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With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

Floods are common and inevitable natural disasters. Achieve Sustainable Development Goal (SDG) 11.5 is a critical challenge for coastal cities, especially those in deltaic lowlands such as in the case of Guangzhou, China. Regarding the spatial planning and design of such urban regions, it is crucial to study the impacts of flooding in compact or decentralized spatial development pathways. This reinforces the understanding of the relationship between strategic decisions for spatial planning and flood mitigation. However, the lack of a computer model to assess spatial evolution paths is a significant limitation. The non-dominated Sorting Genetic Algorithm II (NSGA-II) explores the possibility of a compact built-up land layout in 2030. The results showed that, concerning the 2030 decentralized scenario, the 2030 compact scenario presents a large increase in the integrated fitness function value from 0.618 to 0.771 (the increase is equivalent to 0.153 or about 24.75%). In addition, different development scenarios were constructed by setting different target weights. Compared to the decentralized scenario results, the fitness function values of the optimization results of each scenario showed better results at different levels. They could also serve as a reference for other similar coastal areas to achieve SDG 11.5 by 2030. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) farming is increasingly being used to power electric vehicles (EVs). Although many studies have developed dynamic EV charging prediction and scheduling models, few of them have coupled rooftop PV electricity generation with the spatiotemporal EV charging demands at an urban scale. Thus, this study develops a research framework containing three interconnected modules to investigate the feasibility of EV charging powered by rooftop PVs. The framework is constructed by the statistics of time serial EV charging demands at each station, the planning of rooftop PV installations associated with all charging stations, and the development of a dynamic dispatching algorithm to transmit surplus electricity from one station to another. The algorithm can maximize the overall balance between supply and demand, maximize the total PV electricity generation while minimising the total PV area, minimize the number of PV charging stations used as the suppliers for dynamic dispatch, and minimize the total electricity transmission distance between stations given the same power supply. The experiment utilizes a complete EV charging dataset containing 5574 charging piles with more than 9.7 million records in June and July in Guangzhou, China. The results show that rooftop PVs can supply more than 90% of the charging demand. The results encourage and inspire us to generalize and promote such a solution in other cities. Future work can refine the algorithm by adapting different PV sizes into various charging stations to further improve the electricity generation capability and the dynamic dispatching efficiency. [ABSTRACT FROM AUTHOR]

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. [ABSTRACT FROM AUTHOR]

This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 km2 watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. [ABSTRACT FROM AUTHOR]

The train operation plan that meets the passenger travel needs can better attract passenger flow and improve the core competitiveness of high-speed railway. The maximization of economic benefits and the minimization of travel expenses as the research objects, the high-speed rail operation plan as the research object, and the travel demand of passengers as constraints are taken. The train operation plan is combined with the OD passenger flow. Taking into account the passengers’ purchase psychology and the timeliness of train ticket purchase, a multi-objective optimization model of train operation plan is established based on dynamic passenger flow. In order to solve the problem, a multi-objective differential (SG-MOSaDE) algorithm is designed based on individual information and the improved mutation operator. Taking a certain route in Guangzhou as an example, the results show that the optimized operation plan not only maximizes meeting the passenger travel demand， but also reduces the travel expenses of the passenger while improving the economic benefits of the railway sector. The number of stations has decreased compared with the original, and the stop plan is more balanced. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line. • A novel multi-objective optimization model based on the reinforcement learning is proposed to search for the optimal control sequence. The embedding matrix is used to formulate the control sequence. • An intelligent train operation algorithm based on the gated recurrent unit network is proposed to compute the optimal speed trajectory without the precise train dynamics or the prior knowledge from the experienced drivers. • The energy consumption is reduced by 3.17%, 0.71% and 6.77% respectively comparing with fuzzy predictive control, genetic algorithm and field test data. [ABSTRACT FROM AUTHOR]

