Multi-objective Cooperative Dynamic Scheduling Model with Integrating EMPC and IJADE Optimization for By-product Gas in Steel Production

This paper focuses on multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with machine breakdown. First, a multi-objective dynamic scheduling model is established, with objectives to minimize makespan and total machine workload. Second, according to the processing status of faulty machine, a hybrid rescheduling strategy including transfer rescheduling strategy and complete rescheduling strategy is proposed to react to stochastic machine breakdown. The performance of two rescheduling strategies is analyzed in terms of the scheduling efficiency and its stability, from the delay extent and initial scheduling deviation, respectively. Besides, the optimal adaptation conditions of both scheduling strategies are obtained. Furthermore, the non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the constructed model. Experimental results demonstrate the effectiveness of the proposed strategies on reducing the impact of machine breakdown in real scheduling.

To deal with the problem of tight wartime repair time, heavy repair task, and complicated constraints during wartime, the multi-objective dynamic scheduling study of circuit repair equipment maintenance task scheduling was carried out. The military problem of equipment maintenance task scheduling for circuit repair under complex constraints was proposed. Considering the uncertainty of the repair status of the equipment to be repaired, the constraints such as repair time window, non-traversal, and variety of repair capability were introduced on the basis of the repair capability and the repair time limit. A multi-objective dynamic scheduling model that takes the total number of repaired equipment, the sum of equipment importance degree, and the total time of secondary operations as the scheduling target.is established. An improved multi-objective genetic algorithm based on NSGA-II is designed to solve the model, and the rationality and effectiveness of the model and algorithm are verified through example simulation and analysis.

To integrate the combination of electric vehicles and wind power of the transmission system, a wind farm environment multi-objective transmission model considering plug-in electric vehicles was established. The fuzzy optimal scheduling model for the environment and economy target of wind farm power system considering grid interconnection of electric vehicles designed by the author has the following characteristics: A fuzzy multi-objective dynamic scheduling model for the design of wind farm power system considering grid connection of plug-in electric vehicle is established, and the original 10 systems are taken as an example for power system design. The results show that the better model shows that the electric vehicle grid connection can greatly reduce the disadvantage of wind power stochastic uncertainties of system scheduling. The model can be used under different conditions further analyzing the rationality of the model determined by the author. An improved particle swarm optimization algorithm is used to solve this problem. The model takes the environmental and economic benefits into account, which improves the efficiency of the model.

Scheduling plays a significant role in improving observation effectiveness of distributed imaging satellites. Although extensive satellite scheduling algorithms have been proposed, none of them focuses on dynamic scheduling for emergency tasks. In this paper, a novel multi-objective dynamic scheduling model for emergency tasks on distributed imaging satellites is established for the first time. To improve user's satisfaction ratio and resource utilization, we propose the task merging strategy: establishing a task merging graph (TMG) model and proposing a task merging algorithm-CP-TM based on clique partition. In addition, a rehabilitation technique is suggested to overcome the disadvantage that task merging will make tasks have less imaging opportunities. To further enhance the schedulability, the task backward shift in the waiting sequence is considered in our study. Furthermore, a novel dynamic scheduling algorithm called TMBSR-DES is presented, which comprehensively considers task merging, backward shift, and rehabilitation. To demonstrate the superiority of our TMBSR-DES, we conduct extensive experiments by simulations to compare TMBSR-DES with three existing algorithm-RBHA, RTSSA, and LSA, as well as three baseline algorithms-BS-DES, TMR-DES, and TMBS-DES. The experimental results indicate that TMBSR-DES outperforms the others and is suitable for emergency task scheduling.

A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response

The job shop scheduling problem (JSSP) deals with determining schedule for each resource/machine under job and machine flow restrictions such that the selected objective function is satisfied. Multi-objective scheduling is widely used to obtain desirable results in the existence of more than one performance measure in scheduling problems. The paper focuses on multi-objective scheduling in a job shop environment. One of the useful methods in multi-objective environment is the use of fuzzy operators in modelling the system. Fuzzy operators provide the model with characteristics where user can input desired bounds for all of the performance measures with suitable membership functions. In this study, three mathematical models are presented and combined as a multiple objective scheduling model where the considered three objectives are minimising number of tardy jobs, total tardiness and maximum tardiness, respectively. For the solution of the model, fuzzy programming is utilised by using min operator and augmented max-min operator. The model with augmented max min operator found only non-dominated solutions. Then mathematical model optimising two performance measure is also discussed. The quality between solutions obtained from math model optimising two performance measures model versus three performance measures is also discussed.

