, to guarantee the reliability of information transmission, a reliable redundant channel for BAM neural networks is adopted, whose transmission scheduling strategy is designed on the basis of the packet dropouts rate of the main communication channels. Further, an algorithm is employed to reduce the quasi-synchronization range of the error systems and the controllers are obtained. At last, a simulation result is shown to illustrate the effectiveness of the presented strategy. [ABSTRACT FROM AUTHOR]

A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While cloud computing offers advantages, it introduces scheduling delays caused by data transmission. To address this issue, edge computing has emerged as an alternative to traditional cloud systems, aiming to minimize latency. While various methods have been proposed to address this challenge, they often prioritize one aspect at the expense of overall system performance. In this paper, we present a novel algorithm utilizing ant colony optimization to compute a fitness function and prioritize multiple objectives in scheduling. The algorithm effectively determines how to distribute applications between edge and cloud servers to enhance computational efficiency. This entails a delicate balance between scheduling delays and energy consumption in two distinct phases. Initially, the algorithm identifies applications sensitive to delays and ensures their execution on local edge servers. Subsequently, it identifies applications that require intensive computation and migrates them to the cloud layer, where cloud servers can process them. The results demonstrate that this approach reduces delay costs by 21.19% and decreases energy consumption by 13.76%. [ABSTRACT FROM AUTHOR]

Though dosimetry has a multitude of treatment modalities, software, and workflows to aid in the treatment planning process, treatment planners are still responsible for several tedious and monotonous tasks that could decrease their planning efficiency. The purpose of this study was to determine if scripting could improve treatment planning efficiency for multiple brain lesion stereotactic radiosurgery (SRS) volumetric arc therapy cases by reducing planning time commitment. A script was developed for multiple brain lesion SRS cases using Eclipse scripting application programming interface with the intention of improving treatment planning efficiency by creating optimization structures and importing prescription and suggested OS dose metrics to the optimizer. Nine treatment planners were each provided with 3 different multiple brain lesion, single-isocenter SRS cases. Each planner created 2 plans for each case. One of these 2 plans used the SRS script, and the other did not. There were 54 treatment plans developed, totaling 27 plan comparisons. Each of the 54 treatment plans were considered clinically acceptable based on the participating institution's plan quality guidelines. Statistical analyses of planning time commitment with and without the SRS script were performed using RStudio. The mean and median planning times with and without the SRS script were compared using a paired T-test and Wilcoxon Signed Rank test, respectively, and effect size was evaluated using Cohen's classification. Using the SRS script resulted in statistically significant reduction in total contouring time (11.3 vs 2.8 minutes, p < 0.001), optimizer preparation time (7.7 vs 2.1 minutes, p < 0.001), and overall planning time (105.1 vs 77.9 minutes, p < 0.001). This study concluded that scripts developed using Eclipse scripting application programming interface offer an opportunity to improve treatment planning efficiency by reducing the planning time commitment for treatment planners. [ABSTRACT FROM AUTHOR]

The following geometric vehicle scheduling problem has been considered: given continuous curves f 1 , ... , f n : R → R 2 , find non-negative delays t 1 , ... , t n minimizing max { t 1 , ... , t n } such that, for every distinct i and j and every time t , | f j (t − t j) − f i (t − t i) | > ℓ , where ℓ is a given safety distance. We study a variant of this problem where we consider trains (rods) of fixed length ℓ that move at constant speed and sets of train lines (tracks), each of which consisting of an axis-parallel line-segment with endpoints in the integer lattice Z d and of a direction of movement (towards ∞ or − ∞). We are interested in upper bounds on the maximum delay we need to introduce on any line to avoid collisions, but more specifically on universal upper bounds that apply no matter the set of train lines. We show small universal constant upper bounds for d = 2 and any given ℓ and also for d = 3 and ℓ = 1. Through clique searching, we are also able to show that several of these upper bounds are tight. [ABSTRACT FROM AUTHOR]

