LMP-Opt: A simulation-based hybrid model for dynamic job scheduling and energy optimization in serverless computing

In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. It integrates the scheduling of the workpieces, machines, and maintenance personnel to improve the response efficiency of emergency equipment maintenance. To this end, a self-learning Ant Colony Algorithm based on deep reinforcement learning (ACODDQN) is designed in this paper. The algorithm searches the solution space by using the ACO, prioritizes the solutions by combining the non-dominated sorting strategies, and achieves the adaptive optimization of scheduling decisions by utilizing the organic integration of the pheromone update mechanism and the DDQN framework. Further, the generated solutions are locally adjusted via the feasible solution optimization strategy to ensure that the solutions satisfy all the constraints and ultimately generate a Pareto optimal solution set with high quality. Simulation results based on standard examples and real cases show that the ACODDQN algorithm exhibits significant optimization effects in several tests, which verifies its superiority and practical application potential in dynamic scheduling problems.

PurposeBasic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.Design/methodology/approachUpon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.FindingsThe need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices.Practical implicationsOptimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Social implicationsOptimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Originality/valueThe paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.

Optimal energy flow control strategy for a residential energy local network combined with demand-side management and real-time pricing

Soft-linking energy demand and optimisation models for local long-term electricity planning: An application to rural India

An important component of the cyber-defense mechanism is the adequate staffing levels of its cybersecurity analyst workforce and their optimal assignment to sensors for investigating the dynamic alert traffic. The ever-increasing cybersecurity threats faced by today’s digital systems require a strong cyber-defense mechanism that is both reactive in its response to mitigate the known risk and proactive in being prepared for handling the unknown risks. In order to be proactive for handling the unknown risks, the above workforce must be scheduled dynamically so the system is adaptive to meet the day-to-day stochastic demands on its workforce (both size and expertise mix). The stochastic demands on the workforce stem from the varying alert generation and their significance rate, which causes an uncertainty for the cybersecurity analyst scheduler that is attempting to schedule analysts for work and allocate sensors to analysts. Sensor data are analyzed by automatic processing systems, and alerts are generated. A portion of these alerts is categorized to be significant , which requires thorough examination by a cybersecurity analyst. Risk, in this article, is defined as the percentage of significant alerts that are not thoroughly analyzed by analysts. In order to minimize risk, it is imperative that the cyber-defense system accurately estimates the future significant alert generation rate and dynamically schedules its workforce to meet the stochastic workload demand to analyze them. The article presents a reinforcement learning-based stochastic dynamic programming optimization model that incorporates the above estimates of future alert rates and responds by dynamically scheduling cybersecurity analysts to minimize risk (i.e., maximize significant alert coverage by analysts) and maintain the risk under a pre-determined upper bound. The article tests the dynamic optimization model and compares the results to an integer programming model that optimizes the static staffing needs based on a daily-average alert generation rate with no estimation of future alert rates (static workforce model). Results indicate that over a finite planning horizon, the learning-based optimization model, through a dynamic (on-call) workforce in addition to the static workforce, (a) is capable of balancing risk between days and reducing overall risk better than the static model, (b) is scalable and capable of identifying the quantity and the right mix of analyst expertise in an organization, and (c) is able to determine their dynamic (on-call) schedule and their sensor-to-analyst allocation in order to maintain risk below a given upper bound. Several meta-principles are presented, which are derived from the optimization model, and they further serve as guiding principles for hiring and scheduling cybersecurity analysts. Days-off scheduling was performed to determine analyst weekly work schedules that met the cybersecurity system’s workforce constraints and requirements.

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

The dynamic scheduling of intelligent RGV is an important factor that affecting the production efficiency of intelligent processing systems, and it plays an important role in manufacturing enterprises to improve production efficiency. This paper analyzes the dynamic scheduling problem of intelligent RGV by establishing a reasonable RGV dynamic scheduling model. First of all, it starts from the case where the machine does not malfunction, and the processing of one process and two processes is expressed by a 0-1 integer plan respectively. Secondly, the shortest time to start processing is the objective function. The single-process RGV static scheduling model and the dual-process RGV static scheduling model based on 0-1 integer plan are established respectively, and the model is solved by particle swarm optimization. On the basis of the RGV static scheduling model, the machine failure condition of the CNC is regarded as the state of continuous operation of the CNC by the case of the machine failure. This paper passes the original model in the case of possible machine failure. The constraint conditions are added, and the single-process RGV dynamic scheduling model and the dual-process RGV dynamic scheduling model are established. Finally, the practicality and effectiveness of the built model and algorithm are verified by numerical experiments, and the simulation experiments of the two models are carried out using eM-Plant software. The models and algorithms established in this paper are effective research and application of dynamic scheduling methods and optimization techniques, which play an important role in manufacturing enterprises to improve production efficiency and reduce costs.

