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• A green multi-objective waste collection problem is modeled. • The amount of waste demand for each customer is stochastic. • A multi-objective optimization algorithm based on machine learning is proposed. • A case study is done for a real waste collection problem in a certain area of Beijing. Most of the existing municipal waste management (MWM) systems focus on the optimization of the waste disposal center locations and waste collection paths, which can be modeled based on the location-routing problem (LRP). This study models a green MWM system by a three-objective location-routing problem to achieve equilibrium among the total cost, carbon emission, and residential satisfaction. The amount of waste demand for each customer is considered as an independent discrete random variable following a normal distribution. The multi-objectives and non-deterministic characteristics make this problem more intractable than the traditional LRP. A multi-objective optimization algorithm based on decision tree classifier is proposed for solving this problem. The decision tree classifier learns from previous searching experience, and then guides the following evolution process to avoid blind searching. The experimental results show that the proposed algorithm has high competitiveness compared with other state-of-art methods. A case study is also conducted for a real waste collection problem in a certain area of Beijing. The proposed method adopts efficient location-routing strategies to balance the total cost, carbon emissions, and distance between residential areas and waste disposal centers. [ABSTRACT FROM AUTHOR]

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]

In light of reducing train operation energy consumption while maintaining the passenger service level for creating sustainable urban rail transit systems, we address a non-parallel train timetabling problem considering regenerative braking energy utilization and the non-parallel operation of multiple trains on a metro line via a newly proposed multi-objective timetable (MOT) optimization model and an evolutionary algorithm based on NSGA-II. The optimization objectives of the MOT model are to find satisfactory energy-saving timetables on the Pareto frontier by minimizing the total travel time of passengers and minimizing the net energy consumption of trains. An improved multi-objective evolutionary algorithm based on NSGA-II is constructed to generate the optimal arrival and departure times at each station for each train running in a non-parallel operation mode. This study tests the feasibility of the proposed optimization method via an empirical case using the data collected from the Yizhuang Line of the Beijing metro systems in China. The simulation results show that the proposed optimization method satisfies both the energy utilization and passenger service levels along a Pareto front. The MOT improves the overall effectiveness of regenerative braking energy utilization by 29.88% in comparison with the original timetable; it reduces the net operation energy consumption by 44.86% relative to the travel-oriented timetable (TOT); and it reduces the total passenger travel time by 27.18% compared with the energy-oriented timetable (EOT). [ABSTRACT FROM AUTHOR]

The quality of residential buildings in old urban areas of Beijing is known to be inconsistent, prompting numerous urban renewal projects in the city. This research investigates how building space impacts energy usage and daylighting in courtyard areas of old urban regions in northern China. It also proposes a quick evaluation method for building performance in courtyard spaces, utilizing multi-objective optimization and machine learning classification prediction as a theoretical framework. A study was conducted to gather and organize building space parameters and their corresponding performances using a genetic algorithm. The dataset was then pre-processed and trained using the LightGBM algorithm. The model validation results revealed a recall of 0.9 and an F1-score of 0.8. These scores indicate that the design scheme's performance level can be accurately identified in practical use. The goal of this study is to propose a set of rapid assessment methods for building performance levels in courtyard spaces. These methods can significantly improve the feedback efficiency between design decision and performance assessment, reduce the time wasted in building performance simulation during the architectural design process, and avoid unreasonable renovation and addition in urban renewal. Furthermore, the research method has universality and can be applied to courtyard-shaped buildings in other regions. [ABSTRACT FROM AUTHOR]

Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line's punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model. [ABSTRACT FROM AUTHOR]

In pursuit of strategic sustainable development goals, this study seeks to analyze the spatial placement of vertiports for urban air mobility, positioning them as integral components within the multimodal transportation system. We propose an iterative weighted k -means clustering method that incorporates the value of travel time of different transportation modes to identify potential vertiport locations. A multi-objective optimization model that maximizes total air taxi ridership and minimizes total facility costs and travel distances to vertiports is developed considering the constraints of the distance from vertiports to existing mobility hubs. A case study is conducted using the data of Beijing city, incorporating the modes of taxis and metro. We found that 19 vertiports can guarantee a robust performance of urban air mobility service. Users of urban air mobility can benefit from substantial travel time savings of 80% (48 min on average). This study contributes to a deeper understanding of the optimal setting of urban air mobility facilities from the perspective of network synchronization, offering useful insights into policy decision-making for sustainable infrastructure planning. • Integrated spatial placement of UAM vertiports in multimodal transportation systems. • Proposed iterative weighted k -means clustering methods incorporating value of time. • Developed a multi-objective optimization model to identify optimal vertiport locations. • Found 19 vertiports can guarantee robust performance of UAM service in Beijing. • Found air taxi users can benefit from 80% of travel time savings (about 48 minutes). [ABSTRACT FROM AUTHOR]

