Schedule Coordination Research Articles

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

AbstractUncertain activity durations and multi‐skilled staff allocation are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized resource‐constrained multiproject scheduling problem with uncertain activity durations and multi‐skilled staff allocation. The robust project scheduling is employed to tackle uncertainty. We name it the robust decentralized resource‐constrained multiproject scheduling problem with multi‐skilled staff (RDMPSP‐MS). To formulate this problem, a two‐layer model containing local scheduling and global coordination decision‐making is established based on the multiagent system. To solve this model, a two‐layer approach (TLA) is developed. In the TLA, a starting time criticality heuristic method is adopted to handle the local scheduling problem, and a global resource allocation heuristic algorithm is designed to allocate multi‐skilled staff. The computational experiments are conducted on the adapted Multi‐Project Scheduling Problem LIBrary (MPSPLIB) data set. The results show that the proposed TLA outperforms seven heuristic algorithms based on priority rules regarding solution quality and efficiency on most problem subsets. It is further verified that the robustness of the baseline schedule deteriorates with the increase in problem size and activity duration variability level. Additionally, the results of this research provide practical guidance for managers who expect to determine reasonable project deadlines in a decentralized multiproject environment.

The Influence of Supply Chain Financing on Enterprise Operation Efficiency and Competitiveness

Supply chain financing has become a vital tool for businesses to enhance their operational efficiency and competitiveness in today's dynamic market environment. This research aims to explore the impact of supply chain financing on business efficiency and competitiveness. The study examines how supply chain financing can improve cash flow management, optimize inventory management, and foster collaboration among supply chain partners. By providing timely access to working capital, supply chain financing helps businesses optimize their cash flow, reduce receivables collection time, and minimize the need for costly short-term borrowing. This enhances financial stability and enables companies to meet their financial obligations promptly. Furthermore, supply chain financing offers flexible financing options, such as inventory financing and vendor-managed inventory programs, which enable companies to optimize inventory levels, reduce stockouts, and lower holding costs. This allows businesses to respond quickly to market demands, reduce lead times, and improve overall supply chain efficiency.In addition, supply chain financing platforms facilitate the sharing of financial information and streamline payment processes, enhancing transparency and trust among supply chain partners. This fosters better coordination of production and delivery schedules, reduces supply chain disruptions, and enhances the overall reliability and efficiency of the supply chain.The research methodology includes a comprehensive review of existing literature, case studies, and interviews with industry experts to gather empirical evidence on the impact of supply chain financing on business efficiency and competitiveness. The findings of this research will contribute to a better understanding of the benefits and challenges of supply chain financing and provide insights for businesses seeking to enhance their operational efficiency and competitiveness. Overall, the research highlights the significant role of supply chain financing in improving business efficiency and competitiveness. By leveraging supply chain financing, companies can optimize cash flow, streamline inventory management, and foster collaboration among supply chain partners. This, in turn, enables businesses to respond effectively to market dynamics, gain a competitive edge, and achieve sustainable growth in today's rapidly changing business landscape.

Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers

Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.

Role of Administrative Department in Education with reference to Schools and Universities

The administrative department in educational institutions serves as a crucial pillar, offering indispensable support and guidance to students, faculty, and staff. This multifaceted department shoulders a diverse array of responsibilities, spanning from the intricacies of admissions management to the meticulous upkeep of student records. Coordination of academic schedules and vigilant oversight of financial operations further accentuate the pivotal role played by this administrative backbone. This paper delves into the pivotal functions of the administrative department in education schools and universities, elucidating its intricate responsibilities and the seamless orchestration required for the institution's overall efficacy. Effective communication stands out as a linchpin in the administrative department's modus operandi, ensuring the dissemination of pertinent information and fostering collaboration among different units. The department's organizational prowess becomes evident in its adept management of academic schedules, harmonizing diverse activities to facilitate an optimal learning environment. Moreover, the administrative department's financial acumen is spotlighted through its meticulous oversight of financial operations, guaranteeing fiscal responsibility and sustainability. This study underscores the integral nature of the administrative department, emphasizing its role in the smooth functioning and efficiency of educational institutions. By delving into the nuances of its responsibilities and operational strategies, this paper contributes to a comprehensive understanding of the administrative dynamics crucial for the success of education schools and universities.