This article introduces the notion of interspecies pragmatics and suggests that embodied CA studies can inform such pragmatics. The aim of the study was to investigate how dogs provide embodied responses to human utterances when a human is about to throw a ball for them to catch. Studying dogs' recipiency in interaction requires that multimodal approaches be taken into consideration. The data were collected with GoPro cameras attached to two dogs: each dog brought a first-person camera perspective to the analysis and, when they looked at each other, they also provided a third-person camera angle. The findings based on this multi-camera CA approach demonstrate that when dogs produce embodied responses to human utterances, they can distinguish Goffman's addressed recipient from the unaddressed recipient. Compared to human response production planning time (600–1200 ms), dog response planning time proved much faster (a maximum of 200 ms in this context). Another interesting methodological finding was that the distance between the speaker and recipient influenced the transcription of reaction times in turns-of-talk, as sound travels at only 0.3 m/ms. By contrast, embodied actions observed from video material contained no such transcription-related limitations due to the far higher speed at which light travels. • Interspecies pragmatics is introduced. • A model for organizing a dog-initiated ball game is presented. • Dogs can distinguish addressed recipients from unaddressed recipients. • Dogs' response planning time is faster than humans. • The distance between speakers influences transcribing talk-in-interaction. [ABSTRACT FROM AUTHOR]

A dynamic general equilibrium model is constructed and analyzed under the assumptions that the representative decision maker is endowed with incomplete knowledge about his environment of interest along with limited cognitive skills. Under various beliefs about the long-run growth prospects consistent with Kaldor 's, 1961 stylized facts, the benchmark infinite-dimensional decentralized planning problem collapses into infinite sequences of T > 1 dimensional sub-problems which are each within the decision maker's limited computing range. Based on the time length of the planning horizon and the nature of expectations, the resulting framework in which both forecasts and plans may get revised in the light of new information encompasses at the limits the benchmark infinite horizon case with perfect foresight, a rolling infinite horizon case, a self-fulfilling static planning case and Day 's, 1969 two-period heuristic case. Under exogenous price and quantity growth forecasts consistent with the perfect foresight steady-state balanced growth path, the time length of the planning horizon may have no effect on the resulting competitive equilibrium which may deviate dramatically from the optimal solution. When these growth forecasts are endogenous, the competitive equilibrium effects of the planning horizon time length depend on the characteristics of the information sets. The resulting model may help us to better understand how economies respond to shocks by unraveling new propagation mechanisms related to changes in optimism or pessimism about short-run and/or long-run growth prospects. [ABSTRACT FROM AUTHOR]

Advance care planning (ACP) discussions seek to guide future serious illness care. These discussions may be recorded in the electronic health record by documentation in clinical notes, structured forms and directives, and physician orders. Yet, most studies of ACP prevalence have only examined structured electronic health record elements and ignored data existing in notes. We sought to investigate the relative comprehensiveness and accuracy of ACP documentation from structured and unstructured electronic health record data sources. We evaluated structured and unstructured ACP documentation present in the electronic health records of 435 patients with cancer drawn from three separate healthcare systems. We extracted structured ACP documentation by manually annotating written documents and forms scanned into the electronic health record. We coded unstructured ACP documentation using a rule-based natural language processing software that identified ACP keywords within clinical notes and was subsequently reviewed for accuracy. The unstructured approach identified more instances of ACP documentation (238, 54.7% of patients) than the structured ACP approach (187, 42.9% of patients). Additionally, 16.6% of all patients with structured ACP documentation only had documents that were judged as misclassified, incomplete, blank, unavailable, or a duplicate of a previously entered erroneous document. ACP documents scanned into electronic health records represent a limited view of ACP activity. Research and measures of clinical practice with ACP should incorporate information from unstructured data. [ABSTRACT FROM AUTHOR]

• We studied the advanced air mobility aircraft scheduling problem at vertiports. • We considered separation rules at touchdown and lift-off pads and blocking constraints. • Two mixed integer linear programming formulations are presented for optimally solving small instances. • We propose two heuristic algorithms for solving real-life sized instances. • The computational results provide insights into vertiport operations. Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports' common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances. [ABSTRACT FROM AUTHOR]

