Collaborative Scheduling Research Articles

Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.

DECCo-A Dynamic Task Scheduling Framework for Heterogeneous Drone Edge Cluster

The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor–Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC.

CCSO: a dynamic collaborative scheduling scheme for wireless rechargeable sensor networks with obstacles

CCSO: a dynamic collaborative scheduling scheme for wireless rechargeable sensor networks with obstacles

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

Cloud-Edge Collaborative Scheduling with a Focus on Clean Energy

With the promotion of the national "double carbon" goal, the power system is developing towards the direction of low-carbon transformation. In order to achieve the goal of "striving to peak carbon dioxide emissions by 2030 and striving to achieve carbon neutrality by 2060", we must actively promote the consumption of a high proportion of renewable energy from the grid, which is the most urgent issue to be addressed. In this chapter, access to clean energy will involve multiple aspects such as source, network, load and storage. However, due to the intermittent, random and volatile nature of wind power and photovoltaic power generation, the challenges faced by significant users of the power system are enormous. Traditional energy Internet dispatching adopts centralized dispatching. In this paper, by deploying edge nodes, the autonomous decision-making and autonomous collaboration of power grid nodes at all levels are enhanced, and the collaborative integration level of source, network, load and storage of smart power grid is improved. Aimed at the current power supply and demand imbalance, new energy access problems and energy storage problems. In this paper, a service adaptation algorithm based on dynamic priority is proposed based on the scenario of load storage integration of source network under renewable energy access. Experimental results show that, compared with other algorithms, this algorithm has lower scheduling time and execution time and better performance under the condition of ensuring the highest clean energy consumption rate and first-order load priority response.

Wounded Triage, Transport and Coordination of Emergency Operation in Urban Emergencies

For the wounded triage, transport and cooperative scheduling problem of emergency surgery in urban emergency rescue, this study uses the idea of supply chain collaborative scheduling, considers factors such as the number of the wounded, rescue vehicle capacity and hospital operation time to achieve the optimization goals of the shortest rescue response time and the most economical transportation capacity, establishes a mixed integer programming model, and designs a two-stage scheduling algorithm to solve the model. It uses the relative gap between the maximum time span of the entire rescue process and the optimal performance under ideal conditions to measure the performance of the algorithm. The simulation experiments show that the two-stage scheduling algorithm has better problem-solving ability for scenarios with larger number of the wounded and stronger carrying capacity, and has better performance than MFF algorithm and MBF algorithm.

The Integrated Scheduling Optimization for Container Handling by Using Driverless Electric Truck in Automated Container Terminal

With the increasing maturity of automatic driving technology, the commercial value of driverless container trucks has been gradually excavated. Compared with social roads, the internal roads in the port area have certain practicing advantages. By taking into account the operational characteristics of the driverless electric container truck and the coordination of quay and yard cranes, this paper aims to explore the configuration and optimized scheduling model of the driverless electric container truck with the objective of minimizing overall energy consumption. The results show that the optimized allocation and scheduling of driverless electric trucks can minimize the total energy consumption of terminal operation without delaying the shipping schedule, and has obvious advantages over traditional manual driving diesel trucks and Automated Guided Vehicles in terms of operation efficiency, economy, and sociality. The results can also provide certain decision-making reference for the selection of horizontal transportation equipment and collaborative scheduling of multi-type loading and unloading equipment resources of container terminal operators.

A Refined Collaborative Scheduling Method for Multi-Equipment at U-Shaped Automated Container Terminals Based on Rail Crane Process Optimization