Research on Dispatching Coordination Control Method of Distribution Network with Photovoltaic Power Generation

The coordination control of photovoltaic power distribution networks faces challenges such as inaccurate load power prediction and long coordination times. To address these issues, this paper proposes a scheduling coordination control method for photovoltaic power distribution networks. The method involves analyzing the structure of the photovoltaic power generation system, obtaining the operation state of the network using disturbance observation methods, building a dispatching coordination control model, designing constraints for dispatching coordination control, building a power balance function, and solving the total demand power of the load. An algorithm flow is designed to optimize the coordination control of photovoltaic distribution networks. Experimental results demonstrate that the accuracy of photovoltaic unit power prediction using this method reaches 98%, with power grid dispatching and coordination control times always less than 3.8 seconds. This approach effectively improves the effectiveness of distribution network dispatching and coordination control.

Optimal scheduling of vessels passing a waterway bottleneck

We develop a novel schedule optimization model for vessels passing a waterway bottleneck. From the system-optimal perspective, the model aims to minimize the total vessel bunker cost and delay penalties at destinations by incorporating the nonlinear relationship between bunker consumption and sailing speed into its calculations. The nonlinear model is linearized via two commonly used approximation techniques. The first one linearizes the bunker consumption function using a piecewise linear lower bound, while the second does so by discretizing the time. Numerical case studies are conducted for a real-world waterway bottleneck, the Three Gorges Dam Lock. Results reveal how the optimal cost components, vessel schedules, and delays are affected by key operating parameters, including the fuel prices, delay penalty rates, and the tightness of sailing time windows. Comparison against two simpler benchmark scheduling strategies (one with no vessel coordination and the other adopting a naïve coordination) manifests the sizeable benefit of optimal vessel scheduling.This paper presents the first investigation into the system-optimal scheduling strategy for vessels navigating a shared bottleneck, considering bunker costs, schedule delay penalties, and varying sailing speeds. The results highlight the significant potential of system-optimal scheduling and potential coordination strategies that enable approximation of the system-optimal solution. Additionally, our numerical experiments uncover the limitations of the outer-approximation method, while demonstrating that the discrete-time approach surpasses it in terms of both solution quality and computational efficiency.

Impact of Wind on eVTOL Operations and Implications for Vertiport Airside Traffic Flows: A Case Study of Hamburg and Munich

This study examines the impact of wind/gust speed conditions on airside traffic flows at vertiports in the context of on-demand urban air mobility based on the Vertidrome Airside Level of Service Framework. A wind-dependent operational concept introducing four wind speed categories with corresponding wind-dependent separation values is developed and applied in simulation. A decade (2011–2020) of historical METAR wind/gust speed reports are analyzed for a potential vertiport location at Hamburg and Munich airport, and a representative year of wind speed data is selected for each location as simulation input. Both locations experience performance degradation during the first quarter of the simulated year, which contains over 50% of the annual flight cancellations, and exceed wind-operating conditions, especially during midday and early afternoon hours. This study discusses the importance of wind-dependent coordination of flight schedules and analyzes the challenge of determining appropriate wind speed category thresholds. Lower thresholds result in an increased frequency of operationally unfavorable wind/gust conditions. Additional sensitivity analyses are performed to study the effects of wind-dependent separation deltas and wind-(in)dependent scheduling approaches. In conclusion, the presented approach enables planners and operators to make informed decisions about vertiport traffic flow characteristics and performance, vertiport location, and business cases.