The effective resistance and rapid recovery from external environmental risks has risen to be a social challenge in the accelerated urbanization process of China. However, the current measures for urban high-rise and high-density core area with high-volume pedestrians and high pollution are often focused on one-dimensional means, which cannot meet the multi-objective demands simultaneously. Therefore, this paper is aimed to explore a prevention-oriented route - a vertical pedestrian system for high-rise buildings based on scheme phase, with multiple objectives optimization of strengthening the ability of ecological order restoration and assisting occupants in recovering psychological and physiological injury from high-density environment in a relative fast speed. The feasibility and effectiveness have been verified by a complete technology route of scheme selection, numerical simulation and field measurements. The results show that, first, after the integration of secondary ground pedestrian system (SGPS) basic unit, the average predicted mean vote (PMV) improves 75.45%, the average air temperature declines 2.75 °C, and the average spatial integration increases 35.35%. Second, the optimal range of unit height difference in SGPS under hot-humid climate condition is suggested to be 6–9 storeys. Third, compared with single-sided "E-type" connection, the thermal comfort improves 70.49% and the indoor average PMV is optimized by 2.56%, with a more stable wind speed by using bilateral alternating "S-type" connection. Finally, compared with connections among low-integrated secondary ground units, the average integration is up to 6.98% with visual and path links of high-integrated-orientation. This paper objectively answers the question of whether and to what extent the combination of vertical greening and walking system contributes to ecological order restoration, which provides a new orientation for exploring the routes of maintaining urban ecological stability and optimizing environmental structure. • It evaluates the effect of SGPS on the high-rise and high-density environment. • It analyzes SGPS's effects from a multi-objective perspective. • The SPGS plays a positive role in environmental regulation and health promotion. • The union of walking system and ventilating shaft effectively integrate resources. [ABSTRACT FROM AUTHOR]

• A novel hybrid power/thermal-to-hydrogen energy storage scheme is applied to DES. • Multi-objective optimization is conducted for optimal configurations of the DES. • A case study of DES is performed in different climate regions of China. • The configuration flexibility and performances of the DES are improved. • Climate and market conditions have significant influences on DES deployment. This paper presents a novel distributed energy system (DES) with hybrid-hydrogen energy storage, in which the proton exchange membrane electrolyzer (PEMEC) and steam reforming of methanol (SRM) are used to convert surplus power and waste heat into hydrogen. To identify the feasibility of the proposed DES in different climate regions, a case study is performed to choose a rehabilitation hospital in Harbin, Jinan, Wuhan and Guangzhou of China to explore the DES optimal configurations. The mixed integer linear programming model is established and the multi-objective optimization considering annual total cost (ATC) and annual carbon emissions (ACE) is conducted by improved version of augmented ε -constraint (AUGMECON2) method for system optimal configurations. After selecting four cases among Pareto frontiers for each city, the two methods of Analytic Hierarchy Process/Shannon entropy combined with Gray Relation Analysis are performed to determine the most suitable locations. The results show that adding the SRM to DES as a hybrid-hydrogen energy storage system can improve the configuration flexibility, environmental performance and primary energy ratio compared with only-electrochemical hydrogen storage DES. The Pareto frontiers for four cities demonstrate a trade-off between ATC and ACE, and the ACE is decreased by 6.3% with ATC increased by 1.4% in Jinan. Generally, Guangzhou is the best suitable city, and Jinan and Wuhan rank second by two evaluation methods with 0.727 and 0.643 average scores, respectively. The current electricity and natural-gas prices are verified to be beneficial to Jinan to deploy the proposed DES, and Guangzhou and Wuhan present a better economic performance with the minimum ATC decreased by 11.4% and 6.58% under ideal energy prices. The results can provide useful information for hybrid-hydrogen energy storage application to DES in different climate regions. [ABSTRACT FROM AUTHOR]