With the increasing competition in the manufacturing market, reconfigurable manufacturing system (RMS) is gaining more and more attention because it can quickly respond to the changes in the market by adjusting manufacturing resources and organizational structure. Reconfigurable flexible job-shop (RFJS) is one of the main forms of RMS, which is composed of multiple reconfigurable manufacturing cells (RMCs). Each RMC contains several manufacturing equipment which can process a job simultaneously or continuously without multiple clamping. For RMS, both manufacturing resource configuration and production scheduling have direct impacts on its production efficiency, and they are strongly coupled and need to be considered simultaneously. In addition, multi-machine cooperative machining (MCM) has been realized and applied in various manufacturing scenarios with a wide application of intelligent manufacturing equipment such as mechanical arms. However, it is rarely considered in the production plan of RMS. Based on this background, this paper proposes a reconfiguration scheduling problem considering multi-machine cooperation (MCRSP) for RFJSs. Firstly, a multi-phase scheduling method and two manufacturing resource adjustment strategies are designed, and the mathematical model of MCRSP is established with reconfiguration costs and maximum completion time as evaluation indexes. Then, a three-layer coding method is designed, and a Scout and Mutation-based Aquila Optimizer (SMAO) is developed to solve the MCRSP model. In SMAO, the scout bee strategy and gaussian mutation strategy are introduced to improve the exploration ability in later iterations; a new leader selection method is proposed to avoid falling into local optimum prematurely; the balanced mechanism between exploitation and exploration is redesigned to ensure the search efficiency and convergence accuracy. A comparative experiment shows that SMAO contributes 18 optimal values among the 23 standard test functions, demonstrating the enhancement strategies' pertinence and effectiveness. Finally, the calculation and analysis of a practical case show that the proposed MCRSP can significantly improve production efficiency with a limited increase in reconfiguration costs, so it is suitable for practical engineering. [ABSTRACT FROM AUTHOR]

We study the problem of minimizing total load on a proportionate openshop. The problem is proved to be NP-hard. A simple LPT (Longest Processing Time first)-based heuristic is proposed, and a bound on the worst-case relative error is introduced: 7 6 − 1 6 m (where m is the number of machines). The proposed bound is smaller than the classical bound on the relative error of LPT when minimizing makespan on parallel identical machines. The algorithm is tested numerically and is shown to produce very close-to-optimal schedules. [ABSTRACT FROM AUTHOR]

Factory layout planning is a time-consuming process that has a large impact on the operational performance of a future factory. Besides, changing technologies and market requirements result in a frequent reconfiguration of the factory layout. Automated planning approaches can generate high-quality layout solutions and reduce the planning time compared to mere manual planning. Recent studies indicate that reinforcement learning is a suitable approach to support the early phase of the layout planning process. In this context, reinforcement learning shows potential performance-related advantages by learning the problem-related interdependencies compared to current metaheuristic approaches, which are commonly applied to the regarded problem. However, recent studies only consider a low number of reinforcement learning approaches and regarded application scenarios. In consequence, the performance in different problem sizes and of various existing reinforcement learning approaches has not been investigated. Besides, no comparison between reinforcement learning approaches and existing metaheuristics was performed for factory layout planning. As a consequence, the potential of reinforcement learning based factory layout panning can not be evaluated appropriately. Therefore, an encompassing comparison to metaheuristics is still an open research question. Regarding this background, the performance of 13 different reinforcement learning and 7 commonly used metaheuristics for three layout planning problems with different sizes is investigated in this paper. The approaches are applied to all three layout planning problems in order to compare their performance capabilities. The results indicate that the best-performing reinforcement learning approach is able to find similar or superior solutions compared to the best-performing metaheuristics. • Performance comparison of Reinforcement Learning approaches and metaheurtics for the early stages of factory layout planning • Parameter tuning for all approaches to improve the comparison basis • Comparison based on three factory layout planning problems with rising complexity [ABSTRACT FROM AUTHOR]