Spatio-temporal characteristics and coupling coordination relationship between urbanization and atmospheric particulate pollutants in the Bohai Rim in China

The coordinated development of urbanization and atmospheric environment is of great significance for improving the quality of urbanization and solving air pollution problems in the Bohai Rim region. We have constructed urbanization quality level system indicator system and atmospheric environment system indicator system. By combining the coupling coordination degree model and spatial autocorrelation method, we analyze the spatiotemporal changes of urbanization, PM2.5, and PM10 of 43 cities in the Bohai Rim region from 2015 to 2020 using the coupling coordination model and spatial autocorrelation method, revealing the spatiotemporal evolution law of the coupling coordination relationship between regional urbanization system and atmospheric environment system. The results show that: (1) The overall urbanization level is increasing and the concentration of atmospheric particulate matter pollution is decreasing from 2015 to 2020 in the Bohai Rim; the urbanization level shows a spatial distribution pattern of high in the northeast and low in the southwest, and the concentrations of PM2.5 and PM10 show a pattern of high in the southwest and low in the northeast, with obvious clustering characteristics; (2) The coupling and coordination relationship between urbanization and atmospheric environment is showing an increasing trend in the Bohai Rim region. During the research period, the lowest, highest, average, and coupling coordination levels of the coupling and coordination scheduling are continuously increasing. The spatial distribution pattern of the coupling coordination degree is low in the southwest and high in the northeast, and there is a strong regional convergence of the coupling coordination type transformation, with certain spatial and temporal dependence.

ADAPTIVE CONTROL ELECTRICITY CONSUMPTION IN LOCAL MICROENERGY SYSTEM WITH POLYGENERATION BASED ON CLUSTERING OF A CONDITIONAL DYNAMIC TARIFF

The paper is devoted the increasing the efficiency to use of traditional and renewable sources in microenergy systems. The development of combined power supply systems of local objects with the stochastic nature generation of renewable sources has led to a number of fundamental problems.
 It is associated with the lack of a decision-making system regarding the ratio of power sources of electricity and the coordination of their generation levels with the electricity consumption schedule at the stage of designing such microgrids. The development of microenergy systems with polygeneration is conditioned by the possibility of technical and information support for the formation of the energy balance of local objects to minimize the costs of their electricity supply.
 By applying elements of inductive system-analytical technologies, an approach to solving the problem of modeling dynamic scenarios of energy management in local microgrids with polygeneration based on determining the cost of electricity and adaptive coordination of the electricity consumption schedule in real time is proposed.
 On the basis of statistical data, modeling was carried out and daily graphs of changes in the current cost of electricity with half-hour discreteness of the calculation were obtained, and the clustering procedure of the conditional dynamic tariff was showed.
 The results can be applied in the evaluation of electricity pricing in microgrid energy management systems or the development of control systems for Smart grids with renewable sources.

Coordinated restoration method of transmission and distribution network considering distribution network fault repair scenario

With the development of active distribution network (ADN) technology, coordinated restoration of transmission network (TN) and ADNs has received widespread attention. As an important part of power system resilience, power system restoration should consider the influence of low-probability high-risk events. Such extreme events are often accompanied by component damage, so the coordination of restoration process and repair scheduling needs to be considered. First, the handling of fault repair scenario on TN and ADNs side during black-start restoration (BSR) is discussed. The interaction between the coordinated restoration of TN/ADNs and the fault repair schemes of ADNs is analyzed. Next, the coordinated restoration of TN/ADNs considering the ADN fault repair scenario is modelled in this paper. For the restoration model of TN, the restoration logic relationship of plants/lines, the subsystem division, frequency constraints of the restored system and the power scheduling constraints of source/load are considered. For the restoration model of ADNs, the restoration logic relationship of DGs/lines/buses, the scheduling logic of repair crews and emergency power sources, frequency constraints and the power scheduling constraints of source/load are considered. Then, an analytical target cascading algorithm is used to solve the optimization model. Finally, a TN/ADNs system is constructed by an IEEE_30 bus transmission system and two IEEE_33 bus distribution system, the numerical case study on the TN/ADNs system verifies that the distributed collaborative optimization of the TN/ADNs restoration plan and the fault repair plan of ADNs can improve the overall restoration efficiency during BSR.

