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The solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity. [ABSTRACT FROM AUTHOR]

Distributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively. [ABSTRACT FROM AUTHOR]

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) tariff has been implemented in the manufacturing industry to improve energy efficiency by regulating the electricity imbalance between supply and demand. Besides, not-all-machine (NAM) option is another way of energy-saving by using only a subset of all the available machines. This study investigates a robust identical parallel machine scheduling problem with a two-stage TOU tariff and NAM option. Only interval bounds on job processing times are known. The problem is first formulated into a min–max regret model to maximise the robustness. Based on problem properties, both an iterative relaxation-based exact algorithm and a memetic differential evolution-based heuristic are developed to solve the problem. Computational experiments on 240 randomly generated instances with up to 20 jobs are conducted to evaluate the performance of the developed methods. Besides, 900 large-sized randomly generated instances with up to 150 jobs are tested for sensitivity analysis and to identify managerial insights for achieving energy-efficient schedules. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-objective optimization framework for balanced distribution system planning, based on the allocation and sizing of distributed energy resources (DERs) and capacitor banks (CBs), along with static network reconfiguration. Each individual strategy, involving DERs, CBs, and network reconfiguration, is systematically performed and compared across all possible combinations, with the purpose of expanding the multi-objective search space in terms of overall cost, voltage deviation, and power losses. In this study, daily variations and uncertainties in DERs generation and load are introduced into the optimization problem formulation through Monte Carlo simulations. The Multi-objective Cuckoo Search and the Fuzzy Decision-making Method are adopted to solve the multi-objective optimization problem. Finally, tests conducted on the three-phase balanced 33, 69, and 136-bus distribution systems demonstrate the efficacy of the proposed approach in minimizing costs, reducing voltage deviation, and mitigating power losses. [ABSTRACT FROM AUTHOR]

Extreme dust storms (EDSs) are high-impact low-probability natural disasters, and their occurrence in humid climates can damage the power distribution systems (PDSs) as a critical infrastructure. In this paper, proposed a bi-level stochastic framework for simultaneously hardening substations and distribution lines. In the first level, total capital cost is addressed for PDS hardening under the financial constraints, while in the second level, the expected operating costs are minimized in the case of an EDS under the operating constraints. In the proposed model, the location of remote-controled switches (RCSs) is determined based on the PDS hardening planning results, and the decisions at each level depend on the planning results of the other level. The simulation results at different budget levels show that simultaneous hardening planning of distribution lines and substations considering network reconfiguration can not only reduce expected operating costs, but also can reducing total capital cost to PDS resilience enhancement. [ABSTRACT FROM AUTHOR]

The hydrogen refueling stations (HRSs) are an intermediate hub connecting the upstream hydrogen energy industry and the end users. Reasonable planning of HRSs is an important prerequisite for promoting the development of hydrogen-powered vehicles (HPVs). However, the safety of hydrogen storage has not been fully considered in the existing HRS planning method. Therefore, this paper proposes a coordinated planning model of HRSs and distribution network (DN) considering the hydrogen storage safety indicator and gas-solid two-phase hydrogen storage mode. Firstly, the Trinitrotoluene (TNT) explosion energy is introduced as an HRS safety operation indicator to quantify hydrogen storage risk. Secondly, a gas-solid two-phase hydrogen storage mode is applied to reduce the operational risk of HRS by reasonably converting the storage form of hydrogen and the hydrogen storage model is built based on the dynamic characteristics of hydrogen storage alloy. Thirdly, an unconstrained traffic assignment model is used to determine the distribution of traffic flow in the transportation network (TN), and HRS site selection rules are formulated. Finally, the electric-hydrogen-TN coordinated planning model is constructed considering the safe operation constraints of HRS equipment, DN, and TN. The proposed model is validated by two synergistic networks of different sizes, and the results show that it can make reasonable planning for HRSs and the DN, and effectively improve the operational security of HRS. • Introduce TNT method as quantitative assessment indicator for HRS operational risk. • A gas-solid two-phase conversion model for hydrogen storage is developed. • HRS site selection was designed under coupling network of power and transportation. • Proposed solution has been validated from both safety and economic perspectives. • Reduce total investment cost by 8.4% and dangerous operation time from 8h to 3h. [ABSTRACT FROM AUTHOR]

This paper addresses the integrated problem of production, distribution, and maintenance planning based on a data-driven approach, within the supply chain context, involving multiple production sites and multi-demand. Traditionally, these activities have been treated as separate topics in the literature. We aim to predict an optimal production plan for each site to meet its specific demand. Additionally, we aim to establish a collaborative distribution plan among the sites, bridging the gap between production and demand for needy sites, while minimizing the production-distribution costs and adhering to a service level for each production site. Given that demand forecasting presents a significant challenge, we analyze historical demand data for each site. This analysis aims to accurately forecast demand for each manufacturing site using various deep learning models. We also aim to predict preventive maintenance actions for each site, taking into account the dependency of the failure rate and the production cadence. We propose a data-driven approach that employs deep learning methods, namely the long short-term memory model for forecasting multi-demand and the NeuroEvolution of augmenting topologies model for predicting the three joint plans. The resulting integrated method is assessed using reference datasets. The proposed framework is evaluated using reference datasets, with results being compared across various approaches to highlight the advantages of the proposed framework. [ABSTRACT FROM AUTHOR]

