Optimal Dynamic Scheduling Research Articles

Dynamic hybrid parallel computing of the Ray Model for solving underwater acoustic fields in vast sea

Utilizing acoustic information for search route planning will greatly increase the success rate of searching for underwater targets, which requires rapid computing of numerous underwater acoustic fields. The efficiency of traditional computing methods is too low to meet the requirements of rapid applications. In this paper, a underwater multi-acoustic fields computing model is developed based on ray theory, and multi-level hybrid parallel computing strategies are designed based on the model characteristics, and a dynamic scheduling optimization algorithm at process level is introduced to solve the load imbalance problem. All parallel computing strategies are tested in Tianhe-II High Performance Computer (HPC) system, and the tests show that: 1. compared with the serial version, hybrid parallel computing strategy provides a Speedup of 75.7 under 240 cores; 2. after the introduction of the dynamic scheduling optimization algorithm, the speed of solving the underwater acoustic fields is further increased by 28.99% under the same computing resources, and the Speedup reaches 97.67; 3. the optimal combination of process/thread parameter on the Tianhe-II HPC system is given as 3/8, and the final Speedup reaches 112.13.

Cooperative multi-agent reinforcement learning for multi-area integrated scheduling in wafer fabs

The existing scheduling methods of wafer fabs focus on single area, achieving local optimisation while failing to realise global optimisation due to neglecting the coordination of multi-area. Therefore, it is necessary to consider the complex opposing relationships between multi-area caused by constraints such as batch processing, re-entrance, and multiple residency times within and between areas to conduct integrated scheduling and shorten the production cycle time. For this issue, this paper proposes a cooperative multi-agent reinforcement learning for multi-area integrated scheduling. Aiming at the dynamic batching and scheduling considering the dynamic arrival lots in multi-area, a multi-agent reinforcement learning algorithm is presented to learn the optimal dynamic batching and scheduling policy firstly. Subsequently, a cooperative multi-agent framework is raised to achieve the global optimisation and coordination of multi-area. Furthermore, an adaptive exploration strategy is constructed to enhance the global exploration capability of the complex solution space caused by residency time constraints and re-entrant property. Moreover, a policy share enhanced Double DQN is employed to improve the generalisation and adaptability of the multi-agent. Finally, the experiments demonstrate that the proposed integrated scheduling method has better comprehensive performance compared to the previous area-separated scheduling methods.

Multi-path serial tasks offloading strategy and dynamic scheduling optimization in vehicular edge computing networks

Vehicular edge computing networks (VECNs) can provide a promising solution to support efficient task execution of vehicles. Consider the channel and access time variations caused by the high mobility of vehicles in a vehicular environment when designing task offloading strategies in VECNs. In this paper, we perform multi-path offloading for a task vehicle with serial tasks based on both dynamic communication distances of vehicle-to-infrastructure (V2I) links, that of vehicle-to-vehicle (V2V) links, and slowly varying large-scale fading information of wireless channels. Considering the task vehicle's low delay requirements, our goal is to minimize the maximum task completion time of the task vehicle. A multi-path dynamic offloading scheme (MPDOS), composed of three parts, is proposed to achieve maximum delay minimization. The maximum processing capability of links between a task vehicle and roadside units (RSUs) is first taken as the objective to find the required communication links, which can decrease the total processing time by increasing transmission rate and execution capacity. Then, a task allocation scheme based on a multi-knapsack algorithm matches tasks and RSUs. Finally, a balancing scheme is leveraged to provide load-balancing computing performance across all computation devices. Numerical results show that our proposed scheme outperforms 30.7% of the RA algorithm, and the task completion rate can reach 99.55%.

Reliability and performance of resource efficiency in dynamic optimization scheduling using multi-agent microservice cloud-fog on IoT applications

Reliability and performance of resource efficiency in dynamic optimization scheduling using multi-agent microservice cloud-fog on IoT applications

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

In previous studies on re-entrant hybrid flow shops, the impact of dynamic events was often ignored despite being a common occurrence in practical production. To address this issue and simultaneously reduce energy consumption, a multi-objective evolutionary algorithm with variable neighborhood search (MOEA-VNS) has been proposed to optimize the green scheduling problem in a re-entrant hybrid flow shop with dynamic events (RHFS-GDS).The approach involves creating a green dynamic scheduling optimization model, which aims to minimize the makespan, total energy consumption, and stability of rescheduling solutions. A hybrid green scheduling decoding method is then employed to select machines for each operation and calculate fitness. Additionally, three neighborhood structures are designed to improve the population diversity and optimality of the MOEA-VNS algorithm. Two rescheduling strategies are also adopted to handle dynamic events that may occur during production. Experimental results demonstrate that these approaches are effective in solving the RHFS-GDS problem and can guide actual production. By incorporating dynamic events and rescheduling strategies into the optimization process, the proposed MOEA-VNS algorithm provides a comprehensive solution to the complex challenges faced by re-entrant hybrid flow shops in practical production environments.