Urban green spaces are crucial for improving the quality of life of city residents because they provide numerous health advantages and foster overall wellness. However, previous research has concentrated only on individual goals and overlooked the inherent contradictions that arise when many objectives are considered. This study devised a transdisciplinary framework to link the optimization of plant community characteristics with multiple objectives. In Beijing, we selected 22 urban green spaces and utilized the non-dominated sorting genetic algorithm III (NSGA-III) to create a Pareto-optimal model. This model allowed us to reveal the relationships and limitations between plant community characterization factors and three objectives: temperature comfort, landscape aesthetics, and construction costs. Consequently, 91 Pareto-optimal solutions were obtained. The results indicate that a tradeoff exists between the three goals of temperature comfort, landscape aesthetics, and building costs, and this tradeoff is influenced by the interplay between the objectives and plant community characterization factors. We condensed four methodologies for building plant communities, each with distinct objective orientations, while also considering the preferences of decision-makers. The results of this study offer a novel perspective for conducting research in multi-objective scenarios to promote environmental sustainability. [Display omitted] • We revealed the relationships between plant community characteristics and multiple objectives. • We proposed a multi-objective optimization framework for plant community characteristics. • Optimal solutions are obtained based on EWM-TOPSIS and VCWM-TOPSIS methods. • We proposed recommendations for plant community design and optimization. [ABSTRACT FROM AUTHOR]

Current housing unit design focuses on performance optimization, such as building energy use, daylighting, and occupant comfort. There is significant potential for efficient optimization in the early design stage. This paper proposed a building energy demand optimization framework that considers building design variables for housing unit design in Beijing. First, a high-rise apartment building was designed using geometric and thermal envelope parameters. Second, a thermal load simulation of the housing unit was performed. Third, sensitivity analysis and multi-objective optimization were conducted to evaluate the building's performance. Finally, a neural network was trained to predict the thermal load. A comparison of the base case and optimal cases revealed that more than 20% of energy demand could be saved. In addition, the effect of the parameters on the thermal load and the lighting schedules on the unit's energy load were analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Design of an optimization framework for battery economic-conscious charging. • Costs of both electrical energy waste and battery ageing are directly considered. • Contradictory objectives along with immeasurable states and constraints are optimized. • Charging strategies towards time, energy and economic managements are obtained. • Effects of three sensitive elements are comparatively studied via Pareto frontier. Technical challenges facing the development of battery economic charging for energy management arise from various contradictory objectives, immeasurable internal states, and hard constraints. Available solutions often resort to optimizing economic charging from the perspective of power grid, while few focus on the benefits of electric vehicle (EV) owners. This paper proposes a constrained multi-objective optimization framework to achieve economy-conscious battery charging management. Specifically, a coupled electrothermal-aging model is first applied to capture the nonlinear electrical, thermal, and aging dynamics of a lithium-ion battery with different timescales. Then an economic indicator considering the total charging cost caused by both the battery aging and electrical energy loss is formulated, based on a battery resale cost model and the Beijing peak-valley time-of-use (TOU) price. In addition, two crucial but contradictory objectives including the charging time and battery average temperature are also taken into account, along with hard constraints on the battery State of Charge (SOC), charging current, terminal voltage, and temperature. The complicated multi-objective charging problem is solved by using NSGA-II to optimize the multistage constant-current (MCC) profile. Sensitivities of the cut-off voltage, convection resistance, and ambient temperature are comparatively analysed via Pareto frontier. Numerical results of two extreme cases and several balanced charging cases demonstrate that feasible, economy-conscious battery charging management for various user demands can be achieved through suitable equilibrations among charging speed and thermal variations during charging. [ABSTRACT FROM AUTHOR]