Simulating and predicting urban patterns enables evidence-based decision-making for urban planners. Given limited resources, understanding how to improve urban residential quality and rationally plan the distribution of different quality levels warrants further study. Using cellular automata (CA) and agent-based modules, this study proposes a multi-stakeholder residential quality distribution model to analyze the future quality distribution of residential buildings under different scenarios. The proposed model comprises two modules: a CA module predicting an urban functional layout model and an agent-based module predicting residential quality distribution. The CA module develops the future functional layout of the city, upon which the multiple interests of the government, developers, and residents are taken as constraints by the agent-based module to predict residential quality distribution. The proposed model was applied as a case study to Guanxian County in Shandong Province, China. Three scenario analyses were conducted: a free development scenario, a government macro-regulation scenario, and a scenario with an adjusted preference value for residential quality. The results show that the residential quality distribution model predictions make it possible to align residential quality with residents' needs in different scenarios; hence, unreasonable distribution characteristics can be explored to develop effective improvement measures. [ABSTRACT FROM AUTHOR]

Currently, the world is rapidly advancing in terms of the construction of new power systems, and planning suitable distribution network planning while also considering renewable energy has become a hot issue. Based on this background, this paper studies the distribution network planning problem. Compared with the traditional planning method, the paper considers the impact of load growth and renewable energy penetration and uses the multi-stage planning method to build the planning model; at the same time, in the scenarios selection, the affinity propagation (AP) clustering algorithm is adopted, which can automatically obtain the number of clusters. Based on the proposed model, an IEEE 33-node is used for simulation. The simulation results show that, compared with the traditional static planning method, the total economic cost of the proposed method is reduced by 4.87% and the wind–solar curtailment rate is reduced by 59.01%; in addition, according to the proposed method, the impact of energy storage equipment and wind–solar permeability on the planning results is studied. It is found that, when considering energy storage, the amount of abandoned wind and light decreases by 22.35% and the total cost first decreases and then increases with the increase in wind–solar permeability, while the total economic cost reaches the minimum at about 40%. The impact of load growth rate on the planning results is also studied. Finally, the generalizability of the proposed method is investigated while using the IEEE 69-node system as an example. [ABSTRACT FROM AUTHOR]

Distribution network planning is critical to meet load growth and ensure the reliability of the network. the load demands of electrical distribution networks is increased gradually over time, which raises active and reactive power losses and lowers bus voltages below allowable levels. The modern power system has experienced significant structural modifications as a result of the annual expansion in load. This paper presents the operation of distribution networks with dispatch able mix of different types of distributed energy resources (DERs) units considering load growth up to planning period based on Multi Variant Differential Evolution algorithm (MVDE). The electric power system is supplied by numerous capacity resources including renewable and nonrenewable power resources like photovoltaic system (PV), wind turbine system (WT), fuel cell (FC), and micro-turbine (MT). The main objectives of DERs allocation are to maximize technical, economic and environmental benefits by reducing the power losses, annual cost, and greenhouse gas. The WT, PV, MT, and FC units' capacity is increased by capacity expansion planning in the radial distributed network, which is carried out over a five-year planning horizon. A comprehensive stochastic strategy is offered for a number of uncertainties, such as load increase and output power from renewable energy sources. Two IEEE bus networks are used to illustrate the suggested method's efficacy. The optimization results based on proposed algorithm are compared with some existing algorithms. [ABSTRACT FROM AUTHOR]

ABSTRACT The Birnbaum–Saunders (BS) distribution is used to represent the impact of fatigue on the products produced in a continuous cycle, which is the primary quality characteristic that determines the product's failure time. This article explains the acceptance sampling plan for the BS distribution, which uses percentiles when the life test ends at a certain time, along with the intervened Poisson distribution. More specifically, this article primarily aims to determine the minimum sample size required to confirm the specified life percentile under a specific customer's risk. The operating characteristic values and the producer's risk are established. [ABSTRACT FROM AUTHOR]