This study aimed to identify factors associated with strut-adjusted volume implant (SAVI) displacement in accelerated partial breast irradiation (APBI) using a SAVI device. We retrospectively analyzed computed tomography scans taken at the time of treatment planning and immediately before treatment in 61 patients (median age; 55 years, range; 40–85) treated with SAVI and determined the amount of SAVI displacement that occurred between the time from planning to the treatment. The displacement was calculated for the CT axis and SAVI axis, which is related to the SAVI structure. To investigate the cause of the displacement, multivariate analysis was performed on the calculated standard deviation and the insertion angle of SAVI with respect to the sternum in each cross-section, breast density, amount of air around the SAVI, and SAVI length inside the patient to obtain the β coefficient (p -value). On the CT coordinate system, positive correlations were observed between the SAVI insertion angle and air volume in the lateral (β coefficient:0.255–0.483) and rotational directions (β coefficient:0.341). On the SAVI coordinate system, positive correlations were observed between the SAVI insertion angle and air volume in all lateral (β coefficient:0.270–0.354) and rotational directions (β coefficient:0.294). A negative correlation was observed between the SAVI length inside the patient and the rotational direction (β coefficient: −0.262). SAVI insertion angle, the amount of the air outside SAVI and SAVI insertion length are factors which affect the displacement of the applicator. From the results, the applicator displacement and rotation must be < 3 mm and 10o in order to meet all the dose criteria. Thus, we should be aware of these factors during insertion of the device to avoid the problem in treatment delivery for the APBI. [ABSTRACT FROM AUTHOR]

Retrospective analysis of volumetric modulated arc therapy plans for hypofractionated left whole-breast irradiation with simultaneous integrated boost to assess the performance of the auto-planning (AP) engine. Fifteen treatment plans, produced using manual optimization planning approach (MP) were replanned using (AP) approach. Dose-volume parameters were defined to quantify the quality of concurrent treatment plans assessing target coverage and sparing organs at risk (OARs). The Wilcoxon Signed Rank test was used for statistical comparison of all results obtained from the use of the 2 approaches. Dose coverage for both PTVs, PTV breast, and PTV boost , were similar with AP showing slightly significantly better results for the homogeneity index for both PTVs, for D 98% of PTV breast and D 2% of PTV boost. AP plans provided a significant reduction of dose for ipsilateral lung and contralateral lung. No significant differences were observed for heart and contralateral breast. A percentage difference of -14.0% was found for the mean dose of left coronary artery between AP plans and MP plans. Despite increase of total MU by 4.3% for AP plans, planning time resulted about half of that of the MP approach. Although PTVs doses were similar between MP and AP plans, AP plans generally spared OARs significantly better than MP plans. Furthermore, the shortest AP treatment plan time approach was attractive with respect to the workload. [ABSTRACT FROM AUTHOR]

A graph G is called interval colorable if it has a proper edge coloring with colors 1 , 2 , 3 , ... such that the colors of the edges incident to every vertex of G form an interval of integers. Not all graphs are interval colorable; in fact, quite few families have been proved to admit interval colorings. In this paper we introduce and investigate a new notion, the interval coloring thickness of a graph G , denoted θ int (G) , which is the minimum number of interval colorable edge-disjoint subgraphs of G whose union is G. Our investigation is motivated by scheduling problems with compactness requirements, in particular, problems whose solution may consist of several schedules, but where each schedule must not contain any waiting periods or idle times for all involved parties. We first prove that every connected properly 3-edge colorable graph with maximum degree 3 is interval colorable, and using this result, we deduce an upper bound on θ int (G) for general graphs G. We demonstrate that this upper bound can be improved in the case when G is bipartite, planar or complete multipartite and consider some applications in timetabling. [ABSTRACT FROM AUTHOR]