A U-shaped automated container terminal (ACT) has been proposed for the first time globally and has been adopted to construct the Beibu Gulf Port ACT. In this ACT layout, the double cantilevered rail crane (DCRC) simultaneously provides loading and unloading services for the external container trucks (ECTs) and the automatic guided vehicles (AGVs) entering the yard. The DCRC has a complex scheduling coupling relationship with the AGV and the ECT, and its mathematical model is extremely complex. There is an urgent need to study a practical collaborative scheduling optimization model and algorithm for the DCRC, the AGV, and the ECT. In this paper, we optimize the process flow of DCRCs to study the refined collaborative scheduling model of DCRCs, AGVs and ECTs in U-shaped ACTs. Firstly, we analyze the operation process of the DCRC and divide the 16 loading and unloading conditions of the DCRC into four operation modes for process optimization. Secondly, different variables and parameters are set for the DCRC’s four operating modes, and a refined collaborative dispatching model for the DCRCs with AGVs and ECTs is proposed. Finally, a practical adaptive co-evolutionary genetic algorithm solves the model. Meanwhile, arithmetic examples verify the correctness and practicality of the model and algorithm. The experimental results show that the total running time of the DCRCs is the shortest in the U-shaped ACT when the number of quay cranes (QC) to DCRC and AGV ratios are 1:2 and 1:10, respectively. At the same time, the number of QCs and DCRCs has a more significant impact on the efficiency of the ACT than that of AGVs, and priority should be given to the allocation of QCs and DCRCs. The research results have essential guidance value for U-shaped ACTs under construction and enrich the theory and method of collaborative scheduling of U-shaped ACT equipment.

A Sub-flow Adaptive Multipath Routing Algorithm for Data Centre Network

In response to the fast and intensive traffic changes and the presence of redundant links in the network topology in data centre networks, multipath routing has become the dominant approach. Existing multipath routing algorithms are still inadequate in terms of adapting to dynamic changes in the network state quickly and the cooperation of path selection and sub-flow assignment. Therefore, this paper proposes a sub-flow adaptive multipath routing algorithm for data centre networks (SAMP). The deep deterministic policy gradient (DDPG) algorithm is introduced. DDPG combines deep learning (DL) and reinforcement learning (RL) to implement different network state changes quickly, especially in the process of topology, to achieve dynamic migration of the optimised decisions that have been learned. An adaptive multipath routing model for subflows is established to accomplish collaborative scheduling of path selection and subflow assignment based on the real-time state of the network. The experimental results show that the algorithm can be well adapted to the data centre network, and when compared with several traditional methods, it reduces the latency by an average of 31.3%, improves the task transmission success rate by an average of 14.9%, and increases the throughput by 37.1%.

Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data

Traditional taxi services (TTSs) play an important role in satisfying daily travel demands. The rapid growth of ride-hailing services (RHSs) has increased the convenience of customized travel. However, the volume of empty ride-hailing vehicles has increased. Identifying the collaborative scheduling areas (CoSAs) between RHSs and TTSs can further improve the efficiency of urban travel services. Therefore, we propose a method to identify CoSAs based on the travel demand and vehicle supply of TTSs and RHSs derived from trajectory data. We first optimize and make the temporal resolution of the trajectories of different types of vehicles uniform based on the shortest path algorithm. Then, the indicators describing travel demand and vehicle supply are defined and calculated. Finally, the areas with a high vehicle supply of one type and a low vehicle supply and high travel demand of the other type are identified as CoSAs. A dataset for Xiamen Island indicates that the CoSAs of TTSs to RHSs can provide potential routes to pick up passengers that are 41% and 11% shorter than the actual routes at 9:00 and 18:00, respectively. The constructed method can also improve the CoSA identification results.

The multi-visit-multi-voyage scheduling of the heterogeneous shuttle tanker fleet via inventory-oriented joint planning

The deep-sea oil exploitation leads to the growing utilization of the Floating Production Storage and Offloading (FPSO), where the crude oil is processed and stored until it can be transferred to a shuttle tanker. The conventional scheduling method of one shuttle tanker serving a fixed FPSO is challenged by the increasing numbers and capacities of the deep-sea FPSOs. This paper proposes a collaborative scheduling method by pooling the transportation capabilities of the shuttle tankers and conducting inventory-dependent route planning. The inventory headroom of the FPSO is taken as a key variable for the route planning of the shuttle tankers. A multi-visit-multi-voyage model is constructed to schedule the optimal routes for the shuttle tanker fleet in the multi-voyage time period. Case studies demonstrated that by implementing the collaborative scheduling, the proposed model could achieve 11.14% transportation cost saving in the planning period when compared with the conventional shuttle tanker scheduling model. By proper combining the shuttle tanker types, further cost savings can be obtained while using less shuttle tankers to execute oil transportation.