Background Purpose Methods and materials Results Conclusion Stereotactic arrythmia radioablation (STAR) is a noninvasive technique to treat ventricular tachycardia (VT). Management of cardiorespiratory motion plays an essential role in VT‐STAR treatments to improve treatment outcomes by reducing positional uncertainties and increasing dose conformality. Use of an electrocardiogram (ECG) signal, acquired in real‐time, as a surrogate to gate the beam has the potential to fulfil that intent.To investigate the gated delivery of volumetric‐modulated arc therapy (VMAT) for STAR on a TrueBeam linear accelerator (linac) using a real‐time acquired ECG signal and a dynamic cardiac phantom.Dosimetric characteristics of a 6 MV flattening filter free (FFF) beam from a Varian TrueBeam linac were initially evaluated under high‐frequency gating scenarios relevant to cardiac rhythms with respect to dose linearity, beam output, and energy quality. A microcontroller board was used to interface and gate the linac, sending a beam on/off signal. For real‐time cardiac gated measurements, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the VMAT delivery to an ArcCHECK detector to a specific phase of the cardiac cycle. Gated dose distributions were compared against those acquired for a non‐gated delivery mode. An in‐house dynamic cardiac phantom was developed to simulate cardiorespiratory motion that correlates target position with the signal to gate the beam. Measured dose distributions using gafchromic film were also compared against the static (reference) mode in different scenarios with and without gating.Maximum difference in dose per monitor unit (MU) was found to be no greater than 1% as compared to static mode with variation in the chamber response within 0.2% in the range of 50 MUs to 200 MUs. Maximum percentage differences for the beam output and beam qualiy index (TPR20,10) between gated and non‐gated modes were 0.91% and ‐0.44%, respectively. Comparison of delivered dose distributions for the VMAT plan without gating versus ECG synchronized gating modes provided a passing rate 98% for the gamma analysis with 1% relative dose difference, 1 mm distance‐to‐agreement criteria. For the synchronization of dose delivery with target position, passing rates were 98%, 97%, and 99% for the axial, coronal, and sagittal planes, respectively, when gating the beam based on target position for cardiac motion only, for 3%, 3 mm tolerance as compared to static mode. Without gating the beam, passing rates of the respective plans are 97%, 94%, and 99% for the cardiac motion only, and 67%, 57%, and 55% when including respiratory component of motion.A 6 MV‐FFF TrueBeam is stable for performing gating in STAR under high‐frequency gate windows within typical cardiac cycles. Agreement between measured dose distributions for a VMAT plan in static and ECG‐synchronized deliveries and between static and target‐position gated modes shows that the proposed methodology is feasible and can be implemented on a TrueBeam platform. [ABSTRACT FROM AUTHOR]

As the penetration level of renewable energies increases, many inverter‐based resources (IBRs) managing algorithms are proposed to control the impact of this penetration on the system. Some of these algorithms are based on a local control without visualizing the status of the whole network and others are based on a centralized control approach that results in dynamic settings without considering the mutual sensitivity among different IBRs. Hence, flexible management of IBRs to be simultaneously controlled either locally or remotely and satisfy technical and fair utilization at IBRs and systems levels becomes essential. The proposed flexible management algorithm suggested a dynamic setting that estimates the mutual sensitivity among IBRs at the system level and assures fairness among different owners. This mutual sensitivity link provides a quantifying measure of IBRs injected power on the status of the whole network. Furthermore, this measure defines dynamic clusters' boundaries that are continuously updated by including the most sensitive IBRs and updating their dynamic settings. This update takes place as system configuration, loads, and available sun‐power change in order to satisfy the flexible management algorithm without impacting fairness. The algorithm is applied on a sample DN and imposes compliance with system standard limits within a time‐frame of the stand‐alone LoRaWAN communication platform. [ABSTRACT FROM AUTHOR]

This paper presents the formulation of the simultaneous planning of distributed generations (DGs) and automatic distribution according to the goals of reliability, operation, and economy of the distribution system. The objective function aims at minimizing the total costs of construction, maintenance, and operation of distribution automation resources and devices, plus the cost of voltage deviations and expected energy not supplied in this paper. This scheme limits to AC optimal power flow, the planning constraints of DGs and distribution automation devices, and reliability equations. The mentioned scheme has an integer nonlinear optimization format. In the following, a linear approximation model is extracted for it to reach the unique response. Finally, by applying the proposed problem to the standard distribution grid by GAMS optimization software, the numerical results highlight the capability of the proposed scheme in improving the technical and economic conditions of the distribution network with optimal DGs and distribution automation planning. [ABSTRACT FROM AUTHOR]

This paper addresses integrated supply and distribution planning for a multiproduct, multisite reforestation value chain (RVC) in a make-to-order context. The aim is to develop a tactical planning tool to determine the quantities of seedling species and sizes to order from nurseries to satisfy reforestation site needs while minimizing purchase and distribution costs. A mixed-integer linear programming model was developed and applied to the publicly managed RVC in the province of Quebec, Canada. This tool was also used to analyze scenarios considering the three pillars of sustainability: environmental (climate risk management to bolster resilience), social (fair production allocation), and economic (supply and distribution costs) aspects. This strategic analysis highlights the trade-offs between purely economic and more sustainable scenarios and discusses strategic deployment opportunities to improve the RVC. Ultimately, it serves as a powerful tool for efficiently allocating yearly seedling supply, minimizing costs, and promoting sustainable practices in reforestation planning which provides valuable insights for RVC managers. [ABSTRACT FROM AUTHOR]

In recent years, the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex. Consequently, a large number of active distribution network reconfiguration techniques have emerged to reduce system losses, improve system safety, and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network. While scholars have previously reviewed these methods, they all have obvious shortcomings, such as a lack of systematic integration of methods, vague classification, lack of constructive suggestions for future study, etc. Therefore, this paper attempts to provide a comprehensive and profound review of 52 methods and applications of active distribution network reconfiguration through systematic method classification and enumeration. Specifically, these methods are classified into five categories, i.e., traditional methods, mathematical methods, meta-heuristic algorithms, machine learning methods, and hybrid methods. A thorough comparison of the various methods is also scored in terms of their practicality, complexity, number of switching actions, performance improvement, advantages, and disadvantages. Finally, four summaries and four future research prospects are presented. In summary, this paper aims to provide an up-to-date and well-rounded manual for subsequent researchers and scholars engaged in related fields. [ABSTRACT FROM AUTHOR]