Human long duration exploration missions (LDEMs) raise a number of technological challenges. This paper addresses the question of the crew autonomy: as the distances increase, the communication delays and constraints tend to prevent the astronauts from being monitored and supported by a real time ground control. Eventually, future planetary missions will necessarily require a form of astronaut self-scheduling. We study the usage of a computer decision-support tool by a crew of analog astronauts, during a Mars simulation mission conducted at the Mars Desert Research Station (MDRS, Mars Society) in Utah. The proposed tool, called Romie , belongs to the new category of Robust Advanced Modelling and Scheduling (RAMS) systems. It allows the crew members (i) to visually model their scientific objectives and constraints, (ii) to compute near-optimal operational schedules while taking uncertainty into account, (iii) to monitor the execution of past and current activities, and (iv) to modify scientific objectives/constraints w.r.t. unforeseen events and opportunistic science. In this study, we empirically measure how the astronauts, who are novice planners, perform at using such a tool when self-scheduling under the realistic assumptions of a simulated Martian planetary habitat. [ABSTRACT FROM AUTHOR]

The reducing cost of solar and wind energy together with the UK commitments to net-zero emissions will mean that UK energy systems for 2050 and similarly those in many other countries will be dominated by variable renewable supplies. Electricity systems are expected to be very reliable but renewable energy is inherently intermittent. We explore energy storage as a solution to this problem, considering the physics of the system to gain understanding of its needs, rather than using its economics, which may lead to less adequate designs. The scale and the periodic nature of the energy storage problem are crucial to system design. There are very different physical needs for storing energy for: days, weeks and years. Therefore a range of storage technologies with their differing characteristic will be required for these different periods. A UK energy system with three types of storage is modelled to both dimension and schedule these stores in light of the physical features and the cost parameters of potential storage technologies. The scale of the UK's energy storage need is large - more than a thousand times that of current storage systems - potentially increasing the energy costs of a 2050 energy system based largely on solar and wind, by a significant amount. Though the UK is considered as an example, the methods could be used for any country that is planning an energy system with a majority of variable renewables and a net-zero carbon target. [ABSTRACT FROM AUTHOR]

Interest in reduced-dose pneumococcal conjugate vaccine (PCV) schedules is growing, but data on their ability to provide direct and indirect protection are scarce. We evaluated 1 + 1 (at 2 months and 12 months) and 0 + 1 (at 12 months) schedules of PCV10 or PCV13 in a predominately unvaccinated population. In this parallel, single-blind, randomised controlled trial, healthy infants aged 2 months were recruited from birth records in three districts in Ho Chi Minh City, Vietnam, and assigned (4:4:4:4:9) to one of five groups: PCV10 at 12 months of age (0 + 1 PCV10), PCV13 at 12 months of age (0 + 1 PCV13), PCV10 at 2 months and 12 months of age (1 + 1 PCV10), PCV13 at 2 months and 12 months of age (1 + 1 PCV13), and unvaccinated control. Outcome assessors were masked to group allocation, and the infants' caregivers and those administering vaccines were not. Nasopharyngeal swabs collected at 6 months, 12 months, 18 months, and 24 months were analysed for pneumococcal carriage. Blood samples collected from a subset of participants (200 per group) at various timepoints were analysed by ELISA and opsonophagocytic assay. The primary outcome was the efficacy of each schedule against vaccine-type carriage at 24 months, analysed by intention to treat for all those with a nasopharyngeal swab available. This trial is registered at ClinicalTrials.gov , NCT03098628. 2501 infants were enrolled between March 8, 2017, and July 24, 2018 and randomly assigned to study groups (400 to 0 + 1 PCV10, 400 to 0 + 1 PCV13, 402 to 1 + 1 PCV10, 401 to 1 + 1 PCV13, and 898 to control). Analysis of the primary endpoint included 341 participants for 0 + 1 PCV10, 356 0 + 1 PCV13, 358 1 + 1 PCV10, 350 1 + 1 PCV13, and 758 control. At 24 months, a 1 + 1 PCV10 schedule reduced PCV10-type carriage by 58% (95% CI 25 to 77), a 1 + 1 PCV13 schedule reduced PCV13-type carriage by 65% (42 to 79), a 0 + 1 PCV10 schedule reduced PCV10-type carriage by 53% (17 to 73), and a 0 + 1 PCV13 schedule non-significantly reduced PCV13-type carriage by 25% (–7 to 48) compared with the unvaccinated control group. Reactogenicity and serious adverse events were similar across groups. A 1 + 1 PCV schedule greatly reduces vaccine-type carriage and is likely to generate substantial herd protection and provide some degree of individual protection during the first year of life. Such a schedule is suitable for mature PCV programmes or for introduction in conjunction with a comprehensive catch-up campaign, and potentially could be most effective given as a mixed regimen (PCV10 then PCV13). A 0 + 1 PCV schedule has some effect on carriage along with a reasonable immune response and could be considered for use in humanitarian crises or remote settings. Bill & Melinda Gates Foundation. For the Vietnamese translation of the abstract see Supplementary Materials section. [ABSTRACT FROM AUTHOR]