The steel industry is an important part of the secondary industry, and it has a strong role in promoting economic development. However, the steel industry is a high-energy-consuming industry, which has large energy consumption and waste of resources. Therefore, it is very important to do a good job of energy conservation and emission reduction in iron and steel enterprises. Therefore, this paper studies the dynamic energy balance and optimization model of iron and steel enterprises firstly, and further designs and operates the system based on this. It is show that the model has good practicability, which can balance the pipe network and reduce emission.

Various production disturbances occurring in the flexible job shop production process may affect the production of the workshop, some of which may lead to the prolongation of production completion time. Therefore, a flexible job shop dynamic scheduling method based on digital twins is proposed and a dynamic scheduling framework is constructed. Compared with the traditional workshop, the digital twin-based flexible job shop can upload the relevant production data of the physical workshop to the data management center in real time, and after fusion processing the data can work cooperatively with the upper application system. Taking the dynamic disturbance of rush order insertion as an example, the dynamic scheduling of insertion order is realized based on the dynamic scheduling framework, and then the production efficiency is improved. To achieve the shortest completion time, a mathematical model for dynamic scheduling optimization is established and a genetic algorithm (GA) is applied to solve the model. Finally, a practical case is applied to show that the completion time of this algorithm is reduced by 35%, which verifies the feasibility of the proposed dynamic scheduling method.

Dynamic optimisation of evacuation route in the fire scenarios of offshore drilling platforms

Notice of Violation of IEEE Publication Principles<br><br>"Dynamic Vehicle Routing and Scheduling with Variable Travel Times in Intelligent Transportation System"<br>by Changfeng Zhou, Yan Liu, Yuejin Tan, Liangcai Liao<br>in the Proceedings of the 6th World Congress on Intelligent Control and Automation, June 2006, pp. 8707-8711<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>"Intelligent Transportation System Based Dynamic Vehicle Routing and Scheduling with Variable Travel Times"<br>by Eiichi Taniguchi, Hiroshi Shimamoto<br> in Transportation Reseach, Part C: Emerging Technologies, Vol 12, Issue 3-4, June 2004, Elsevier, pp. 235-250<br><br> <br/> A dynamic vehicle routing and scheduling model that incorporates real time information using variable travel times is presented. Dynamic traffic simulation was used to update travel times. The model was applied to a test road network. Results indicated that the total cost decreased by implementing the dynamic vehicle routing and scheduling model with the real time information based on variable travel times. Therefore, the dynamic vehicle routing and scheduling model will be beneficial for both carriers in reducing total costs and society at large by alleviating traffic congestion

Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times

The full-cycle operation optimization of the acetylene hydrogenation reactor should strictly adhere to the operation optimization scheme within the operation cycle, regardless of scheduling changes. However, in actual industrial processes, in order to meet temporary process scheduling requirements, the acetylene hydrogenation reactor needs to adjust its operation strategy temporarily within the remaining operation cycle based on the results of dynamic optimization for a certain period. It brings additional challenges and a research gap to the operational optimization problem. To make up for this research gap, this paper focuses on researching a type of full-cycle dynamic optimization problem where the operation optimization scheme is temporarily adjusted during the operation cycle. The methods employed for changing the operation optimization scheme include modifying the operation cycle, maximizing economic benefits, and altering the optimization goal to maximize the operation cycle. A novelty full-cycle scheduling optimization framework based on surplus margin estimate is proposed to build a platform for these methods. The paper analyzes the impact of process scheduling changes on full-cycle optimization using a dynamic optimization model that maintains the operation margin. It establishes a full-cycle scheduling optimization model and obtains the optimal scheduling strategy by a novelty method NSGBD (non-convex sensitivity-based generalized Benders decomposition). In this process, an adaptive CVP (control vector parameterization) based on a decomposition optimization algorithm is proposed, which tackles the challenge of optimizing complex acetylene hydrogenation reactor models on a large time scale. Scheduling optimization can be realized as an annualized benefit of 1.56 × 106 and 1.57 × 106 ¥ separately within two scheduling optimization constraints, and the computational time required is much less than previous operational optimizations.

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system.

The increase and rapid growth of data produced by scientific instruments, the Internet of Things (IoT), and social media is causing data transfer performance and resource consumption to garner much attention in the research community. The network infrastructure and end systems that enable this extensive data movement use a substantial amount of electricity, measured in terawatt-hours per year. Managing energy consumption within the core networking infrastructure is an active research area, but there is a limited amount of work on reducing power consumption at the end systems during active data transfers. This paper presents a novel two-phase dynamic throughput and energy optimization model that utilizes an offline decision-search-tree based clustering technique to encapsulate and categorize historical data transfer log information and an online search optimization algorithm to find the best application and kernel layer parameter combination to maximize the achieved data transfer throughput while minimizing the energy consumption. Our model also incorporates an ensemble method to reduce aleatoric uncertainty in finding optimal application and kernel layer parameters during the offline analysis phase. The experimental evaluation results show that our decision-tree based model outperforms the state-of-the-art solutions in this area by achieving 117% higher throughput on average and also consuming 19% less energy at the end systems during active data transfers.