Evaluating the impact of virtual energy storage under air conditioning and building coupling on the performance of a grid-connected distributed energy system

Energy storage technologies are vital in improving the operation performance of grid-connected distributed energy systems. The adjustability of indoor temperature and the thermal inertia of buildings can form an excellent virtual energy storage. However, there are few studies on the impact of this virtual energy storage on the operation performance of grid-connected distributed energy systems. Therefore, this study first calculates the equivalent thermal resistance and thermal capacitance of a building by using EnergyPlus and establishes the first-order thermodynamic load calculation model. Subsequently, the system dynamic optimal scheduling model, considering the virtual energy storage, was developed based on the first-order thermodynamic load calculation model. The Gurobi nonconvex solver is used to optimize the model, and the results are compared with the optimal scheduling results without considering the virtual energy storage. The results indicate that, guided by time-of-use electricity pricing, the virtual energy storage effectively reduces the air conditioning load during high and peak tariff periods while increasing it during valley tariff periods. This change in air conditioning load leads to an increase in grid power consumption during valley tariff periods. However, it also improves the system's operation efficiency and ultimately reduces the total grid power and gas consumption. The lower energy consumption makes the primary energy saving rate and carbon dioxide emission reduction rate of Scenario 2 greater than zero compared to Scenario 1 and increases with the increase of renewable energy penetration of the integrated power system. Furthermore, affected by the difference in the proportion of grid power consumption under time-of-use electricity pricing and the total energy consumption, the system's operating cost in Scenario 2 is less than in Scenario 1. The reduction rates in summer and winter typical days are 1.95 % and 6.48 %, respectively. Therefore, fully utilizing the virtual energy storage under air conditioning and building coupling can reduce the operating cost, primary energy consumption, and carbon dioxide emissions of grid-connected distributed energy systems.

An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.

Research on data mapping and fusion method of ship production workshop based on digital twins

Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved.

Dynamic Scheduling Optimization Method for Multi-AGV-Based Intelligent Warehouse Considering Bidirectional Channel

With the implementation of AGV technology and automated scheduling, storage and retrieval systems have become widely utilized in warehouse management. However, due to the use of unidirectional channels, AGV movement is restricted, and detours may occur frequently. Additionally, as the number of AGVs increases, deadlocks can arise, which lead to delays in order packaging and a decrease in overall warehouse performance. Hence, this paper proposes a dynamic scheduling method for task assignment and route optimization of AGVs to prevent collisions. The routing optimization method is based on an improved A* algorithm, which takes into account the dynamic map as input. Moreover, this paper investigates highly complex collision scenarios in bidirectional channels. Through simulation experiments, it is evident that scheduling methods based on bidirectional channels offer a clear advantage in terms of efficiency compared to those based on unidirectional channels.

Whether the off-design characteristics of equipment should be considered in integrated energy system optimization design models – A case study

The operational performance of integrated energy systems (IESs) varies with their configuration. The equipment model directly influences the capacity configuration of IESs. The conventional static model assumes that equipment efficiency is constant, ignoring its off-design characteristics. For this reason, this paper establishes a dynamic model considering the off-design characteristics of the equipment. Based on the dynamic and static models, bi-level optimization models with the minimum total cost as the optimization objective are established to optimize the configuration of IESs, and the optimization results are System 1 and System 2, respectively. Then, the operational performance of the two systems under the dynamic optimal scheduling model is compared. The results demonstrate that the capacity of the absorption chiller (ABC), combined heating and power (CHP) unit in System 1 are larger than in System 2 owing to the difference in equipment models, whereas the capacity of ground source heat pumps (GSHPs) shows the opposite trend. The larger capacity of the GSHP increases the grid power consumption of System 2 but also reduces its operating efficiency. The greater operation energy consumption and grid power consumption make the operating cost, carbon tax, and total cost of System 2 under the design condition higher than that of System 1, with an increased rate of 1.38%, 3.47%, and 1.14%, respectively. As with the design condition, the carbon tax, operating cost, and total cost of System 2 are higher than those of System 1 under both summer and winter operating conditions, particularly under summer operating conditions, where the carbon tax, operating cost, and total cost increase by 2.27%, 1.24%, and 0.88% respectively. Even if the energy price changes, the carbon tax, operating cost and total cost of System 2 in a typical year are still greater than those of System 1. Therefore, it is necessary to consider the off-design characteristics of equipment in the bi-level optimization design model.