Multi-objective coordinative scheduling of system with wind power considering the regulating characteristics of energy-intensive load

Energy-intensive load (EIL) has excellent regulating potential in the utilization of wind power: it not only offers high flexibility and large power capacity but also features significant price sensitivity. Without considering the production tasks and the regulating characteristic of energy-intensive load, the current scheduling approach is difficult to implement. A novel multi-objective coordinative scheduling approach is proposed in this paper for wind-power utilization considering the regulating models of energy-intensive load to guarantee the benefits of both power system and energy-intensive load, which can greatly improve the willingness of energy-intensive load to participate in scheduling. Firstly, A general model of regulating characteristics for energy-intensive load is given based on production process. Next, based on this modeling, a coordinative scheduling model is established with three layer. Layer A determines the total tasks of load side and generating side with the goal of minimizing the wind curtailment. Layer B and layer C make decomposition optimization to thermal power units and load control units with the goal of minimizing the generation cost and load-adjustable cost, respectively. Finally, a case study to verify the modeling is presented. The study shows that the proposed method can significantly improve the utilization of wind power at lower cost on the basis of ensuring the production requirements and benefits of energy-intensive load.

Pulsed Power Load Coordination in Mission- and Time-critical Cyber-physical Systems

Many mission- and time-critical cyber-physical systems deploy an isolated power system for their power supply. Under extreme conditions, the power system must process critical missions by maximizing the Pulsed Power Load (PPL) utility while maintaining the normal loads in the cyber-physical system. Optimal operation requires careful coordination of PPL deployment and power supply processes. In this work, we formulate the coordination problem for maximizing PPL utility under available resources, capacity, and demand constraints. The coordination problem has two scenarios for different use cases, fixed and general normal loads. We develop an exact pseudo-polynomial time dynamic programming algorithm for each scenario with a proven guarantee to produce an optimal coordination schedule. The performance of the algorithms is also experimentally evaluated, and the results agree with our theoretical analysis, showing the practicality of the solutions.

Bi-layer game method for scheduling of virtual power plant with multiple regional integrated energy systems

Regional integrated energy system (RIES) can be connected with the virtual power plant (VPP) through power tie line, and participate in the VPP demand response dispatch. In order to improve the benefit of VPP and RIES, a bi-layer game method for scheduling of VPP with multiple RIES is proposed. The upper layer is the master-slave game between VPP and RIES-coalition. VPP guides RIES-coalition to formulate purchase and sale power quantity strategy, in response to the power demand scheduling of VPP by the power purchase and sale price incentive policy. The lower layer is cooperative game among RIES-coalition members. Within the RIES-coalition, a multiple energy optimization and coordination scheduling strategy was established to meet the “electrical/ heating/ gas” load demand of each RIES, and cooperation benefits are shared among members based on Shapley value method. CIQPSO algorithm is used to solve the Nash equilibrium point of the game model, namely, the optimal power price strategy of VPP and the optimal purchase and sale power quantity strategy of each RIES. The results of example show that the proposed strategy can improve the peak shaving and valley filling effect of VPP, maximize the benefit of RIES-coalition, and ensure the reliable operation of VPP.

A Distributed Dual-Optimization Framework for Ancillary-Service Coordination Between MV Microgrids and LV Distribution Networks

This article aims to develop an efficient distributed real-time strategy for internal device scheduling and ancillary service coordination in the interconnected system of a medium voltage microgrid and a low voltage distribution network (LVDN). A distributed agent-based dual engine is proposed for coordinated ancillary service optimization, thereby reducing communication and computational burden. The strategy enables the microgrid operator and the LVDN aggregator, which are selfish entities, to perform ancillary service power exchanges by coordinating for both the active/reactive power as well as the compensation rates. The proposed strategy is numerically validated on a 30-node microgrid-LVDN test system consisting of solar PVs, conventional and renewable distributed generators, loads, community battery, and electric vehicles (EVs). This is shown to be beneficial than simply tracking a power profile by the LVDN or considering fixed compensation rates. Microgrid reduces its total operating cost significantly by approximately 31.78%, while maintaining the voltage and state of charges of EVs and community battery within the operation limit. Further, ancillary service coordination for residential and commercial LVDNs with a car park is analyzed, and the study is extended to consider fast charging of EVs with other related constraints.