Community decision-makers’ choice of multi-objective scheduling strategy for integrated energy considering multiple uncertainties and demand response

With the rapid growth of electric vehicle (EV) ownership, the problem of increased peak loads on distribution networks due to large-scale EV integration needs to be addressed. This study proposes an active distribution network multi-objective optimization scheduling method. It takes into account the charging demands of large-scale EVs and aims to minimize distribution network operating costs, reduce net load variance, and maximize the photovoltaic (PV) consumption rate. First, the Monte Carlo sampling method is used to analyze the charging load demands of a large number of EVs. Next, we construct a multi-objective optimization scheduling model for the active distribution network. This model integrates the charging demands of EVs with the operating constraints of the distribution network. To tackle the multi-objective optimization problem, we propose the NSGAII-NDAX algorithm. Additionally, we employ a fuzzy theory-based method to select the Pareto optimal solution set, addressing the challenge of decision-making complexity posed by the large size and information-rich content of the optimal solution set. Finally, the effectiveness of the proposed method in the comparative analysis of multiple scenarios is verified by an improved IEEE 33-node example. The experimental results show that the proposed model and method can effectively utilize EV charging load optimization to reduce the peak-to-valley difference in the system load while ensuring the system’s economic operation and the maximum PV consumption rate. Compared with the other algorithm, the NSGAII-NDAX algorithm has stronger optimization ability and can better handle multi-objective optimization problems.

Selection of new absorbents for carbon dioxide capture

During the steelmaking and hot rolling processes, various defects and cracks appear throughout the steel product. These cracks may initiate and grow throughout the hot rolling process and result in a lower quality of the product than is acceptable. The most energy-intensive part of the hot rolling process is the reheating furnace, where slabs are heated up to a target rolling temperature largely through radiant heat transfer. In the reheat furnace, large stresses may develop due to the thermal gradients within the steel product. A thermal-stress analysis is proposed based on finite element method (FEM) to study the impacts of charging temperature, slab velocity, and heating rate on stress development as the steel slab travels through an industrial pusher-type reheat furnace. Furnace zone information is taken from a previously validated computational fluid dynamics (CFD) model and applied as thermal boundaries and constraints within the thermal-stress FEM models. Temperature and stress results were taken at the core, top, bottom, top quarter, and the bottom quarter of the steel slab at different residence times. Moreover, temperature lines and contour plots taken along the length of the slab allow visualization of the gradual development of temperature and identification of the locations corresponding to temperature variations as the slabs move in the furnace. The slab temperature predicted by the FEM model was found valid when compared with industrial data. Stress predictions found similar trends with previously published works as well as evidence of thermal shock in the sub-surface near the beginning of the residence time.

PurposeThe purpose of this paper is to provide insights into the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in New Zealand (NZ).Design/methodology/approachThis study adopted narrative inquiry methodology that involved semi-structured interviews (SSI)/expert opinion and systematic literature review (SLR) data to determine the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in NZ. The data were analysed by using descriptive analysis, Likert scale and thematic analysis techniques to understand the relationship of these factors to propose a system dynamic model.FindingsThis study concludes that weather, pandemic, material, geotechnical and disaster factors highly influence while other factors such as equipment shortage, breakdown, design error, labour and event had mixed impact on the dynamic scheduling (DS) that aids on-time project delivery. The proposed system dynamic model can enhance the understanding of factors affecting DS.Research limitations/implicationsSLR is limited to English literature. The limitations of an SSI and a small sample size are acknowledged.Practical implicationsThe proposed model can reduce the uncertainty and scheduling errors during the planning phase and aid in the lesser scheduling modification during the execution phase. In practice, this study will be helpful for road contractors to understand environment, surroundings and resource in-control and out-of-control factors, overcome road construction delays, reduce cost, aid in stakeholder management and sustainable development.Social implicationsThe inclusion of environment, resource and surroundings factors in force majeure clauses will bring an understanding between contracting parties and in turn reduce disputes and delays and help social causes such as on-time infrastructure delivery.Originality/valueFor the first time in a road construction, dynamic project scheduling model that collectively included and linked environment, resource, and surroundings factors to determine the in-control and out-of-control factors for an organisation is proposed. The novelty in the paper is provided by the inclusion of the events, disasters, and pandemics influence on DS in the NZ road construction industry for the first time.

METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

The Industrial Internet of Things (IIoT) enhances production efficiency through inter-factory resource sharing and intelligent human-machine interaction (HMI) in flexible manufacturing. This study addresses large-scale multiobjective resource scheduling optimization in the complex IIoT environments to improve production efficiency and resource scheduling under growing customization and order demands. We develop a multiobjective IIoT scheduling model under assumptions of task priority and resource constraints, optimizing makespan, logistics time, energy consumption, tardiness time, production cost, and carbon emission. To improve resource scheduling performance, a fuzzy decision-enhanced dual directed sampling-assisted large-scale multiobjective optimization algorithm (LMOEA-FDDS) is proposed. The algorithm employs a dual directed sampling method, uses two different types of search directions to guide the population evolution, and combines a complementary environmental selection strategy based on angle penalty distance (APD), effectively improving the algorithms search eiciency and performance. Experimental results show that LMOEA-FDDS achieves 30%-70% higher HV values than state-of-the-art algorithms, demonstrating its effectiveness in solving complex IIoT scheduling problems.

A cluster system consisting of interconnected microgrids represents a novel form of smart grid architecture. However, the uncontrolled access of a large number of electric vehicles (EVs) poses challenges to system operation, including increased operational costs and threats to safe and stable grid performance. To address these issues, this paper proposes a Multi-Factor Oriented Model strategy (MOM strategy) and improved bald eagle search (BES) algorithm to optimize the multi-objective day-ahead scheduling of microgrid clusters. Firstly, building upon an established disordered charging model, roulette strategy is employed to integrate photovoltaic redundancy and time-of-use tariffs, thereby constructing an orderly charging and discharging model for EVs. Secondly, a multi-objective day-ahead scheduling model for microgrid cluster with multiple charging and discharging strategies for electric vehicles is established. The multi-microgrid scheduling problem is solved within the proposed optimization framework using an improved bald eagle search algorithm that integrates the Levy flight strategy and the golden sine strategy through an updating factor. Finally, through case study of a park-based microgrid cluster, the scheduling results under various scenarios and EV strategies are analyzed. The results demonstrate that the proposed improved algorithm outperforms other algorithms in practical applications. The multi-factor oriented ordinal model strategy effectively reduces operational costs, minimizes power fluctuations in the tie lines between microgrids, and mitigates the adverse effects of large-scale EV integration on the economic and stable operation of the cluster system. This study provides a scientific reference for addressing the scheduling challenges of EVs at different scales in day-ahead.

In order to cope with the increasingly complex challenges in power system scheduling, especially in the context of large-scale integration of renewable energy and frequent fluctuations in load demand, this study aims to solve multi-objective optimization problems in power system scheduling, including economy, reliability, and environmental friendliness. First, by introducing a state space model (SSM) to accurately model and predict the dynamic evolution process of the power system in order to grasp the real-time state of the system. On this basis, a multi-objective optimization scheduling model was constructed, which combines various constraints of the power system, adopts an adaptive weight particle swarm optimization (PSO) algorithm for search, and further optimizes the solution using the Cauchy–Lorentz (CL) distribution, aiming to achieve the best balance between economy, environmental protection, and efficiency in power system scheduling and obtain the Pareto optimal solution set. The experimental results indicate that the strategy proposed in this study has significant advantages in improving the operational efficiency of power systems, enhancing system adaptability, and promoting green and low-carbon development. Not only does it provide new theoretical guidance and technical means for the field of power system scheduling, but it also provides strong support for promoting the optimization and transformation of energy structure and achieving sustainable development goals.