Research on construction data association management of Urban Rail Transit Based on BIM

With the continuous expansion of the scale of urban rail transit construction projects, it is difficult to improve the coordination in construction and the relevance of construction data. In order to solve the problem of data association in construction, an urban rail transit construction data association management system oriented to BIM Technology is proposed. BIM is used to build a dynamic workflow collaboration architecture to manage the data in urban rail transit construction, so as to realize construction collaborative scheduling. In the test and analysis of the system, the comprehensive ability test of the system shows that the minimum response time of the system can be reduced to 1.2 ms. At the same time, the system has ideal visualization effect and obvious practical value in practical application. Therefore, the urban rail transit construction data association management system based on BIM can show ideal results in complex construction environment, which is of great significance to China's urban construction.

Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems.

Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions

An inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server’s computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.

A collaborative scheduling model for production and transportation of ready-mixed concrete.

Ready-mixed-concrete (RMC) is an important green and clean building material which is widely used in modern civil engineering. For the large-scale planar foundations of urban public buildings, huge amounts of RMC need to be continuously delivered to the construction site according to strict time windows, which brings the problem of multi-plants collaborative supply. In this paper, considering transportation capacity, initial setting time, and interrupt pumping time, a collaborative scheduling model for production and transportation of RMC with the objective of minimizing the penalty cost of interruption pumping and vehicle waiting time and fuel consumption cost was established. According to the characteristics of the problem, a double chromosome synchronous evolution genetic algorithm was designed. Finally, the model and algorithm proposed in the paper were verified by data experiments. The computing results showed that in two cases of different scenarios, such as ordinary constructions and emergency constructions, the proposed scheduling model can save 18.6 and 24.8% cost respectively. The scheduling model and algorithm proposed in the paper can be applied directly to improve the operational efficiency of RMC supply chain.

Collaborative Shift-Scheduling and Perceived Procedural Justice as Predictors of Clinical Decision-Making in Managed Care Practice among Selected Samples of Health Workers in the Federal Capital Territory (FCT), Abuja, Nigeria

This study examined the roles of collaborative shiftwork schedule and procedural justice as predictors of clinical decision-making in managed care practice among a selected sample of health workers in Abuja, Nigeria. The study was survey design conducted among 197 healthcare workers were selected using convenient sample from public and private healthcare institutions in Abuja. The Bergen Shift Work Sleep Questionnaire (BSWSQ) developed by Flo et al. (2012) was used to assess shiftwork collaborative scheduling. Procedural justice was measured using the Procedural Justice Scale (OJS) developed by Niehoff and Moorman (1993). While The Clinical Decision-Making Survey (CDMS) developed by Ferrell et al. (1991) was used to measure clinical decision-making. The result revealed that shiftwork did show significant relationship with clinical decision-making among psychiatric nurses [r (197) = .451, p <. 01]. Results from the multiple regression showed that procedural justice significantly predicted clinical decision-making among healthcare workers [? = .331, p <0.01]. The result of this study suggest that healthcare workers who engage in shift-schedule with a high perception of procedural justice have higher propensity to provide enhanced clinical decision-making at the workplace. The Nigerian healthcare reforms and policies should be reviewed, specifying issues relating to collaborative shiftwork for healthcare workers.

Study on scheduling and path planning problems of multi-AGVs based on a heuristic algorithm in intelligent manufacturing workshop

In order to solve the scheduling and path planning problems of multi-AGVs in an intelligent manufacturing workshop, it is necessary to consider loading, unloading, and transporting the workpiece of each AGV at the same time. A step task scheduling and path optimization mode of AGV is proposed. The process is as follows: Firstly, a mathematical model algorithm and a material transportation task allocation algorithm based on the urgency degree of workpiece processing were established for the optimization objective, and all workpiece transportation task sequences between shelves and processing equipment were assigned to the corresponding AGV to generate the initial feasible path of each AGV. Then, the AGV collision detection and anti-collision algorithm are designed to plan the global collision-free walking path of multi-AGVs in the workshop, and the path can be dynamically adjusted according to the delivery task. The model is solved by a heuristic algorithm ant colony algorithm and MATLAB coding. Finally, an example is given to verify the effectiveness of the method, which can effectively solve the task allocation of multi-AGVs and avoid collision path planning based on the transportation task sequence, and improve the work efficiency of AGV. This research can provide a theoretical basis and practical reference for realizing multi AGVs collaborative scheduling by using AGV automated material transport system in an intelligent production workshop.