In this work, a novel perspective is developed to investigate the property of controllability/observability of linear switched sampled-data systems under the non-equidistant sampling schedule. In this regard, two new notions are introduced as ϵ -controllability and ϵ -observability to create a feasible ground for controlling and observing linear switched sampled-data systems with any initial time chosen from outside of ϵ neighborhood of switching instants. The main motivation is to control and observe the given switched sampled-data system, which consists of a finite number of discrete-time sub-systems within each sub-system. Hence, the system requires a lower number of sampling times for ϵ -controllability and ϵ -observability compared to the original notions of controllability and observability in this context. Although the number of sampling times in a certain time interval increases whenever ϵ tends to zero, the number of sampling candidates required for ϵ -controllability and ϵ -observability becomes finite. Numerical experiments are performed on several continuous time switched linear systems, and ϵ -controllability and ϵ -observability of their corresponding switched sampled-data systems are derived for various ϵ values under constructed non-equidistant sampling patterns. • Notions of ϵ -controllability and ϵ -observability are introduced in switched systems. • Kalman rank condition provides the property of ϵ -controllability/ ϵ -observability. • ϵ -Controllability/ ϵ -Observability requires less number of sampling times. • Different kinds of industrial problems are investigated in this context. [ABSTRACT FROM AUTHOR]

Fog computing, an emerging paradigm, extends cloud capabilities to the network's edge, enhancing real-time data processing and analysis. This study focuses on optimizing fog computing systems using an adaptive scheduling algorithm. It efficiently categorizes incoming tasks based on their security requirements into private, semi-private, and public groups, distributing them among Mobile Data Centers (MDCs) and Cloud Data Centers (CDCs). By considering various parameters like job quantities, MDCs, CDCs, their capacities, and communication delays, the algorithm ensures tasks are allocated optimally. The performance of three scheduling algorithms (FCFS, SJF, EDF) is analyzed in both homogeneous and heterogeneous fog environments, shedding light on system efficiency, workload distribution, and resource management. This study provides valuable guidance for the design and operation of efficient fog computing infrastructures tailored to the diverse requirements of IoT applications while optimizing resource utilization. [ABSTRACT FROM AUTHOR]

This study examines the optimal scheduling and routing of electric vehicles across multiple time periods to reduce battery consumption and commute times. The objective is to convey products to consumers using a fleet of electric vehicles as efficiently as feasible. This hybrid solution addresses this issue and combines the most advantageous aspects of the Local Search 2-Opt and Particle Swarm Optimization (PSO) metaheuristics. Local Search 2-Opt is used to enhance the quality of the original solutions, whereas PSO is used to search through all possible solutions and identify the best one. Considering constraints such as limited vehicle capacity, limited charging station availability, and limited time, the proposed method optimizes the coupling of consumers with electric vehicles. The evaluation results indicate that the hybrid algorithm accomplishes more significant energy savings, travel time savings, and overall solution quality than the alternatives currently considered to be state-of-the-art. This research's findings can be used to optimize EVRS operations and improve the administration of electric vehicle fleets. [ABSTRACT FROM AUTHOR]