As the cornerstone of contemporary urban transit infrastructure, the metro rail transit system significantly contributes to both energy consumption and carbon emissions. Recognizing the potential of rooftop photovoltaic (PV) applications in elevated stations to mitigate the carbon footprint of the metro system, harnessing this potential becomes imperative for advancing toward a cleaner future in the metro transit sector. However, the techno-economic feasibility of the rooftop PV application in elevated metro stations has not been fully reported in existing literature. Addressing this research gap, our study delves into the impact of the photovoltaic-battery (PVB) system on energy conservation and economic benefits within the elevated stations of a metro line in Beijing, China, and a multi-objective nonlinear mixed-integer programming (MIP) is formulated under a 3-tier time-of-use tariff scheme for minimizing grid load and maximizing economic benefits. The results demonstrate that the incorporation of both the battery energy storage device and the PV subsystem leads to an 8.3% and 19.2% reduction in annualized costs, respectively. When applied across the entire metro line, the PVB system is anticipated to yield a substantial annualized cost reduction ranging from 26.1% to 47.8%. Moreover, outcomes from the multi-objective MIP optimization reveal that the simultaneous achievement of economic benefits maximization and grid load minimization is infeasible, and the weighted sum method offers a decision-making platform for investors to choose one option or another according to extra criteria. [ABSTRACT FROM AUTHOR]

This study provides an innovative application of virtual water trade in the traditional allocation of physical water resources in water scarce regions. A multi-objective optimization model was developed to optimize the allocation of physical water and virtual water resources to different water users in Beijing, China, considering the trade-offs between economic benefit and environmental impacts of water consumption. Surface water, groundwater, transferred water and reclaimed water constituted the physical resource of water supply side, while virtual water flow associated with the trade of five major crops (barley, corn, rice, soy and wheat) and three livestock products (beef, pork and poultry) in agricultural sector (calculated by the trade quantities of products and their virtual water contents). Urban (daily activities and public facilities), industry, environment and agriculture (products growing) were considered in water demand side. As for the traditional allocation of physical water resources, the results showed that agriculture and urban were the two predominant water users (accounting 54% and 28%, respectively), while groundwater and surface water satisfied around 70% water demands of different users (accounting 36% and 34%, respectively). When considered the virtual water trade of eight agricultural products in water allocation procedure, the proportion of agricultural consumption decreased to 45% in total water demand, while the groundwater consumption decreased to 24% in total water supply. Virtual water trade overturned the traditional components of water supplied from different sources for agricultural consumption, and became the largest water source in Beijing. Additionally, it was also found that environmental demand took a similar percentage of water consumption in each water source. Reclaimed water was the main water source for industrial and environmental users. The results suggest that physical water resources would mainly satisfy the consumption of urban and environment, and the unbalance between water supply and demand could be filled by virtual water import in water scarce regions. [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]

Combined cooling, heating and power (CCHP) systems have recently received considerable attention, which can meet multiple energy demands of the users. In this study, a novel CCHP system combining the organic flash cycle (OFC) and solar thermal utilization (ST) system is proposed. Two operating strategies based on the following electric load (FEL) are designed for the building complex in Beijing: the FEL strategy with the negative feedback (NFEL) and the FEL strategy considering flexibility (FFEL). The multi-objective optimization of the operating strategy is performed and solved by the genetic algorithm and the random forest algorithm. The results show that the time of the simplified model by the random forest is reduced by 94.5%. The cost of the three strategies decreases and then increases as the power rating of the internal combustion engine (ICE) increases. When the weight factors are inconsistent, the optimal power rating of the ICE increases and the optimal flash temperature decreases for different strategies. The contribution of the heating provision of the NFEL strategy is 8% higher than that of the FEL strategy. The levelized cost of the FFEL strategy is 6.1% and 2.9% lower than the FEL and NFEL strategies. • A novel CCHP system with organic flash cycle and ST system is proposed. • A FEL strategy considering flexibility are designed for the building complex. • A multi-objective optimization is performed using the genetic algorithm. • The random forest algorithm is used to simplify the OFC thermodynamic model. [ABSTRACT FROM AUTHOR]

• A novel sandwich self-heating structure is proposed to rapidly heat the battery system. • The proposed structure has negligible impact on the battery life and energy density. • Multi essential but conflicting objectives are optimized to match the battery system and fuel cell system. • A fast cold start of the fuel cell buses is achieved without relying on external energy. • The proposed method was applied to fuel cell buses for the 2022 Beijing Winter Olympic Games in batches. Hydrogen fuel cell vehicles (HFCVs), consisting of dual energy sources of fuel cell system (FCS) and lithium-ion battery system (LIBS), have become the technological frontier of electric vehicles due to the advantages of no mileage anxiety, fast hydrogen refueling, and zero emissions. However, the deterioration of battery performance at low temperatures makes it difficult to start the FCS and seriously hinders the application range of HFCVs. Herein, a sandwich self-heating structure-based LIBS is proposed to realize rapid self-heating and performance recovery of the LIBS without relying on external energy and achieve a fast cold start of the HFCV at extremely low temperatures. Furthermore, the proposed LIBS were successfully applied to all fuel cell buses in the core area of the 2022 Beijing Winter Olympic Games in batches. Firstly, compared with the mainstream heating methods, this sandwich self-heating structure has the advantages of fast heating speed (5.9 °C/min), high heating efficiency (78.75 %), and good temperature uniformity. More importantly, this structure has a small impact on the life and energy density of the LIBS. Secondly, in response to the severe road conditions in the Winter Olympic Games area, we matched the FCS and sandwich self-heating LIBS parameters based on multi-objective optimization and realized fast cold start and rapid performance improvement of fuel cell buses. Thirdly, the sandwich self-heating structure can realize the cold start of FCS with a smaller LIBS, which provides a new possibility to reduce the cost and light weight of HFCVs. Finally, the configuration is also applicable to pure electric vehicles, showing great application potential. [ABSTRACT FROM AUTHOR]