Collaborative scheduling of production resources and spare parts inventory for distributed equipment with feedback guidance and minimum capacity loss

Previous studies on production scheduling have neglected the impact of the supply mode of inventory allocation on the arrangement of production resources and guiding significance of delivery information to the demand side. To fill these gaps, this study investigates a collaborative scheduling model of production resources and spare parts inventory to satisfy the spare parts requirements of demand-side distributed equipment, and we propose feedback guidance based on delivery information to reduce the negative impact of delayed spare parts. The feedback guidance adopts an optimal operating strategy to reduce the capacity loss of the equipment corresponding to the delayed spare parts. An optimization objective, referred to as the total capacity loss of all distributed equipment, is introduced to visually display the effect of the scheduling scheme on the demand side. Another objective is to minimize the total service cost of the service provider, including the inventory, transportation, and tardiness costs. A modified restarted iterated Pareto greedy algorithm is proposed to solve the model. Based on the problem characteristics, certain effective operators, including two problem-dependent local-search operators and a modified idle-time insertion method, are designed to improve the search performance. Extensive experiments are conducted to demonstrate the effectiveness of the algorithm, including the feedback guidance and proposed operators. Finally, the impact of different inventory levels on the service provider and demand side is analyzed, and the results indicate that the collaborative mode can effectively reduce inventory.

A cooperative EV charging scheduling strategy based on double deep Q-network and Prioritized experience replay

The rapid growth of the number of electric vehicles (EVs) has significantly increased the demand for electricity of residents, which may lead to transformer overload of distribution networks if there is no reasonable power consumption planning. In view of the coordinated charging of EVs under the photovoltaic (PV) energy connected to the power grid, a collaborative charging control strategy is proposed based on a double deep q-network with Prioritized experience replay (DDQN-PER). Specifically, Long–Short-Term Memory (LSTM) neural network is utilized to capture the uncertainties caused by power requirement, PV power, and real-time electricity price. Then, a modified DDQN approach and the corresponding reward function are applied to solve the cooperative charging problem. In addition, the introduction of a PER mechanism based on TD bias sampling in reinforcement learning alleviates the problem of reward sparsity in learning scenarios and improves the training stability and efficiency. The proposed approach can achieve collaborative scheduling of EVs, which helps reduce the charging cost, promote the consumption of PV energy and avoid transformer overload. Simulation case studies based on real data demonstrate that the proposed algorithm can reduce the charging cost by 30% and increase the PV power utilization by 10% compared to the DDQN algorithm.

Analysis of a Collaborative Scheduling Model Applied in a Job Shop Manufacturing Environment

Collaborative Manufacturing Scheduling (CMS) is not yet a properly explored decision making practice, although its potential for being currently explored, in the digital era, by combining efforts among a set of entities, either persons or machines, to jointly cooperate for solving some more or less complex scheduling problem, namely occurring in job shop manufacturing environments. In this paper, an interoperable scheduling system integrating a proposed scheduling model, along with varying kinds of solving algorithms, are put forward and analyzed through an industrial case study. The case study was decomposed in three application scenarios, for enabling the evaluation of the proposed scheduling model when envisioning the prioritization of internal–makespan-or external–number of tardy jobs-performance measures, along with a third scenario assigning a same importance or weight to both kinds of performance measures. The results obtained enabled us to realize that the weighted application scenario permitted reaching more balanced, thus a potentially more attractive global solution for the scheduling problem considered through the combination of different kinds of scheduling algorithms for the resolution of each underlying sub problem according to the proposed scheduling model. Besides, the decomposition of a global more complex scheduling problem into simpler sub-problems turns them easier to be solved through the different solving algorithms available, while further enabling to obtain a wider range of alternative schedules to be explored and evaluated. Thus, contributing to enriching the scheduling problem-solving process. A future exploration of the application in other types of manufacturing environments, namely occurring in the context of extended, networked, distributed or virtual production systems, integrating an increased and variable set of collaborating entities or factories, is also suggested.