Search Results (140)

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

The transition to fully electric bus (EB) fleets introduces new challenges in coordinating daily operations and managing charging energy needs, while accounting for infrastructure constraints. The paper proposes a three-stage optimization framework that integrates EB scheduling with overnight charging under realistic depot layout constraints. In the first stage, a mixed-integer linear program (MILP) determines the minimum number of EBs with ample batteries and related schedules to complete all timetabled trips. With the fleet size fixed, the second stage minimizes the EB battery capacity by optimizing trip assignments. In the third stage, charging schedules are iteratively optimized for different numbers of chargers to minimize charger power capacity and charging cost, while ensuring each EB is fully recharged before its first trip on the following day. The matrix-shape depot layout imposes spatial and operational constraints that restrict the charging and movement of EBs based on their parking positions, with EBs remaining stationary overnight. The entire process is repeated by incrementing the fleet size until a saturation point is reached, beyond which no further reduction in battery capacity is observed. This results in a Pareto frontier showing trade-offs between required battery capacity, number of chargers, charger power capacity, and charging cost. The proposed method is applied to a real-world airport parking shuttle service, demonstrating its potential to reduce the battery size and charging infrastructure demands while maintaining full operational feasibility. Full article

The current plunger gas lift production process still relies on the traditional ‘one-to-one’ control configuration, where one controller manages a single gas well. This approach does not fulfil platform requirements for centralized, efficient, and unified coordination and management of multiple wells. To increase production, improve efficiency, and mitigate safety risks, this article offers an intelligent optimization method for a collaborative plunger gas lift in multi-objective, multi-well platforms. The method integrates mechanistic modeling and data-driven approaches to develop a collaborative model for multiple wells on the platform, accounting for inter-well pressure interference and pipeline backpressure. A particle swarm optimization algorithm is implemented to solve the model, with a composite fitness function balancing maximum daily gas production and minimum production fluctuations. A case study on the XXX Platform shows that the method enhances total gas production, reduces production fluctuations, and lowers system backpressure compared to the current operating schedule. Implemented via a localized edge computing architecture, it supports real-time scheduling, providing technical references for shale gas development. Full article

To address energy shortages and achieve carbon peaking/neutrality, this study develops a distributed renewable-based integrated energy system (IES) for rural active zero-energy buildings (ZEBs). Energy consumption patterns of typical rural houses are analyzed, guiding the design of a resource-tailored IES that balances economy and sustainability. Key equipment capacities are optimized to achieve net-zero/zero energy consumption targets. For typical daily cooling/heating/power loads, equipment output is scheduled using a dual-objective optimization model minimizing operating costs and CO₂ emissions. Results demonstrate that: (1) Net-zero-energy IES outperforms separated production (SP) and full electrification systems (FES) in economic-environmental benefits; (2) Zero-energy IES significantly reduces rural building carbon emissions. The proposed system offers substantial practical value for China’s rural energy transition. Full article

In this study, we examined the impact of an intelligent system and air conditioning control on power consumption. The experiment was carried out during five distinct time periods: (1) background room usage, (2) smart system setup, (3) air conditioning control to maintain room temperature at no more than 27 °C, (4) air conditioning temperature control during working hours, and (5) air conditioning operated continuously to maintain the room temperature at 27 °C. For each time period, the daily power consumption was evaluated, and outliers were identified and eliminated using a threshold derived from the hourly average. The findings demonstrated that the smart system setup period and air conditioning control resulted in lower usage compared to continuously operated air conditioning with substantial spikes in demand. The impacts of the novel system and air conditioning control on energy consumption were revealed through statistical analysis, which included regression models and hypothesis tests. According to this study’s findings, it is essential to regulate spikes and guarantee proper operation to reduce the carbon footprint while maintaining a comfortable atmosphere. Notably, the integration of the smart system and optimized scheduling resulted in a substantial decrease in greenhouse gas emissions, with annual carbon emissions reduced by up to 65% compared to continuously operated air conditioning without smart control. Moreover, these systems can optimize energy use. Full article