Background: There has been no study in which the correlation between clinical results and dosimetry based on a 3D treatment planning system in patients with 198Au grains for head and neck cancer was evaluated. Methods: Thirty‐two patients who were treated with 198Au grains for head and neck cancer were reviewed. Twenty‐five patients were treated with brachytherapy alone and seven patients were treated with a combination of brachytherapy and neoadjuvant external beam radiation therapy. Results: With a median observation period of 60 months, the 5‐year local control rate was 82.9%. V85Gy of CTV in patients with local recurrence tended to be lower than that in patients without local recurrence (p = 0.07). The maximum dose of the keratinized gingiva in patients in whom bone exposure occurred was significantly higher than that in patients in whom bone exposure did not occur (p = 0.001). Conclusions: Dose distribution with 198Au grains can predict local control and late adverse events. [ABSTRACT FROM AUTHOR]

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry's low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning. [ABSTRACT FROM AUTHOR]

This article analyzes the service distribution imbalance within the Metropolitan District of Quito (DMQ) and its impact on urban mobility, aiming to propose strategies for more equitable territorial planning. The data were gathered from sources such as the National Institute of Statistics and Census (INEC), the Ministry of Health, the Ministry of Education, and OpenStreetMap. These data were integrated with GIS tools to model patterns of accessibility and mobility. Through a comprehensive approach, the study assessed education, banking services, employment, and healthcare, identifying how inequitable access to these services drives increased travel demand, especially in rural and peri-urban areas. In the education field, over 500 neighborhoods faced a shortage of institutions, compelling students to commute to other neighborhoods. For financial services, only 67% of neighborhoods had adequate access, with disparities across different socioeconomic zones. Additionally, employment-related mobility posed another challenge, with 88% of workers commuting outside their residential parish. Finally, access to healthcare was also unequal across the DMQ, particularly in peripheral areas where residents must travel long distances. In this context, it can be concluded that more efficient urban planning in the Metropolitan District of Quito (DMQ) is crucial to address imbalances in the distribution of services and enhance quality of life. Proposed strategies include establishing a land reserve, decentralizing services to underserved areas, integrating smart technologies, and promoting incentives for remote work, sustainable mobility, and public transport. These actions aim to foster greater territorial equity and accessibility. [ABSTRACT FROM AUTHOR]

Disruptions in the global food supply caused by the COVID-19 pandemic and geopolitical tensions have exposed significant vulnerabilities in food distribution systems. Designing resilient food supply chain (FSC) networks that have the flexibility to react swiftly to disruptions while ensuring fair and equitable access to quality food is vital. This study proposes a flexible multi-objective mixed-integer programme (FMOMIP) to jointly address resilience and equity during disruptions. The proposed FMOMIP aims to satisfy demand equitably while minimising food loss and overall costs in a multi-echelon FSC network. In the event of disruption, the equity weights, obtained through a multi-criteria decision-making (MCDM) framework, are applied to revise the distribution plan and minimise the inequity and food loss in the network. The proposed model is tested under various disruption scenarios, considering the case of the Targeted Public Distribution System in India during the COVID-19 pandemic. The findings suggest that the existing storage and distribution network may be altered during disruptions to ensure equitable access to food at all demand points. Additionally, the impact of equity and other objectives on multi-echelon FSCs varies depending on the kind of disruption experienced by the system. Hence, we propose that food distribution organisations and government agencies should consider designing FSC networks that can react with higher responsiveness and incorporate equity perspectives instead of adopting a purely cost-oriented approach. [ABSTRACT FROM AUTHOR]

The present study aimed to improve the dose distribution of radiotherapy planning for nasopharyngeal carcinoma (NPC) by comparing the effects of various minimum monitor units (MUs) per dynamic control point (MMCP) values on the quality and execution efficiency of dynamic intensity-modulated radiotherapy (IMRT) planning. Thirty-four clinically implemented dynamic IMRT plans for patients with NPC were retrospectively selected. In total, 170 plans were obtained by modifying only the MMCP values (set as 1, 3, 5, 7, and 9) in the treatment planning system's (TPS) optimization parameters. These plans were divided into 5 groups. Analyzing the effects of MMCP on the target and organ dose at risk (OAR), also the execution efficiency of the treatment plan in each group and using a quality score system, we conducted an objective quantitative study of the dose distribution and execution efficiency. The target dose evaluation indicators (target coverage (TC), homogeneity index (HI), and conformity index (CI)) of all IMRT plans showed a trend of variation with an increase in MMCP values, and the difference was statistically significant when MMCP values were 5, 7, 9, and 1 (p < 0.05). With an increase in MMCP, the dose to OAR slightly increased, but the difference was not statistically significant (p > 0.05). With an increase in MMCP, the average number of MUs per segment significantly increased (p < 0.01). The groups based on MMCP values of 1, 3, 5, 7, and 9 received quality score system of 1.188, 1.180, 1.171, 0.987, and 1.184, respectively, with the MMCP7 group achieving the lowest score, indicating that this plan had the highest overall quality. The MMCP value for dynamic IMRT planning in the Monaco TPS for patients with NPC should be set to 7 to achieve fewer segments, the best execution efficiency without significantly deteriorating the target and OAR dose. [ABSTRACT FROM AUTHOR]