A technology called cloud computing makes it possible to offer computing resources including storage, software, and services over the internet. Users can access these resources whenever they need them and just pay for what they actually use. There is a strict need to maintain track of all the resources which are being used by machines to perform certain activities such as scheduling of tasks. Resource scheduling and load balancing are important techniques used in modern computing systems to improve performance and resource utilization. Resource scheduling algorithms are used to allocate resources such as CPU, memory, and storage to different tasks or processes. Load balancing algorithms, on the other hand, distribute incoming traffic or workloads across multiple servers or resources to ensure that no single resource becomes overloaded. These techniques are essential for maintaining the stability and efficiency of large-scale systems, such as cloud computing environments, web services, and distributed systems. There are various kinds of load-balancing algorithms that differ in terms of their complexity, flexibility, and performance. Selecting the right algorithm for a specific system depends on the system's specific requirements and constraints. Overall, resource scheduling and load-balancing algorithms play a crucial role in ensuring the smooth operation of modern computing systems and are an active area of research and development. [ABSTRACT FROM AUTHOR]

Effective project planning is crucial for the successful execution of complex industrial projects, as it can significantly reduce costs and enhance overall efficiency. However, applying precise methods for obtaining optimal project plans is often limited to small activity graphs, rendering them impractical for real-world scenarios. To address this issue, heuristic and metaheuristic algorithms have been the focus of extensive research over the past few decades. This study explores an extended problem formulation known as the Multiple Contractor-Resource Project Scheduling Problem (MC-MRCPSP). This formulation extends beyond the basic problem structure, incorporating various constraints typically encountered in industrial project scheduling. To tackle this challenging optimization problem, we propose a novel hybrid algorithm that combines a fine-tuned genetic algorithm with an advanced tabu search. This approach strikes an optimal balance between exploration and exploitation of the solution space, leading to enhanced project plan optimization. Through comprehensive experiments and evaluations, we demonstrate the effectiveness of our proposed approach. Compared to a purely genetic algorithm, our hybrid solution achieves a 38% improvement in project plan optimization. These results underscore our study's significant contribution to project planning and lay the groundwork for future advancements in MC-MRCPSP solutions. Ultimately, our research brings us closer to improved resource utilization, cost reduction, and enhanced efficiency in managing complex industrial projects. [ABSTRACT FROM AUTHOR]

Medium-term optimal scheduling of hydropower plants (MOSHPP) should be frequently updated for recurring extreme precipitation events in China to reduce spillage and increase power generation; however, the curse of dimensionality makes obtaining a satisfactory solution in an acceptable time difficult. In this paper, a parallel improved dynamic programming with successive approximation (PIDPSA) for MOSHPPs is proposed to improve the solution quality and meet time requirements. The solution quality is improved by successive approximation of multiple plants instead of one plant for considering more hydraulic connections, a state space reduction strategy combined with constraint preprocessing is adopted to reduce unnecessary calculations and the fine-grained parallelism based on the fork/join framework is employed to greatly shorten the solution time. The proposed method was applied to 11 hydropower cascade plants in the Lancang River with a time horizon of 30 days. The results showed that higher quality solutions can be obtained by IDPSA than conventional methods and DPSA, and the space reduction strategy can effectively improve the solution efficiency. The computation time of PIDPSA decreased from 81345 s for the single-core environment to 6073 s for the 32-core environment, which proves the high efficiency and practical value of PIDPSA for solving MOSHPP problems. [ABSTRACT FROM AUTHOR]

Correction of the digital designs have emerged as a major bottleneck at Register Transfer Level (RTL) due to the growing complexity of the digital systems and shortening time-to-market. Existing automated correction methods face problems like large number of error root causes, poor scalability, and multiple design errors. To address these problems, we propose an automatic correction methodology based on a lightweight partial high level synthesis (HLS) flow which takes as inputs a golden C specification and a buggy RTL design and outputs the corrected RTL model. Synthesizing the specification using a HLS tool without considering the buggy RTL to obtain the corrected RTL is not the case because manual RTL optimizations done by the designer should be preserved so that the architecture of the corrected RTL design should be as similar as possible to that of the buggy one. The results on industrial large designs show that the proposed methodology achieves 92.3% less new resources and 81.1% less connections compared to synthesizing the golden specification using a HLS tool without considering the architecture of the buggy RTL design. • A lightweight partial high level synthesis mechanism to correct RTL designs. • Preserving manual RTL optimizations done by the designer. • Addressing the large number of error root causes issue. [ABSTRACT FROM AUTHOR]