This study evaluates the feasibility and benefits of introducing battery-powered hybrid electric aircraft (HEA) into regional airline operations. Using 2019 U.S. domestic flight data, the ERJ175LR is selected as a representative aircraft, and several HEA variants are designed to match its mission profile under different battery technologies and power management strategies. These configurations are then tested across over 800 actual daily flight sequences flown by a regional airline. The results show that well-designed HEA can achieve 3–7% fuel savings compared to conventional aircraft, with several variants able to complete all scheduled missions without disrupting turnaround times. These findings suggest that HEA can be integrated into today’s airline operations, particularly for short-haul routes, without the need for major infrastructure or scheduling changes, and highlight opportunities for future co-optimization of aircraft design and operations. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

Currently, deep reinforcement learning has been widely applied to energy system optimization and scheduling, and the DRL method relies more heavily on historical data. The lack of historical operation data in new integrated energy systems leads to insufficient DRL training samples, which easily triggers the problems of underfitting and insufficient exploration of the decision space and thus reduces the accuracy of the scheduling plan. In addition, conventional data-driven methods are also difficult to accurately predict renewable energy output due to insufficient training data, which further affects the scheduling effect. Therefore, this paper proposes a small-sample scenario optimization scheduling method based on multidimensional data expansion. Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed method is verified by arithmetic examples. Full article

Urban rail transit, a crucial component of urban public transportation, often experiences increased operational costs and carbon emissions due to low-load operations being conducted during off-peak passenger flow periods. This study aims to develop an optimization method for the daily scheduling of rail train operations with the goal of carbon emission reduction, while comprehensively considering the built environment and travel demand. Firstly, the influence of the urban built environment on residents’ travel demand is analyzed using an XGBoost model. Secondly, a time convolutional travel demand prediction model, Built Environment-Weighted Temporal Convolutional Network (BE-TCN), weighted by built environment factors, is constructed. Finally, an optimization method for rail train operation schedules based on the built environment and travel demand is proposed, with the objective of carbon emission reduction. A case study is conducted using the Hangzhou urban rail transit system as an example. The results indicate that the optimization method proposed in this study can achieve monthly carbon emission reductions of 1524.58 tons, 1181.94 tons, and 520.84 tons for Lines 1, 2, and 4 of the Hangzhou urban rail transit system, respectively. The research findings contribute to enhancing the economic efficiency and environmental sustainability of urban rail transit systems. Full article

Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand forecasts into a discrete-time planning horizon, thereby enabling real-time adjustments to dispatch decisions. Additionally, we introduce a tailored mathematical model—grounded in mixed-integer linear programming and space-time flows—that serves as a benchmark to evaluate our heuristic’s performance under the operational constraints typical of traditional public transportation systems in Colombian mid-sized cities. A key contribution of this research lies in combining predictive modeling (using Prophet for passenger demand) with operational optimization, ensuring that dispatch frequencies adapt promptly to varying ridership levels. We validated our approach using a real-world case study in Montería (Colombia), covering eight representative routes over a full day (5:00–21:00). Numerical experiments show that: 1. Our heuristic matches or surpasses 95% of the optimal solution’s operational utility on most routes, with an average gap of 4.7%, relative to the benchmark mathematical model. 2. It maintains high service levels—above 90% demand coverage on demanding corridors—and robust bus utilization, without incurring excessive operating costs. 3. It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. Overall, these results underscore the heuristic’s practical effectiveness in boosting profitability, optimizing resource use, and rapidly adapting to demand fluctuations. The proposed framework thus serves as a scalable and implementable tool for transportation operators seeking data-driven dispatch solutions that balance operational efficiency and service quality. Full article

In the context of electricity and carbon markets, with the in-depth research of virtual power plants and to realize the mutual assistance of electric energy in different regions within the same distribution network, a scheduling strategy of virtual power plant alliance based on dynamic electricity and carbon pricing using the Master–Slave game is proposed. Firstly, an interactive framework of virtual power plant alliance is designed in which the alliance operator formulates the electricity and carbon prices, and each user entity formulates the operation plan according to the prices. Secondly, the information gap decision theory is adopted to handle the uncertainties on the source–load side. Based on the Master–Slave game and source–load interaction, an economic optimal dispatching model for the virtual power plant alliance is established. Finally, the particle swarm optimization algorithm nested with the CPLEX solver is used to solve the model, and the rationality and effectiveness of the proposed strategy are demonstrated through case analysis. The simulation results show that, after considering the electricity energy interaction and dynamic electricity–carbon pricing, the daily operation cost of the virtual power plant alliance was reduced by 47.7%, carbon emissions decreased by 24.6%, and comprehensive benefits increased by 77.2%. Full article