The travel behavior of subway passengers has shown systematic differences with the onset and progression of the COVID-19 pandemic. This study focused on subway stations in Xi'an and systematically analyzed the spatiotemporal features of the average travel distance of passengers (ATDP) during the peri- and post-COVID-19 periods. Using the gradient boost regression tree analysis framework, the study explored the nonlinear relationship between various built environment factors and changes in ATDP during pandemic periods. The results indicated that the distance to the city center, land-use mix, and road network density contributed significantly more than other variables in different periods. Furthermore, the study found that the ATDP in subway stations does not vary significantly based on the structural characteristics of the station network. These findings hold significant value for operational organizers seeking to comprehend the station-level changes in travel distances under the influence of public health crises, offering effective scientific data support for the rational development of operational plans to balance the distribution of operational capacities. [ABSTRACT FROM AUTHOR]

The uncoordinated integration of numerous distributed resources poses significant challenges to the safe and stable operation of distribution networks. To address the uncertainties associated with the intermittent output of distributed power sources, we propose a multi-objective planning strategy for distribution networks based on distributionally robust model predictive control (MPC). Initially, an error fuzzy set is established on a Wasserstein sphere using historical data to enhance out-of-sample performance. Next, a multi-objective optimization framework is constructed, balancing returns and risks, and is subsequently converted into a single-objective solution using value-at-risk conditions. This is followed by the implementation of multi-step rolling optimization within the model predictive control framework. We have linearized the proposed model using the linearized power flow method and conducted a thorough validation on an enhanced IEEE 37-node test system. Distributionally robust optimization (DRO) has been shown to reduce costs by a significant 29.16% when compared to an RO method. Moreover, the energy storage capacity required has been notably reduced by 33.33% on the 29-node system and by 20% on the 35-node system. These quantified results not only demonstrate the substantial economic efficiency gains but also the enhanced robustness of our proposed planning under the uncertainties associated with renewable energy integration. [ABSTRACT FROM AUTHOR]

Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path planning. At the upper level of the bi-level programming model, k-means clustering analysis is used to obtain all accurate information about alternative locations for the front warehouse for site selection, thereby providing the corresponding foundation for the lower level algorithm. At the lower level of the model, a fusion algorithm of particle swarm optimization (PSO) and a genetic algorithm (GA) is used for solving. To accelerate the convergence speed of the population and lower the running time of the algorithm, the parameter values in the algorithm are determined adaptively. An adaptive hybrid algorithm combining the particle swarm optimization algorithm and the genetic algorithm (APSOGA) is used to reallocate the location information on backup points for the front-end warehouse, ultimately determining the facility location of the front-end warehouse and planning the end path from the front-end warehouse to the customer point, achieving joint optimization of the front-end warehouse's location and path. A comparative analysis of algorithm optimization shows that using the APSOGA hybrid algorithm can reduce the total cost of the logistics network by 14.57% compared to a traditional single-algorithm PSO solution and reduce it by 5.21% compared to using a single GA. This proves the effectiveness of the APSOGA hybrid algorithm in solving location and path planning problems for cold chain logistics distribution companies. [ABSTRACT FROM AUTHOR]

The flexibility of soft open point (SOP) in spatial power regulation enhances the distribution network's (DN) integration of large‐scale renewable energy sources. However, the high cost of SOP and its limited capability for temporal power regulation impede its widespread adoption. Given the rapid expansion of 5G base stations (BSs), utilizing their energy storage to participate in DN planning and operation optimization provides a promising solution. Therefore, this paper proposes an optimal planning method of SOP in DN, considering collaborations with 5G BSs. The objective is to enhance DN's power regulation in both temporal and spatial dimensions, while minimizing the investment cost of SOP and fully utilizing the unused capacity in base station energy storage (BSES). Firstly, the flexible regulation models of SOP and 5G BS are established, with the real‐time dispatchability of BSES formulated. Then, a bi‐level optimization model is proposed, where the planning layer aims to minimize the total cost, while the operational layer aims to decrease the average voltage deviation. Additionally, an improved Shapley value method based on interactive power is developed for benefit allocation, which enhances the engagement of 5G BSs to participate in DN regulation. The effectiveness of proposed method is validated by simulation results. [ABSTRACT FROM AUTHOR]

In the construction process of construction engineering, there exist a complex construction site layout and multiequipment collaborative work. If a reasonable construction plan cannot be made quickly and accurately, the investment in construction costs will be increased. Aiming at the construction material distribution, this study proposed a construction plan planning method under the multinode and multivehicle scene. Firstly, a mathematical model of the material distribution planning problem in the multinode multivehicle scene was established. The basic assumptions, objective functions, and constraints were proposed. The triangular scalar variables and 'unitization' processing methods were incorporated into the mathematical model. In order to solve the problem of material distribution planning, a heuristic neural network algorithm was designed. A 'gene-chromosome-individual' representation method and a 'two-step' calculation concept were defined. The specific implementation steps of the algorithm were given. Fully considering the type and quantity of vehicles and the length of the path, a method of establishing the mapping relationship between the construction scheme and the construction cost was proposed. The feasibility of the proposed theoretical method was verified by a cable truss structure model experiment. A calculation method with high fitting degree and fast running speed was established. According to the experiment model, the best construction scheme was formed. The results showed that the optimal construction scheme had a high vehicle loading rate and a clear distribution path, which effectively saved the construction cost under the premise of ensuring smooth construction. [ABSTRACT FROM AUTHOR]