Research on multi-rider collaborative dynamic scheduling model and algorithm of takeaway delivery

Aiming at the field of high-demand takeaway delivery, a dynamic scheduling strategy based on multi-rider collaborative delivery status is proposed, and the model implementation is obtained by constructing a collaborative dynamic scheduling optimization model and using the optimization TS algorithm to obtain a multi-rider scheduling scheme in a dynamic environment, so that the total delivery time is minimized. Finally, the experimental results show the effectiveness and feasibility of the model and algorithm implementation.

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing

In the quest for sustainable production, manufacturers are increasingly adopting mixed-flow production modes to meet diverse product demands, enabling small-batch production and ensuring swift delivery. A key aspect in this shift is optimizing material distribution scheduling to maintain smooth operations. However, traditional methods frequently encounter challenges due to outdated information tools, irrational task allocation, and suboptimal route planning. Such limitations often result in distribution disarray, unnecessary resource wastage, and general inefficiency, thereby hindering the economic and environmental sustainability of the manufacturing sector. Addressing these challenges, this study introduces a novel dynamic material distribution scheduling optimization model and strategy, leveraging digital twin (DT) technology. This proposed strategy aims to bolster cost-effectiveness while simultaneously supporting environmental sustainability. Our methodology includes developing a route optimization model that minimizes distribution costs, maximizes workstation satisfaction, and reduces carbon emissions. Additionally, we present a cloud–edge computing-based decision framework and explain the DT-based material distribution system’s components and operation. Furthermore, we designed a DT-based dynamic scheduling optimization mechanism, incorporating an improved ant colony optimization algorithm. Numerical experiments based on real data from a partner company revealed that the proposed material distribution scheduling model, strategy, and algorithm can reduce the manufacturer’s distribution operation costs, improve resource utilization, and reduce carbon emissions, thereby enhancing the manufacturer’s economic and environmental sustainability. This research offers innovative insights and perspectives that are crucial for advancing sustainable logistics management and intelligent algorithm design in analogous manufacturing scenarios.

Multi-objective global dynamic optimal scheduling of smart building loads considering carbon emissions

In recent years, there has been a significant increase in electricity consumption and carbon emissions from households in smart buildings. This increase has had a profound impact on the economic aspects of residential electricity consumption and the achievement of the national dual carbon goal. In this paper, an optimal scheduling model for household loads in smart buildings is established. It considers the user demand side, grid-side supply, and the impact of the PV energy storage system (PESS) on carbon emissions. The optimization objectives are the economy of household electricity consumption and the total deviation of electricity consumption. Second, the adaptive mixed inverse learning strategy multi-objective beluga optimization algorithm (AMOBWO) is adopted to enhance its overall dynamic optimization. This optimization addresses challenges such as the uneven distribution of electricity consumption, load timing constraints, power limitations, and uncertainty in electricity consumption during the scheduling of smart building loads. Finally, the results demonstrate that AMOBWO decreases the total cost of household electricity consumption in smart buildings by 59.32% (based on the selected maximum PV capacity). Additionally, it reduces the total deviation by 14.28% after optimal scheduling. As the PESS capacity increases, there is a significant reduction in the indirect carbon emissions from smart buildings, decreasing from 881.279 kg CO2 to 431.299 kg CO2.

Research on Digital Management and Collaboration Platform for Civil Aircraft Manufacturing Logistics Scenarios

Abstract In this paper, the overall architecture design of the platform is proposed, and the data architecture and business architecture of the platform are discussed. On this basis, the logistics path planning method of digital collaboration is proposed, including the aspects of path demand splitting and distribution vehicle spatio-temporal network, and the demand splittable logistics path planning model is considered. Finally, the simulation of the workshop logistics dynamic scheduling example is designed, and the dynamic scheduling optimization results are analyzed and verified. The results show that: through optimization, its time penalty cost f 1 = 801.38, the total distance traveled by the delivery trolley f 2 = 1709m, and the total distance of empty load is 692.15 m, and the dispatching scheme increases the distance of empty load by 59 m . The research results of this paper can provide a reference for digital management and collaboration platform for civil aircraft manufacturing enterprises and provide a reference for research in related fields.

