Planning Scheduling Research Articles

Optimizing the anesthetic care of patients with aromatic l-amino acid decarboxylase deficiency.

Aromatic l-amino acid decarboxylase (AADC) deficiency is a rare autosomal recessive disorder that results in a lack of the monoamine neurotransmitters dopamine, serotonin, norepinephrine, and epinephrine. Patients present with a wide spectrum of symptoms, including motor and autonomic dysfunction, hypotonia, and developmental delay, often before the age of one. Until recently, treatment options were limited to symptom control, but the recent approval of the first gene therapy for AADC deficiency in Europe and the UK has provided an alternative to treating symptoms for this disease. Eladocagene exuparvovec is a one-time gene therapy, administered bilaterally to the putamen by magnetic resonance imaging-guided stereotactic neurosurgery. While administration of the gene therapy itself is minimally invasive, the anesthetic management of patients with AADC deficiency is challenging due to the absence of sympathetic regulation secondary to the lack of adrenergic neurotransmitters. Optimal anesthetic management requires an understanding of the complex and heterogeneous nature of the disease. Hemodynamic instability, temperature dysregulation, and hypoglycemia are of primary concern, but there are also challenges regarding intravenous access and airway management. A thorough preoperative assessment is essential and should be guided by the patient's history. Advanced planning is necessary regarding the timing of the procedure schedule and operative plan; meticulous preparation, simulation for the operating room, as well as communication with all perioperative staff members, are crucial. Intraoperatively, utmost care must be taken to protect the skin, maintain body temperature, and to prepare for inotropic and/or glycemic support as needed. Postoperative intensive care management is necessary for consideration of postoperative extubation and provision of supportive care. With careful planning, preparation, and vigilance, patients with AADC deficiency can safely undergo anesthesia.

Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours.

Resilient shuttle vehicle plan under route disruption scenarios and ticketing policies: a case study of the Qiandaohu archipelago scenic area, China

ABSTRACT Scenic areas in an archipelago context are often accessed with shuttle vehicles to transport tourists between different scenic spots. Vehicle routing and schedule planning could be challenging when route disruptions are considered frequent and fragile areas are protected and conserved. Transport provisions also affect the number, sequence and patterns of visited spots for each tourist, which in turn may not only impact the revenue of the scenic area but also on visitor satisfaction. This paper develops a mixed-integer programming model for this optimisation problem at scenic areas by considering two common ticketing policies: ‘all-inclusive policy’ and the ‘two-part tariff policy’. Experimental results based on the case study of Qiandaohu scenic area in China demonstrate that our method can obtain high-quality solutions for real-sized problems where some routes are disrupted. It is found that ticketing policies may have different impacts on shuttle vehicle planning. Under the all-inclusive policy, the shuttle vehicle planning maximises the total profit. Under the two-part tariff policy, however, the plan needs to maximise the profit to strike a balance between the operational cost and the number of spots visited by tourists. Our study may shed light on the effectiveness of shuttle vehicle planning and ticketing policy selection.

Two-stage scheduling of apron support vehicles considering multi-vehicle coordination

Large airport apron support vehicles have always been facing great operating pressure, and the coordination constraints of various apron support services and dynamic flight information pose higher challenges to vehicle scheduling. Considering the differences in vehicle operation constraints in three different modes: continuous work, continuous work with capacity constraints, and round-trip work, a multi-vehicle coordinated apron support vehicle scheduling model is established with the goal of minimizing the number of vehicles and the total driving distance. According to the actual operation of the airport, the model is solved in two stages. In the static stage, an NSGA II algorithm integrating local search is designed, and in the dynamic stage, a neighborhood search algorithm is designed to solve the multi-vehicle coordinated scheduling problem. Data simulation results show that compared with first-come-first-served, the number of vehicles and the total driving distance in the static scheduling stage are reduced by 18.9%and respectively 8.9%; the dynamic scheduling stage results can maintain the number of vehicles in the original scheduling plan, and the adjustment of driving distance is reduced compared with the large-scale neighborhood search algorithm 25.8% . The research results can provide certain guiding significance for the scheduling management and decision-making of apron support vehicles in large airports.

Research on charging scheduling optimization of pure electric buses considering peak-valley time-of-use electricity prices and battery loss costs

In recent years, with the accelerated progress of pure electric buses in my country, the charging scheduling and operation plan of pure electric buses urgently needs to be rationalized and scientific. This study takes the charging scheduling operation in the daily operation of pure electric buses as the starting point, and effectively describes the battery loss cost function by analyzing the optimal fluctuation range of the state of charge of the power battery of pure electric buses. Combined with the analysis of the characteristics of peak and valley time-of-use electricity prices during the charging period, the problem is refined into a pure electric bus charging scheduling decision with controllable operation completion, and the parallel machine scheduling theory is used to characterize and solve the mathematical optimization model, with the aim of minimizing the operating cost of bus companies. At the same time, an immune optimization algorithm based on random key encoding and a greedy algorithm based on the idea of avoiding peaks and filling valleys are developed to solve large-scale problems. In addition, the effectiveness of the algorithm and the applicability of the method are verified through the real bus network in Shanghai.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Robust day-ahead scheduling of cooperative energy communities considering multiple aggregators