This article has been retracted. A retraction notice can be found at . [ABSTRACT FROM AUTHOR]

This article addresses the distance protection challenges associated with the series‐compensated transmission lines and the impact of fault resistance by employing a machine‐learning model. In the proposed model, stacked layers of bidirectional long short‐term memory (Bi‐LSTM) cells are fed by voltage and current signals to distinguish between different fault scenarios. This method takes advantage of only local bus measurements to prevent information leakage in communication channels. Moreover, to make the proposed method harmonics‐robust and improve the correlation interpretation between the features for the Bi‐LSTM model, the 3‐phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. Then, an extensive amount of fault scenarios including different compensation levels, fault resistances, and fault locations in normal and power‐swing operational conditions are simulated to train the model. Finally, to validate the performance of the proposed protection method in the series‐compensated transmission lines, distinctive studies are also carried out based on electromagnetic transient simulations. The obtained results confirm the remarkable performance of the proposed method in discriminating fault types, faulty phases, internal or external faults, and normal or power‐swing conditions of the power system. [ABSTRACT FROM AUTHOR]

Product distribution in supply chain management has been hotly debated during the last decade. However, during COVID-19, many supply chains suffered from sudden changes in local market demands. Such changes cause a bullwhip effect throughout a supply chain, making it unable to respond rapidly. This research develops a new model for distributing products in the food chain using real urban and geographical data of blockchain technology. The aim is to re-adjust the product distribution plans by using a horizontal layer product distribution readjustment strategy while local markets confront sudden market changes. To address the problem, a heuristic was proposed and coded by Python based on the largest density-distance rule. Then, to evaluate the performance of the proposed method, the schedules are assessed with some metrics gathered in the literature. For this purpose, a Full Factorial design of experiments is generated by Python. Moreover, the outcomes are compared with those gained from short-traveling time and greedy loading-based heuristics. The results showed that using the horizontal layer product distribution readjustment strategy for modifying the initial schedules could prevent lost sales in all studied cases. Besides, by responding to sudden market demand changes rapidly, which subsequently prevents lost sales, more profits were gained in 58.3% of the studied cases. In addition, in 61.11% of studied cases, the proposed method was faster than other studied heuristics in terms of computational time. [ABSTRACT FROM AUTHOR]

The research reviews the design and planning impact of green spaces and their role in social interaction within the spaces of the Iraqi Hunting Club in Baghdad, which is characterized by its recreational, cultural, social, and sporting identity that targets all age groups. The research addresses the issue of lack of social interaction and its objective is to increase social communication, as well as focus on good design and planning for the distribution and scheduling of social recreational activities and services that encourage social interaction to form social networks that contribute to the development of society and enhance social cohesion. The research assumes that the elements of design and planning are the basis for supporting social interaction, and this was proven as there was full agreement with some questions in the researcher's questionnaire such as the integration of security and privacy systems gives a sense of confidence in place and others. Design and planning are the bases on which the work and success of other factors affecting social interaction such as the environmental factor and activities, events, and services factors are based. The methodology included using a Likert Scale as a statistical method, and the sample is club members. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Gênero e Interdisciplinaridade is the property of Instituto de Ensino e Pesquisa Periodicojs and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson's correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale. [ABSTRACT FROM AUTHOR]

The low-voltage distribution network (LVDN) is the final stage in delivering electric energy from power plants to consumers, and its operational condition greatly impacts many power users. While medium-voltage and high-voltage distribution networks can be managed through intelligent digital systems, load imbalance issues in LVDNs often rely on planners' experience, leading to significant limitations. With advancements in electric vehicle (EV) charging technology and vehicle-to-grid (V2G) technology, where EVs act as distributed energy storage units, bidirectional energy exchange between vehicles and the grid can now contribute to LVDN operation. This paper proposes a low-voltage load distribution planning method that integrates street information and V2G technology. A two-stage stochastic programming mixed-integer model is developed to tackle load imbalance in LVDNs, with the planning scheme derived from solving this model. A case study is presented to verify the effectiveness of the method, demonstrating that incorporating V2G technology enhances load distribution accuracy and reduces reliance on manual planning, improving network stability and operational efficiency. [ABSTRACT FROM AUTHOR]