• A life-cycle evaluation framework coupled with NSGA-II and SWMM was proposed. • NSGA-II algorithm was used for automatic GI layout optimization. • SWMM was used to calculate the objective function scores of each scheme. • The optimal layout under 10-year return period was recommended. • Three preferences were provided for more convincible results. Rapid urbanization and more frequent urban floods instigated by climate change make traditional gray infrastructure become less effective and efficient. Green infrastructure (GI) have proved to be effective measures to address urban floods. Whether the GI layout can yield significant benefits and low investment cost requires further exploration. Besides, the type, size, and location of GI needs to be optimized to achieve better performance. A life-cycle evaluation framework coupled with a multi-objective optimization algorithm (NSGA-II) and SWMM was proposed for GI layout optimization. The framework took investment cost, economic-environmental-social monetization benefit, and runoff control capacity into consideration. Tongzhou District in Beijing was selected for empirical analysis. Simulation results reveled that GIs performed good in runoff control and the optimal layout under 10-year return period was recommended. The total cost and benefit of the recommended layout is 6.34×109 RMB and 8.36×106 RMB/Design rainfall, respectively, which outperforms than other scenarios. Permeable pavement accounted for the highest proportion in the optimized layout scenario. For actual construction, decision-makers should select appropriate measures according to local conditions (e.g., precipitation, land use type, cost of GIs) and choose the optimal layout scheme according to their preference. Results displayed can provide a reproducible and dependable planning scheme for future Sponge City construction in China. [Display omitted] [ABSTRACT FROM AUTHOR]

This study proposes a multi-objective optimization methodology for planning multi-energy complementary distributed energy systems considering process synergy and thermal integration. The process integration technique is integrated into the Energy Hub model to deal with the multi-process synergy and temporal source-load matching. The system design and dispatch strategy are optimized by an augmented ε-constraint method with three objectives (economics, carbon emission, and fossil fuel consumption), and then the optimal tradeoff solution is identified by the Technique for Order Preference by Similarity to an Ideal Solution. Moreover, a novel multi-energy complementary distributed energy system is developed, which includes comprehensive utilization of solar energy (photovoltaic, photothermal, and thermochemical) and middle-low temperature heat utilization technologies, as well as hybrid energy storage technologies. Finally, a case study located in Beijing is selected as an illustrated example. The obtained single-objective optimization solutions and Pareto optimal solutions are further analyzed and compared in terms of system configuration, hourly/yearly energy balance, and thermal integration condition. The results show that the multi-energy complementary distributed energy system presents an economic benefit (reducing 25% of the annual total cost) compared to a gas turbine-based integrated energy system. Considering thermal integration contributes to 5.13% of the cost reduction. The configuration of the energy storage devices will reduce 18% energy supply cost, 9% fossil fuel consumption, and 42% carbon emission with the storage devices' boundary increase from 2 MWh to 60 MWh. Moreover, the optimal design of the system provides a reference for decision-making and a basis for flexible operation. The annual total cost, carbon emission, and fossil fuel consumption of the optimal solution in the Pareto frontier are 8.19 million CNY, 2.91 kt CO 2 -eq./year, and 18.4 GWh, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new bi-level planning model is proposed that integrates the interests of both charging station investors and EV users, where the upper model has the global economic return as the optimization objective, and the lower model has the EV user's satisfaction with the charging service (charging cost) as the optimization objective. The global economic cost is optimized while the service satisfaction of the user is improved. • The time cost, economic cost, and mileage anxiety are considered together to characterize the service satisfaction of EV users. Specifically, the charging queuing time, driving distance, desired charging volume, and actual charging volume are used to reflect the charging cost of EV users and to reflect the autonomy of EV users more accurately, and a combination of fast and slow charging posts are used for planning to meet the needs of different users and grid constraints, to improve the operational efficiency of charging stations and reduce the charging queuing time of EV users. • The conventional method and the bi-level planning method are solved separately for a region of Beijing, and the effectiveness of the proposed method is verified by comparing factors such as investors' returns, users' charging costs, and queuing times. The rapid development of electric vehicles (EV) has placed greater demands on the planning and construction of public electric vehicle charging stations (EVCS). As EV users are highly autonomous in their charging behavior, the interests of investors and EV users are mutually affected and challenging to balance. Therefore, this paper proposes a bi-level planning model to balance the interests of investors and EV users and optimize global economic costs while improving the service satisfaction of users. The upper-level model aims to optimize the economic cost. In contrast, the lower-level model aims to optimize the service satisfaction of EV users and characterizes the charging satisfaction of EV users through the costs of charging queuing time, distance traveled, desired to charge volume, and actual charging volume, to more accurately reflect the autonomy of EV users. A combination of fast and slow charging piles is also used for planning to meet the needs of different users and improve charging stations' operational efficiency. Finally, a case study is conducted in an area of Beijing to verify that the optimization model has the advantages of low global economic cost, short charging queuing time for users, and high service satisfaction. [ABSTRACT FROM AUTHOR]

Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design. [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]

• Propose a multi-objective optimization and robustness analysis integrated framework. • Merge multi-objective optimization with two-stage stochastic programming. • Generate probabilistic stochastic scenarios capturing multiple uncertainties. • Identify the final optimum solutions by two decision-making methods. • Combine optimization with Monte Carlo simulation for robustness analysis. The optimal design of building integrated energy system is sensitive to the variation of uncertain parameters. For addressing the tradeoff of uncertainty and optimality-robustness, this study proposes a combined multi-objective optimization and robustness analysis framework for optimal design of building integrated energy system. The proposed framework includes two parts. In the optimization part, on the basis of scenario generation for capturing the uncertainties of renewable energy sources and energy demands, two-stage multi-objective stochastic mixed-integer nonlinear programming is conducted to optimize the system's economic and environmental objectives. Two decision-making methods are introduced to identify the final optimum solution from the obtained Pareto frontier. In the robustness-analysis part, a combined Monte Carlo simulation and optimization method is implemented to verify the robustness of the optimal solutions. The two parts of the framework are integrated to investigate the case of a hotel in Beijing, China. The results indicate that compared with the stochastic model, the deterministic model underestimates the annual total cost. Achieving economic and environmental optimum is conflicting and needs a trade-off through decision making. Moreover, in the robustness analysis, an acceptable robustness value is identified, considering both the selected objectives and the operation constraints' probability of failure. The Shannon-entropy-based final optimum solution exhibits the best comprehensive performance, with an annual total cost of $695 × 103/year, an annual carbon emissions of 2100 tons/year, and an 8.81% probability of failure. [ABSTRACT FROM AUTHOR]

Water management strategies play an important role in water shortage alleviation. This study evaluates the cost and water ecosystem benefit of 14 water management strategies in Beijing in the future scenarios for 2020 and 2035. In addition, optimal implements of abatement strategies are obtained within the context of legislated targets, with the consideration of interaction among strategies. The result shows that Beijing can meet its commitments for total water use and pollution control by the water management strategies implementation in both 2020 and 2035. For 14 water management strategies analyzed in this study, 5 options with negative abatement cost value achieve 12.2–24.1% of the total water ecosystem benefit in 2020 and 2035. Wastewater reclamation is the most efficient strategy in water ecosystem impact (WEI) reduction, which contributes 38.4% of the total WEI reduction with an abatement cost of 1.6 Yuan/m3 H 2 O -eq. However, the sequence of optimal strategy implementation rate is not in accordance with the abatement cost of the strategies. The most cost-effective option is the water-efficient shower head, while the highest implementation rate is found for promotion of production technologies. A comparison between water management optimization with and without the consideration of interactions among strategies shows that taking the interaction among strategies into account imposes almost no influence on the total WEI reduction. But it has impacts on optimal implementation rate of each water management option and the cost estimation (+10.8%) of water management implementation. Such a systematic analysis of water management strategies provides general recommendations on sustainable water resource management in water scarce regions. Image 1 • Cost and water ecosystem benefit of 14 water strategies were evaluated. • Water management options were optimized within the legislated targets. • Beijing can meet its commitments for total water use and pollutants discharge. • Sequence of strategy implement rate is not in accordance with its abatement cost. • Interaction among strategies has almost no impact on optimal WEI reduction. [ABSTRACT FROM AUTHOR]