Future cities must play a vital role in reducing energy consumption and decarbonizing the electricity sector, thus evolving from passive structures towards more efficient smart cities. This transition can be facilitated by energy communities. This emerging paradigm consists of collectivizing a set of residential installations equipped with onsite renewable generators and storage assets (i.e., prosumers), which can eventually share resources to pursue collective welfare. This paper focuses on cooperative communities, where prosumers share resources without seeking selfish monetary counterparts. Despite their apparent advantages, energy management and scheduling of energy communities suppose a challenge for conventional tools due to the high level of uncertainty (especially due to intermittent renewable generation and random demand), and privacy concerns among prosumers. This paper addresses these issues. Specifically, a novel management structure based on multiple aggregators is proposed. This paradigm preserves users' confidential features while allowing them to extract the full potential of their assets. To efficiently manage the variety of assets available under uncertainty, an adaptive robust day-ahead scheduling model is developed, which casts as a solvable and portable Mixed Integer Linear Programming framework, which eases its implementation in real-world cases. The new proposal concerns uncertain generation and demand using a polyhedral representation of the uncertainty set. A case study is conducted to validate the developed model, showing promising results. Moreover, different results are obtained and analysed. Finally, it is worth remarking on how the level of robustness impacts the collective bill, incrementing it by 75 % when risk-averse conditions are assumed. In addition, the role of storage assets under pessimistic conditions is remarked, pointing out that these assets rule the scheduling plan of the community instead of renewable generators.

Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm.

Dynamic production scheduling and maintenance planning under opportunistic grouping

Maintenance and production scheduling are intertwined activities that should be addressed simultaneously to uphold production systems’ reliability and production efficiency. The digital twin is an emerging technology that advances industrial digitalization, as it embeds a “virtual” image of reality that runs in line with the real system, enabling evaluation, optimization, and prediction of the physical system’ state. On the other hand, deep reinforcement learning (DRL) can provide real-time decision-making by leveraging real-time data from the digital twin such as condition monitoring and production progress data. This research addresses the joint flexible job shop scheduling and maintenance planning problem, considering new job insertions and multi-component machines with economic dependencies among components. The objective is to minimize both the expected total tardiness and maintenance cost, considering opportunistic grouping of maintenance activities on components and breakdown costs associated with failure risk. To achieve this joint decision-making, we develop a hierarchical architecture with two interconnected hierarchies. The upper-level hierarchy determines whether to switch the decision-making process between production scheduling and maintenance planning. Subsequently, the lower-level hierarchy selects an action through the corresponding agent: a multi-head Deep Q-Network (DQN) if the maintenance option is chosen, and a DQN with seven dispatching rules for the production option. The computational experiment results reveal that the proposed scheduling method can learn a high-quality dispatching policy, outperforming the non-hierarchical DRL approach and individual dispatching rules in solution quality, CPLEX in runtime efficiency, and the genetic algorithm and particle swarm optimization in both solution quality and runtime efficiency.

Joint Optimization of Personalized Route Planning and Global Traffic Scheduling: A DT-Enabled Integrated Framework

Joint Optimization of Personalized Route Planning and Global Traffic Scheduling: A DT-Enabled Integrated Framework

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

Stochastic optimal allocation of grid-side independent energy storage considering energy storage participating in multi-market trading operation

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective.

Adaptive assessment based on fractional CBCT images for cervical cancer.

Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images. Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA. The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan. This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.

Optimal operation model of ecological flow of hydropower station based on genetic algorithm and neural network

Amid diverse water demands, the conflict between ecological flow adjustments and power generation flow adjustments in hydropower station water resource dispatching, coupled with an increasing diversity of constraints, complicates the issue of water resource optimization and dispatching. How to scientifically develop feedback regulation mechanisms to promote a virtuous ecological cycle, fine tune existing hydropower station scheduling plans, and achieve multi-objective optimization scheduling is the core issue in researching the comprehensive utilization of water resources. This study establishes a reservoir flow regulation model with the objective functions of maximizing power generation and achieving optimal average ecological protection. The model, constrained by water balance, reservoir capacity, unit output, and unit overflow, was optimized and solved using the genetic algorithm-backpropagation neural network (GA-BPNN) approach. This study establishes a reservoir flow regulation model with the GA-BPNN algorithm was applied in multi-objective optimization to identify an optimal solution that maximizes overall system performance while adhering to ecological flow constraints. The results indicate that the optimized plan has performed effectively in hydropower station operations. It not only satisfies ecological flow requirements, but also significantly enhances power generation efficiency. Specifically, power generation increased by 32,680 kw·h, a 23.85% growth rate. The optimized plan significantly enhances the comprehensive utilization efficiency of water resources. It offers a scientific basis for the rational planning, dispatching and utilization of water resources in hydropower stations and reservoirs.

Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage

Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task dispatching during the time period. It uses an ant colony algorithm for a solution. The model uses vehicle size and mileage as the optimization objectives while considering practical constraints like multi-vehicle operation, maximum pick-up intervals, etc. The feasibility of the model and the algorithm’s effectiveness are verified using the Shanghai Huyi Highway Demonstration Line as a case study. The results indicate that the model can effectively generate the optimal scheduling plan for DRT, significantly reduce the system’s operating cost, and improve resource utilization efficiency.

A cleaner demolition scheduling methodology considering dust dispersion: A case study for a post-earthquake region

In the present century, pollution is a primary concern for billions, prompting governments to advocate cleaner ways of production. Demolition activity is often an indispensable solution for structures that have completed their economic life. However, there are no regulations for the scheduling of demolition, except those related to the method of demolition and ensuring worker safety. Older buildings incorporate hazardous materials, such as asbestos, silica, and lead. These materials not only carry inherent risks, but high levels of aerosols in the air also adversely affect health. In this study, a demolition scheduling method is proposed, considering the dust dispersion. This research is pioneering, providing a structured demolition schedule to minimize the impact on both humans and the environment. In the methodology, a dispersion model is used to calculate the region exposed to dust and the concentration distribution throughout that area. In addition to the dust effect map, a vulnerability map is created using Analytical Hierarchy Process (AHP), aiding in determining interrelations between vulnerable sites. Thus, the dust effect map is derived by considering both dust exposure and the vulnerability map. The region affected by dust and the concentration of dust vary based on wind characteristics. By knowing the dust effect maps for the site (or all sub-sites) during specified time periods, a schedule can be defined. As a case study, schedules causing the absolute minimum and optimum dust effect rates are established for Kahramanmaraş, Türkiye which recently experienced a devastating earthquake. The findings of the case study show that the dust effect on humans and the environment is significantly reduced. Consequently, by adhering to the proposed scheduling plan, human exposure to demolition dust is minimized, resulting in reduced medical expenses even without increasing the cost of the demolition.

Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load

A two-layer scheduling method of energy storage that considers the uncertainty of both source and load is proposed to coordinate thermal power with composite energy storage to participate in the peak regulation of power systems. Firstly, considering the characteristics of thermal power deep peak regulation, a cost model of thermal power deep peak regulation is constructed and fuzzy parameters are used to manage the uncertainty of wind, photovoltaics, and load. Secondly, based on the peaking characteristics and operating costs of composite energy storage, a two-layer optimal scheduling model of energy storage is constructed. The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. Finally, we verify the effectiveness of the proposed strategy based on an IEEE 39-node system.

A novel bi-objective model for sustainable and efficient airport logistics management: A case study on Copenhagen airport

Due to the growing volume of air transportation and the scarcity of airport resources, aviation planners are paying more attention to the efficiency of airport resource management. Optimizing the assignment and scheduling of ground facilities plays an important role in reducing ground congestion, flight delays, and passenger dissatisfaction. To prevent any collisions among aircraft on the apron, taxi network, and runways, safety considerations should be strictly followed by traffic controllers. Minimizing taxi time is another matter of importance in airport logistics management that helps mitigate environmental concerns (e.g., fuel consumption, tire abrasion, and noise pollution). In this study, a bi-objective mixed-integer linear programming model is developed to optimize the ground resource scheduling problem, taking into account the challenges of gate assignment, taxi planning, and runway scheduling. The model is developed in a way that includes safety and sustainability considerations. The validation of the proposed model is investigated through several examples based on Copenhagen airport using the augmented ε-constraint method. Since the simultaneous scheduling of multiple resources is an NP-hard problem, two multi-objective metaheuristic algorithms are employed to deal with the large-scale problems. The performance of the algorithms is then compared in terms of coverage and convergence rates. Additionally, the results are discussed in detail to provide managerial implications for airport authorities.

Dynamic job allocation method of multiple agricultural machinery cooperation based on improved ant colony algorithm

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments.

A Robust Human–Machine Framework for Project Portfolio Selection

Based on the project portfolio selection and scheduling problem (PPSS), the development of a systematic and scientific project scheduling plan necessitates comprehensive consideration of individual preferences and multiple realistic constraints, rendering it an NP-hard problem. Simultaneously, accurately and swiftly evaluating the value of projects as a complex entity poses a challenging issue that requires urgent attention. This paper introduces a novel qualitative evaluation-based project value assessment process that significantly reduces the cost and complexity of project value assessment, upon which a preference-based deep reinforcement learning method is presented for computing and solving project subsets and time scheduling plans. This paper first determines the key parameter values of the algorithm through specific examples. Then, using the method of controlling variables, it explores the sensitivity of the algorithm to changes in problem size and dimensionality. Finally, the proposed algorithm is compared with two classical algorithms and two heuristic algorithms across different instances. The experimental results demonstrate that the proposed algorithm exhibits higher effectiveness and accuracy.