Improved reinforcement learning-based real-time energy scheduling for prosumer with elastic loads in smart grid

In this study, we investigate the dynamic energy-scheduling problem of a prosumer (producer/consumer) with an energy storage device (EDS) and elastic loads. The goal is to develop efficient real-time scheduling strategies for prosumers, and to minimise their total long-term costs (i.e. cost of energy purchased from the external grid and depreciation cost of the EDS). The challenges are twofold: the uncertainty of the energy output of the prosumer and the time-coupling constraints of the EDS and elastic loads. To address these challenges, we first describe the dynamic energy-scheduling problem as a Markov decision process. Then, an approximate state dual-agent Q-learning algorithm is proposed to solve the optimal dynamic scheduling problem by improving the model-free reinforcement learning(RL) method. Compared with the traditional RL method, the proposed algorithm reduces the system-state dimensions and exhibits improved performance. The proposed algorithm can only be assisted by mutual interactions between the environment with a reward feedback mechanism to dynamically respond to uncertain changes in the environments, without modelling or predicting the system environments. Finally, extensive empirical evaluations using real-world traces are conducted to study the effectiveness of the proposed algorithm. The results show that the proposed algorithm can reduce the total cost of the prosumer by up to 6.3%, 11.7% and 22.4% compared with the traditional RL method, Lyapunov optimisation and greedy algorithm, respectively.

Negative carbon optimal scheduling of integrated energy system using a non-dominant sorting genetic algorithm

An integrated energy system, as a system where multiple energy sources coexist, is important for achieving energy conservation and emission reduction and upgrading the energy structure. In this study, a regional integrated energy system capable of absorbing CO2 emissions is proposed. The system can absorb 38.24% of the CO2 generated in the system by using the alkaline solution produced by electrolysis of water for hydrogen production. At the same time, the system can recover the waste heat generated by electrolysis of water and fuel cells, which can meet 42.42% of the heat demand. In order to better achieve efficient utilization between energy sources of the integrated energy system, a nonlinear mixed integer dynamic scheduling optimization model is constructed with multiple objectives of minimizing economic operating costs, CO2 emissions, and energy consumption. The model is solved using a non-dominated ranking genetic algorithm, and the optimal scheduling scheme is determined by combining compromise schemes. Meanwhile, the system can spontaneously select the most appropriate scheduling strategy based on the proposed scheduling model. The results show that it saves 7.23% of operation cost, 33.04% of CO2 emission, and 9.94% of energy consumption compared with the conventional cooling, heating, and power triple-supply system. The system is not only a stable source of energy such as electricity and hydrogen, but also achieves energy saving and emission reduction.

Meta-learning-based multi-objective PSO model for dynamic scheduling optimization

The by-product gas is an important secondary energy in the iron and steel industry. It is important to make the by-product gas’s utilization efficient and reasonable,which is the key to improve the economic efficiency and the level of energy conservation and emission reduction. Aiming at the problems of complex dynamic changes, difficult to accurately model and difficult to predict real-time traffic in gas energy system, this paper proposes an optimal scheduling method based on meta-learning multi-objective particle swarm optimization algorithm. The gas energy optimization scheduling problem is modeled as a multi-objective dynamic scheduling optimization problem. The difficulty of this problem is to comprehensively consider multiple indicators, continuously adapt to the change of the objective function over time, and track the changing Pareto optimal solution set. One of the promising solutions is to build prediction models with better performance under dynamic changes. The model we propose makes full use of the optimization results and the experience information of the optimization process in the historical optimization task. We propose a dynamic parameter initialization method based on meta-learning by starting multiple historical optimization tasks at the same time. Thus,the comprehensive parameters of different tasks are obtained, and a general and best base parameter is learned. Experiments show that the proposed model has better convergence and accuracy than the conventional algorithm.

Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.

Digital twin-based job shop anomaly detection and dynamic scheduling

• A DT driven anomaly detection and dynamic scheduling framework is proposed to reduce the deviation between initial plan and execution result. • An anomaly detection model called multi-level production process monitoring model is proposed to detect disturbances in the production. • A real-time optimization strategy based on rolling window mechanism is explored to support timely revision and iterative optimization of scheduling scheme. Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.

Optimal dynamic pricing and production policy for a stochastic inventory system with perishable products and inventory-level-dependent demand

In this paper, we consider a joint dynamic pricing and production policy for a stochastic inventory system with perishable products. The demand is stochastic and dependent on the price and the level of the on-hand inventory. Combined dynamic pricing and production control, a stochastic dynamic optimization model that maximizes the total discounted profit is built. Applying the stochastic optimal control theory, we formulate the problem of finding the optimal joint dynamic pricing and production schedule as the problem of solving a Hamilton-Jacobi-Bellman (HJB) equation. By solving the HJB equation, analytical solutions for optimal pricing and production rate are obtained. In addition, a numerical example is given to illustrate the validness of the theoretical results and sensitivity analysis on major system parameters is carried out.