• A multi-objective optimization and analysis framework for HGGI is proposed. • A trade-off between costs and systematic resilience for HGGI design is developed. • The HGGI layouts with different degree of decentralization is compared. • Importance of spatial allocation for HGGI is highlighted in different urban areas. The trade-offs for alternative grey-green infrastructure (HGGI) solutions between life cycle cost (LCC) and systematic resilience may impose many limitations in planning and implementation of urban stormwater strategies in high-dense urban catchment. This study presents a generic approach in the context of a multi-objective optimization, with the aim to (1) optimize and design HGGI under various uncertainties, and conflicting objectives (e.g., LCC, technological and operational resilience); and (2) determine the trade-offs between the multi-objectives and degrees of system decentralization in areas with various development intensities. Taking two areas in Guangzhou, China, as case studies, this study shows that decentralized HGGI exhibits economic advantages. However, to maintain a high systematic resilience in high-density catchments, it is often necessary to rely on solutions with high degree of centralization, which may incur higher cost. HGGI is found to perform better in terms of technological resilience under extreme storms, than operational resilience to the uncertainty associated with structure performance. In addition, porous pavement is found to exhibit high potential for wider application compared to bioretention cells as green infrastructure in HGGI. The framework developed in this study could support decision-making for stormwater management via enhancing both hydraulic reliability and hydrologic resilience. [Display omitted] [ABSTRACT FROM AUTHOR]

Tugboat assistance is essential when large ships are berthing and unberthing, and requires ports to produce efficient tugboat schedules considering various constraints. Few studies optimize tugboat scheduling from multiple vectors simultaneously, however. This paper thus constructs a bi-objective, mixed-integer linear programming, green tugboat scheduling model in order to minimize the maximum completion time and total fuel consumption, which improves port service levels, decreases tugboat company operating expenditures and thus environmental pollution emissions. The proposed model also considers the time window characteristics of the tidal port. We use the non-dominated sorting genetic algorithm II (NSGA-II) framework integrating the characteristics of tugboat scheduling to solve the model. Finally, we make a case study about Guangzhou Port to validate the model and the algorithm via a comparison of results solved by NSGA-II and CPLEX solver. The Pareto fronts obtained show the trade-off relationship between the two objectives, and provide a basis for port tugboat scheduling plans. [ABSTRACT FROM AUTHOR]

• Develop a multi-class distribution network design problem with inventory and superimposed risks. • Introduce risk superposition coefficients for multi-class hazmat to capture the superimposed risks among different materials resulting from possible chemical reaction. • Formulate the long-term detour cost/risk and the cyclic time windows penalty costs under the time-dependent (periodic) road closure policy. • Propose a population-based risk definition that evaluates the risk for the population at any location and any time with respect to its multi-class hazmat logistics system Provide a metropolitan wide real-world application and new insights. Transportation and inventory are essential to hazardous materials logistics, while different classes of hazardous materials are often transported over a network simultaneously. Despite their in-transit and storage incompatibility, the superimposed risks among different materials, which results from possible chemical reaction once accidents (e.g., leakage, explosion) happen, further complicate the comprehensive plans. In this study, we introduce a new multi-class hazmat distribution network design problem with inventory and superimposed risks (MHND) in a multi-echelon supply chain, where the planning of locations, inventory, and routes are made together. The long-term detour cost/risk and the cyclic time windows penalty costs under the time-dependent (periodic) road closure policy are explicitly formulated. We further propose a new population-based risk definition that evaluates the risk for the population at any location and any time with respect to its multi-class hazmat logistics system. In particular, to capture the interactions between different types of materials, we introduce risk superposition coefficients to capture possible superimposed risks among different hazmat that accommodate a general system with more than two hazmat types. We develop a knowledge-based NSGA-II algorithm with cyclic dissimilarity-based elitist selection (NSGA-II-CD) to solve the problem. The devised cyclic dissimilarity-based elitist selection (CD) operator can tackle the issue of speeding proliferation, which greatly improves the solution quality. Our model is applied to a metropolitan-wide real-world case study in Guangzhou, China. The results suggest that, from the perspective of the traffic management sector, the periodic road closures policy in Guangzhou could be possibly upgraded to a full-time prohibition. Moreover, the results provide the following insights to authorities (1) there is a positive convex relation between risk minimization and risk equilibration. The authorities should not try to find a perfect distribution of risk, and they should make a trade-off between the risk equity and total exposed risk; (2) there is a positive correlation between the level of service and total risk. Thus, in practice, the agencies should make a trade-off between economic viability of the system, exposed risk, and maintaining good service for customers; and (3) the interactions between different types of hazmat considerably affect the distribution network design; specifically, the route overlapping ratio for different types of hazmat decreases when their interactions intensify. [ABSTRACT FROM AUTHOR]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]