• Develop a generic framework for maintenance scheduling of multi-component systems with environment modeling, state representation and policy optimization. • Propose Guided-Probabilistic Reinforcement Learning for sampling-efficient maintenance scheduling by incorporating domain expert knowledge. • Develop Bayesian formulations to address data sparsity for environmental reward modeling, performance degradation, and exploration of action spaces. In recent years, multi-agent deep reinforcement learning has progressed rapidly as reflected by its increasing adoptions in industrial applications. This paper proposes a Guided Probabilistic Reinforcement Learning (Guided-PRL) model to tackle maintenance scheduling of multi-component systems in the presence of uncertainty with the goal of minimizing the overall life-cycle cost. The proposed Guided-PRL is deeply rooted in the Actor-Critic (AC) scheme. Since traditional AC falls short in sampling efficiency and suffers from getting stuck in local minima in the context of multi-agent reinforcement learning, it is thus challenging for the actor network to converge to a solution of desirable quality even when the critic network is properly configured. To address these issues, we develop a generic framework to facilitate effective training of the actor network, and the framework consists of environmental reward modeling, degradation formulation, state representation, and policy optimization. The convergence speed of the actor network is significantly improved with a guided sampling scheme for environment exploration by exploiting rules-based domain expert policies. To handle data scarcity, the environmental modeling and policy optimization are approximated with Bayesian models for effective uncertainty quantification. The Guided-PRL model is evaluated using the simulations of a 12-component system as well as GE90 and CFM56 engines. Compared with four alternative deep reinforcement learning schemes, the Guided-PRL lowers life-cycle cost by 34.92 % to 88.07 %. In comparison with rules-based expert policies, the Guided-PRL decreases the life-cycle cost by 23.26 % to 51.36 %. [ABSTRACT FROM AUTHOR]

With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. [ABSTRACT FROM AUTHOR]

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows' deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experiments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process. [ABSTRACT FROM AUTHOR]

Attention to energy-efficient permutation flow shop scheduling problems is increasing in the context of globalization and environmental awareness. However, the energy-efficient no-idle permutation flow shop scheduling problem (NIPFSP) has rarely been studied. This study proposes a procedure using Beluga Whale Optimization (BWO) to solve the energy-efficient NIPFSP issue to reduce total energy consumption (TEC). The offered procedure is tested using four job and machine variations and compared with the PSO algorithm. An independent sample t-test was performed to test the optimization results of the BWO and PSO in 4 cases. The results indicate that the offered procedure produces lower total energy consumption than the PSO algorithm. In addition, the algorithm also provides more competitive results when solving the energy-efficient NIPFSP problem with a larger number of jobs. The implications of this academic research show that the proposed procedure can be applied to solve the problem of energy-efficient NIPFSP, which is indicated by low energy consumption. [ABSTRACT FROM AUTHOR]

It is a consensus that hydropower should be clean and renewable. The economic emission dynamic scheduling of hydro hybrid combined heat and power (Hydro-CHP) systems allows for further reduction of greenhouse gas emissions from the power sector. A new version of our Membrane Search Algorithm, denoted as IMSA, is proposed to solve the complex dynamic scheduling problems of CHP and hydro-thermal systems with spatio-temporal coupling, which can obtain competitive solutions. Furthermore, based on IMSA, we construct a multi-objective algorithm, called the Multi-objective Improved Membrane Search Algorithm (MOIMSA), which can solve the complex economic emission dynamic scheduling problem of CHP systems with ramp limits, and provide a solution set with relatively less emissions. Finally, a solution to the dynamic scheduling problem of the Hydro-CHP system is proposed. IMSA obtains the maximum power output of the hydro unit, and MOIMSA is used to search for the Pareto front of the economic emission of CHP. Compared with the unimproved MOMSA, the compromise solution provided by MOIMSA is better. Experiments show that this study contributes to the efficient use of renewable energy and the reduction of emissions. [ABSTRACT FROM AUTHOR]