A Directional TDMA Protocol for High Throughput URLLC in mmWave Vehicular Networks

Future vehicular wireless applications, such as autonomous and remote driving, will demand High Throughput Ultra-Reliable Low Latency Communication (HT-URLLC) unsupported by current wireless standards. Hybrid network designs that combine Millimeter-Wave (mmWave) and Sub-6GHz communications were recently proposed to establish HTURLLC in Vehicle-To-Everything (V2X) networks. To enable such integration, new Medium Access Control (MAC) protocols must be developed. As such, we consider a linear vehicular network, in which vehicles driving closely in the same lane utilize both front and rear directional mmWave antennas to form a Lane Cluster (LC). Data packets, then, propagate forwards and backwards along the LC towards its terminals in a multihop fashion. In turn, the LC terminals utilize omnidirectional antennas at Sub-6GHz frequencies to disseminate the cluster's packets to nearby LCs, Road Side Units (RSUs), and cellular base stations. To resolve the channel contention between subsequent LC members, we devised a two-timeslot MAC scheme with a schedule alignment mechanism. Additionally, we developed centralized and distributed schedule coordination protocols aligning the schedules of adjacent LCs to mitigate cross-lane interference due to antenna beam misalignment. The proposed protocols were evaluated using VEINS (extension of OMNET++ network simulator) and compared against the CSMA-CA scheme of IEEE 802.11p under different parameters and scenarios. The results show that the proposed linear topology along with the scheduling protocols can potentially achieve HT-URLLC while addressing the mmWave practicality issues.

ENERGY EFFICIENCY OF PUBLIC TRANSPORT FOR SUSTAINABLE URBAN MOBILITY

The article discusses the main problems associated with energy efficiency in creating a sustainable mobility system in cities. Urban public transport occupies a special place in meeting the needs of the population to move around the city. Solutions are considered that can help save energy resources, improve the ecological state and speed up the delivery of city residents. The possible effects of the use of alternative types of public transport and vehicles with an alternative source of energy, the integration of the mode of operation of various types of transport, the coordination of traffic schedules are analyzed.

A Two-Stage Algorithm Based on 12 Priority Rules for the Stochastic Distributed Resource-Constrained Multi-Project Scheduling Problem With Multi-Skilled Staff

In practical multi-skilled resource-constrained multi-project management, the activity duration is often affected by some factors (e.g., rework, increased workload), leading to uncertainty. Moreover, multiple projects are often managed under a distributed decision-making environment. To deal with uncertain activity durations in distributed multi-project management with multi-skilled staff, this paper studies a stochastic distributed resource-constrained multi-project scheduling problem with multi-skilled staff (MS-SDRCMPSP). In a distributed decision-making environment, a two-stage model with local scheduling and global coordination stages is established to describe MS-SDRCMPSP. A two-stage algorithm with 12 priority rules (TSA-12PRs) is proposed, these 12 priority rules are composed of 4 activity priority rules and 3 resource priority rules. In the local schedule stage, 4 activity priority rules (PRs) are applied to obtain the local schedule plan. In the global coordination phase, we develop 3 resource PRs based on variable neighborhood search (VNS), of which VNS is used to solve the execution order of conflicting projects, and 3 resource PRs are developed to formulate multi-skilled resource assignment strategies. Based on the multi-skilled instances adapted from benchmark instances, we evaluate the performance of the 12 PRs on different instances. The experiment results show that two PRs among 12 PRs perform better than other PRs in all-size instances. Comparing the two-stage algorithm with better two PRs with other approaches in literatures, we find that our method performs better than other approaches, especially in large-size instances. In addition, further experiments show that our method is more conducive to shortening the CPU runtime on distributed problems than centralized methods.