Background: The use of insecticide-treated nets (ITNs) is a strategy recommended by the World Health Organization (WHO) for malaria prevention. In Nigeria, ITNs have been periodically distributed since 2007 through campaigns. Campaign activities and assets are typically tracked using either a paper-based or digital system. In 2017, a digital approach was introduced in Ondo state for tracking attendance at training sessions as part of the ITN campaign. Following the success of the 2017 introduction, subsequent campaigns planned to digitise other aspects of the campaign to improve accountability and efficiency of the ITN distribution. The COVID-19 pandemic posed additional challenges for the ITN distribution planned for 2021 and adaptations were made to the programme strategy to ensure the campaign could go ahead safely. This article presents lessons and experiences from the 2021 ITN distribution campaign in Ondo state, Nigeria. Methods: The campaign used RedRose, a customised mobile application, to monitor the planning and delivery of the campaign, collect household information including training personnel and tracking the transfer of ITNs between distribution hubs and households. ITNs were delivered through a single-phase door-to-door distribution strategy. Results: The campaign distributed 2,965,125 ITNs covering 1,057,577 households across Ondo state. The digital application was beneficial for monitoring the quality of implementation and tracking assets and staff to ensure safety.. The single-phase door-to-door approach was more convenient for households compared to fixed-point distribution but increased the workload for mobilization and distribution teams. Conclusions: Single phase door-to-door strategy using digital tools was an effective method to increase coverage of ITNs while closely tracking the progress of distribution campaigns. High-quality population data are needed to further improve the planning and implementation of ITN campaigns and other health interventions. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi‐level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power‐to‐gas (P2G) and gas‐to‐power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy‐sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14‐bus power system and a 10‐node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model. [ABSTRACT FROM AUTHOR]

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users' electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users' electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account. [ABSTRACT FROM AUTHOR]

In the Internet era, the e‐commerce industry has risen, its development scale continues to expand, cross‐border e‐commerce (CBEC) has also been born, and it is now in the stage of sustainable development. The rapid development of CBEC also needs the strong support of logistics, the two are inseparable, and today, the development scale of CBEC is constantly expanding. The existing e‐commerce logistics (ECL) model is also gradually unable to meet the increasingly diverse needs of users, and new logistics models need to be actively explored. To change this situation, this paper carried out a specific analysis of CBEC logistics model, and applied embedded technology to ECL, which also built a logistics tracking system. At the same time, combined with the ant colony algorithm, the paper carried out experimental research on the logistics package distribution route planning problem. From the experimental results, in terms of average delivery time, the algorithm's result was 25.95 hr, while the traditional algorithm was 32.53 hr; in terms of average distribution freight cost, the algorithm's result was 163.3 yuan, while the traditional algorithm was 257.7 yuan; in terms of average distribution cost, this algorithm's result was 131.53 yuan, while the traditional algorithm was 211.68 yuan. To sum up, this algorithm could effectively optimize the distribution route of logistics packages and improve the efficiency of package transportation. [ABSTRACT FROM AUTHOR]

Wind farm can obtain the maximize profit by optimizing micro‐locations and cables. The factors that affect profit include the power output of wind turbines, cost and et al., where power output is affected by wake effect, cable cost is related to the length and type of collector cable. The profit is calculated on the premise that the costs and power loss of collector cable are determined. Obviously, there is a hierarchical relationship between the above problems. Therefore, a bi‐level optimization model with constraints is constructed in this paper, where the upper‐level objective function is the maximum profit, and the lower‐level objective functions are consists of minimum the cable cost and the power loss of collector cable; Moreover, an improved algorithm (IDEDA), based on differential evolution and Dijkstra, is used to optimize above model; Finally, simulation experiments are carried out for IDEDA and four algorithms for two different wind conditions, and the results show that IDEDA performs better compared to the other four algorithms in terms of profit and cable cost. [ABSTRACT FROM AUTHOR]

This study employs the MATSim agent-based simulation model to analyse bike-sharing station locations by examining dynamic trip flows and individual behavioural changes. It explores demand on a microscopic scale, capturing the behaviour of multi-segment trips. A protocol was established to evaluate different station configurations' impact on profitability and demand. Findings suggest that optimising the station count from 219 to 66 strategic locations can significantly enhance both revenue and operational efficiency. The simulation produces data on the number of users arriving and departing from each station in different configurations, indicating the size of each station. This data allows stations to be classified into three types: generator, attractor, and interchange; displaying their changes across different configurations. This quantification offers operators insights for predicting bike distribution and planning operational strategies. Considering spatial and built environment factors, the findings underscore the potential of bike-sharing stations to evolve into mobility hubs, offering valuable insights for policymakers. [ABSTRACT FROM AUTHOR]

Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the effect of Hounsfield units (HU)-relative electron density (RED) calibration curves obtained with devices from three different Computed Tomography (CT) manufacturers on dose distribution in Accuray Precision planning of patients with lung cancer. Methods: All CT data required for treatment planning system (TPS) were obtained using the Tomotherapy "cheese" phantom. HU RED calibration curves were created with images obtained from Siemens Somatom, GE Optima, and Toshiba Aquilion devices. The obtained calibration curve was extrapolated. CT images of lung cancer patients were acquired on a single device and treatment plans were created. The existing plans were recalculated using three calibration curves and the effect of the HU RED calibration curve on dose distribution was analyzed. Results: The results showed that different CTs did not produce significant dose differences in organ doses and PTV for Accuray TPS. Conclusions: Based on clinical judgment, images from different CT devices can be used in treatment planning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Previous plan competitions have largely focused on dose metric assessments. However, whether the submitted plans were realistic and reasonable from a quality assurance (QA) perspective remains unclear. This study aimed to investigate the relationship between aperture-based plan complexity metrics (PCM) in volumetric modulated arc therapy (VMAT) competition plans and clinical treatment plans verified through patient-specific QA (PSQA). In addition, the association of PCMs with plan quality was examined. A head and neck (HN) plan competition was held for Japanese institutions from June 2019 to July 2019, in which 210 competition plans were submitted. Dose distribution quality was quantified based on dose-volume histogram (DVH) metrics by calculating the dose distribution plan score (DDPS). Differences in PCMs between the two VMAT treatment plan groups (HN plan competitions held in Japan and clinically accepted HN VMAT plans through PSQA) were investigated. The mean (± standard deviation) DDPS for the 98 HN competition plans was 158.5 ± 20.6 (maximum DDPS: 200). DDPS showed a weak correlation with PCMs with a maximum r of 0.45 for monitor unit (MU) ; its correlation with some PCMs was "very weak." Significant differences were found in some PCMs between plans with the highest 20% DDPSs and the remaining plans. The clinical VMAT and competition plans revealed similar distributions for some PCMs. Deviations in PCMs for the two groups were comparable, indicating considerable variability among planners regarding planning skills. The plan complexity for HN VMAT competition plans increased for high-quality plans, as shown by the dose distribution. Direct comparison of PCMs between competition plans and clinically accepted plans showed that the submitted HN VMAT competition plans were realistic and reasonable from the QA perspective. This evaluation may provide a set of criteria for evaluating plan quality in plan competitions. [ABSTRACT FROM AUTHOR]

This study proposes a production hub location (PHL) problem that integrates the classical multiplant lot-sizing and hub location problems. The PHL problem is to determine the location of production facilities, lot-sizing, inventory, hub location, and the distribution of multiple commodities from plants to customers, with an objective to minimize the total production, setup, inventory, hub operating, and transportation costs. The PHL problem applies to manufacturing companies that either have built a hub-and-spoke distribution network or are accessible to such a network through collaborations with other third-party logistics companies. Because the PHL problem is NP -hard, we propose an exact method that integrates dynamic programming and Benders decomposition (DPBD) for solving the problem. The DPBD method is enhanced by exploring several problem properties, such as a multicut reformulation, the generation of Pareto-optimal cuts, a two-stage hub elimination and restoration procedure, and the inclusion of a novel heuristic procedure. We compare the PHL model with several related models theoretically and computationally with newly created benchmark instances. The computational results indicate that the proposed model can reduce the total costs and facilitate better network designs and system decisions, highlighting the value of an integrated approach. We also provide managerial insights into the benefits of integration and show the efficiency of the DPBD method through an extensive number of computational tests. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2023.0339. [ABSTRACT FROM AUTHOR]

Multi-unmanned aerial vehicle (UAV) collaborative task planning and distribution path planning are the core content of agricultural UAV logistics distribution. In this study, the multi-UAV collaborative task planning and the distribution path planning were discussed, and such constraint conditions as UAV load capacity, battery capacity and flight time were comprehensively considered, aiming to reduce the number of UAVs and their power consumption. To ensure the safe and efficient completion of multi-UAV logistics distribution tasks, 3D agricultural ultralow space was subjected to environment modeling, and a bilevel planning model for collaborative planning of UAV distribution route and flight path was constructed. Then, an improved particle swarm optimization (PSO) algorithm with the improved learning factor and inertia coefficient was designed on the basis of PSO framework, and the global optimal solution in the current iteration was improved using variable neighborhood descent search. The feasibility of the proposed algorithm was verified by analyzing a practical case. With the central city area of XX City as the study area, 1 logistics & freight transportation center was taken as the central warehouse (coordinates: 50, 50, unit: km) and 50 intelligent express cabinets as the express cabinets of UAVs. The obtained results were comparatively analyzed with those acquired through the basic PSO algorithm. The results manifest that the proposed algorithm performs better than the compared algorithms. The improved PSO algorithm is superior to the basic PSO algorithm in aspects of total UAV flight distance, number of UAVs used and algorithm convergence time, indicating that the model and algorithm established in this study are feasible and effective. [ABSTRACT FROM AUTHOR]

Power distribution networks should strive for reliable delivery of energy. In this paper, we support this endeavor by addressing the Maintenance Resources Allocation Problem (MRAP). This problem consists of scheduling preventive maintenance plans on the equipment of distribution networks for a planning horizon, seeking the best trade-offs between system reliability and maintenance budgets. We propose a novel integer linear programming (ILP) formulation to effectively model and solve the MRAP for a single distribution network. The formulation also enables flexibility to suit new developments, such as different reliability metrics and smart-grid innovations. Then we develop a straightforward ILP formulation to address the MRAP for several distribution networks which takes the advantages of exchanging maintenance information between local agents and upper management. Using a general-purpose ILP solver, we performed computational experiments to assess the performance of the proposed approaches. Optimal maintenance trade-offs were achieved with the new formulations for real-scale distribution networks within short running times. To the best of our knowledge, this is the first time that the MRAP is optimally solved using ILP, for single or multiple distribution networks. [ABSTRACT FROM AUTHOR]