With the increasing proportion of renewable energy in power systems, the applications of mobile energy storage systems (MESSs) with better flexibility and controllability are becoming more widespread. To further explore the hybrid ESS optimization scheduling problem of MESS and SESS, this paper first quantifies parts of actual road topologies in Dali City, China, and combines the Dijkstra algorithm to obtain an MESS path optimization framework. Subsequently, a hybrid ESS optimization scheduling model combining MESS and SESS is constructed with the objective functions of maximizing the scheduling benefits of the hybrid ESS and minimizing system voltage deviation. Finally, the non-dominated sorting genetic algorithm III (NSGA-III) is used to solve the hybrid ESS optimization scheduling model. To verify the effectiveness of the proposed method, this paper selected typical daily load and renewable energy output data in winter in Dali for the case study. The final result shows that the total profit of the optimized scheduling of the hybrid ESS is CNY 578, of which the arbitrage income is CNY 1119.2 and the total cost is CNY 540.44. Meanwhile, the voltage distribution range and total power loss are optimized from [0.9616, 1.0105] to [0.9723, 1.0008] and 0.963 MW to 0.9134 MW, which indicates that the coordinated scheduling of hybrid ESS is the key to improving the reliability of distribution network operation, and the path optimization of MESS is crucial for enhancing its profitability. Full article

With the proliferation of electric bicycle battery swapping models, spatial supply demand imbalances of battery resources across swapping stations have become increasingly prominent. Existing studies predominantly focus on location optimization but struggle to address dynamic operational challenges in battery allocation efficiency. This paper proposes an integer programming (IP)-based dynamic scheduling optimization method for shared batteries, aiming to minimize transportation costs and balance battery distribution under multi-constraint conditions. A resource allocation model is constructed and solved via an interior-point method (IPM) combined with a branch-and-bound (B&B) strategy, optimizing the dispatch paths and quantities of fully charged batteries among stations. This study contributes to urban sustainability by enhancing resource utilization efficiency, reducing redundant production, and supporting low-carbon mobility infrastructure. Using the operational data from 729 battery swapping stations in Shanghai, the spatiotemporal heterogeneity of rider demand is analyzed to validate the model’s effectiveness. Results reveal that daily swapping demand in core commercial areas is 3–10 times higher than in peripheral regions. The optimal scheduling network exhibits a ‘centralized radial’ structure, with nearly 50% of batteries dispatched from low-demand peripheral stations to high-demand central zones, significantly reducing transportation costs and resource redundancy. This study shows that the proposed model effectively mitigates battery supply demand mismatches and enhances scheduling efficiency. Future research may incorporate real-time traffic data to refine cost functions and introduce temporal factors to improve model adaptability. Full article

In this paper, the dispatch for the industrial micro-grid with an integrated photovoltaic-gas-manufacturing facility system considering carbon emissions and operation costs is investigated. Two kinds of energy, electricity and natural gas, are contained in the integer energy system, in which the electricity mainly comes from the PV panels and the utility electricity network, and the natural gas mainly comes from the utility gas network. In addition, electricity and natural gas can be converted into each other. Four kinds of loads, electricity load, gas load, heating load and cooling load, need to be satisfied, in which the electricity load can be divided into fixed load and flexible load. The flexible load comes from the scheduling for manufacturing facilities, and the scheduling of manufacturing facilities is modeled as a kind of deferable load to be integrated into the energy system. Moreover, daily operation costs and carbon emissions are considered in the decision, and the deviation preference strategy is used to solve this multi-objective optimization problem. Finally, a case study with a lithium-ion battery assembly system is proposed. According to the results, it can be found that the proposed model can help managers realize effective scheduling of the industrial micro-grid. Full article

Standalone wind–solar–diesel–storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such as rural regions and islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable energy generation and the uncertainty of load demand, this paper proposes an adaptive optimization scheduling method (DQN-PSO) that integrates Deep Q-Network (DQN) with Particle Swarm Optimization (PSO). The proposed approach leverages DQN to assess the operational state of the microgrid and dynamically adjust the key parameters of PSO. Additionally, a multi-strategy switching mechanism, incorporating global search, local adjustment, and reliability enhancement, is introduced to jointly optimize both clean energy utilization and power supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. These findings validate the strong adaptability of the proposed algorithm to dynamic environments. Further, a parameter sensitivity analysis underscores the significance of the dynamic adjustment mechanism. Full article