Resource coordination scheduling optimisation of logistics information sharing platform considering decision response and competition

The varying types and quantities of shared resources and fluctuating demands over time in the logistics information platform (LIP) often bring unbalanced resource competition, resulting in lower response demander success rates as well as lower platform resource utilisation rates, and weakening the LIP values. To address the problem of unbalanced resource competition in LIPs, a resource coordination scheduling optimisation method comprising a two-stage model is proposed. As the inherent reason for resource competition is decision time centralisation of each demander, the first-stage model decentralises the decision time points by optimising the LIP decision crowding degree and scheduling risk of an LIP transaction system. Subsequently, considering the scheduled time points obtained in the first stage as input, based on resource distribution changes that affect the comprehensive benefit of the demander's decision making, the second-stage model is proposed. The purpose is to optimise the resource distribution for ensuring that demanders achieve the highest decision benefits at the scheduled time points. Additionally, an entire dynamic scheduling strategy is designed based on the actual situation of vehicle-cargo matching. Particle swarm optimisation and a genetic algorithm are adopted to solve the models. An application example demonstrates that the proposed method and model are both valid and efficient. The proposed model can improve the demander response success and platform resource utilisation rates, solve the platform resource competition problem at a lower cost compared with price incentives, and contribute to new methods for scheduling and controlling shared resources. This can help improve the functionality and promote the healthy operation of the platform.

A simulation-based metro train scheduling optimization incorporating multimodal coordination and flexible routing plans

The promotion of multimodal coordination is of great significance to the joint development of diversified urban transportation systems. Focusing on an urban comprehensive passenger transport hub (UCPTH) that converges both inter-city modes and multiple inner-city modes, this study aims to customize a bridging metro train operation plan with the utilization of multimodal passenger simulation. A bi-objective integer nonlinear programming model (INLP) incorporating train schedule and flexible routing plan is proposed to minimize passenger waiting time and operating costs simultaneously. Notably, a real-time flow prediction method based on Monte Carlo simulation and queuing network system is proposed and embedded in the optimization model framework to reflect the difference of individual passengers’ transfer walking time and thereby provide detailed passenger distribution on the platform for metro hub stations connecting UCPTH. Compared with the estimation using historical data, this method can particularly support the scheduling coordination of metro and other modes during an abrupt change of arrival flows. Moreover, to solve the collaborative optimization model tractably, the ε-constraint method based on a tailored variable neighborhood search algorithm is developed to generate approximate Pareto optimal solutions, including the number of effective trains, demand-sensitive headway, the route pattern of each effective train, and train dwell time. Finally, two sets of numerical experiments, including a small-scale case and a real-world hub-metro network instance are conducted to demonstrate the effectiveness of the proposed approaches. The study associated with the findings can benefit both metro providers and users especially those who need to transfer to the metro system at hubs by achieving the best match between passenger and train flows in temporal and spatial domains.

Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks

With the rapid development of science and technology and the improvement of people’s living standards, vehicles have gradually become the main means of travel. The increase in vehicles has also brought about an increasing incidence of car accidents. In order to reduce traffic accidents, many researchers have proposed the use of vehicular networks to quickly transmit information. As long as these vehicles can receive information from other vehicles or buildings nearby in a timely manner, they can avoid accidents. In vehicular networks, the traditional double connection technique, through interference coordination scheduling strategy based on graph theory, can ensure the fairness of vehicles and obtain suitable neighborhood interference resistance with limited computing resources. However, when a base station transmits data to the vehicular user, the nearby base station and the vehicular network user may be in a state of suspended communication. Thus, the resource utilization of the above double connection vehicular network is not sufficient, resulting in a waste of resources. To solve this issue, this paper presents a study based on earnings learning with a vehicular network multi-point collaborative transmission mechanism, in which the vehicular network users communicate with the surrounding collaborative transmission. We use the Q-learning algorithm in the reinforcement learning process to enable vehicular network users to learn from each other and make cooperative decisions in different environments. In reinforcement learning, the agent makes a decision and changes the state of the environment. Then, the environment feeds back the benefit to the agent through the related algorithm so that the agent gradually learns the optimal decision. Simulation results demonstrate the superiority of our proposed approach with the revenue machine learning model compared with the benchmark schemes.