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3rd International Conference on Energy, Environment and Sustainable Development (EESD 2013)

作者： Zhang, Hongliang Guangdong Elect Power Res Inst Guangzhou 510600 Guangdong Peoples R China

ISBN: (纸本)9783037859735

In this study, an interval-parameter programming method has been used for urban vehicle emissions management under uncertainty. The model improves upon the existing optimization methods with advantages in uncertainty reflection, system costs and limitation of emission. Moreover, the model is applied to a case study of urban vehicle emissions management in a virtual city. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.

关键词： traffic system vehicle emissions uncertainty interval-parameter programming

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A grey-forecasting interval-parameter mixed-integer programming approach for integrated electric-environmental management-A case study of Beijing

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ENERGY 2013年 63卷 334-344页

作者： Wang, Xingwei Cai, Yanpeng Chen, Jiajun Dai, Chao Beijing Normal Univ Sch Environm Minist Educ Key Lab Water & Sediment Sci Beijing 100875 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 7H9 Canada Peking Univ Dept Environm Sci Beijing 100871 Peoples R China

In this study, a GFIPMIP (grey-forecasting interval-parameter mixed-integer programming) approach was developed for supporting IEEM (integrated electric-environmental management) in Beijing. It was an attempt to incorporate an energy-forecasting model within a general modeling framework at the municipal level. The developed GFIPMIP model can not only forecast electric demands, but also reflect dynamic, interactive, and uncertain characteristics of the IEEM system in Beijing. Moreover, it can address issues regarding power supply, and emission reduction of atmospheric pollutants and GHG (greenhouse gas). Optimal solutions were obtained related to power generation patterns and facility capacity expansion schemes under a series of system constraints. Two scenarios were analyzed based on multiple environmental policies. The results were useful for helping decision makers identify desired management strategies to guarantee the city's power supply and mitigate emissions of GHG and atmospheric pollutants. The results also suggested that the developed GFIPMIP model be applicable to similar engineering problems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： interval-parameter programming Mixed-integer programming Grey-forecasting model Integrated electric-environmental management Beijing Uncertainties

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An interval-parameter mixed integer multi-objective programming for environment-oriented evacuation management

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2010年第5期41卷 547-560页

作者： Wu, C. Z. Huang, G. H. Yan, X. P. Cai, Y. P. Li, Y. P. Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Wuhan Univ Technol Engn Res Ctr Transportat safety MOE Wuhan 430063 Hubei Peoples R China Peking Univ Coll Urban & Environm Sci Beijing 100871 Peoples R China

Large crowds are increasingly common at political, social, economic, cultural and sports events in urban areas. This has led to attention on the management of evacuations under such situations. In this study, we optimise an approximation method for vehicle allocation and route planning in case of an evacuation. This method, based on an interval-parameter multi-objective optimisation model, has potential for use in a flexible decision support system for evacuation management. The modeling solutions are obtained by sequentially solving two sub-models corresponding to lower- and upper-bounds for the desired objective function value. The interval solutions are feasible and stable in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving decision makers' estimates under different conditions. The resulting model can be used for a systematic analysis of the complex relationships among evacuation time, cost and environmental considerations. The results of a case study used to validate the proposed model show that the model does generate useful solutions for planning evacuation management and practices. Furthermore, these results are useful for evacuation planners, not only in making vehicle allocation decisions but also for providing insight into the tradeoffs among evacuation time, environmental considerations and economic objectives.

关键词： evacuation management interval-parameter programming optimisation uncertainty multi-objective

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interval-parameter Conditional Value-at-Risk Two-Stage Stochastic programming Model for Management of End-of-Life Vehicles

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ENVIRONMENTAL MODELING & ASSESSMENT 2019年第5期24卷 547-567页

作者： Simic, Vladimir Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11000 Serbia

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

关键词： End-of-life vehicle Risk control Conditional value-at-risk Two-stage stochastic programming interval-parameter programming Uncertainty

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interval-parameter semi-infinite programming model for used tire management and planning under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 113卷 487-501页

作者： Simic, Vladimir Dabic-Ostojic, Svetlana Bojovic, Nebojsa Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11000 Serbia

In the European Union, approximately 3.6 million tonnes of used tires are generated annually. Because used tires are not biodegradable, there is strong motivation to successfully manage this fast-growing waste flow. One of the most popular approaches for sustainable environmental stewardship of used tires is retreading. The problem investigated in this paper is to identify optimized purchasing, retreading, and inventory planning schemes of used tires in multiple tire retreading plants as well as allocation patterns of retreaded, reusable, and end-of-life tires that secure maximized profit within a multi-period planning horizon and under multiple uncertainties. This paper proposes an interval-parameter semi-infinite programming model for used tire management and planning. The underlying difference between this model and those developed in previous research is its ability to consider the effects of external impact factors related to complicated economic, environmental, and social activities on used tire management systems. Moreover, it can successfully handle real-life uncertainties of the used tire management systems expressed as functional and crisp intervals. A numerical example is provided to demonstrate the usefulness of the developed model. Flexible long-term purchasing, retreading, inventory, and allocation plans, which are adjustable with variations of external impact factors, are obtained. The presented model has advantages in addressing the dynamic complexity of used tire management systems by introducing the functional interval parameters associated with the price of a new tire as well as electricity and gas prices and labor costs in waste management and transportation sectors. Compared with the available models, the resulting solutions are far more robust because they are able to satisfy all possible levels of external impact factors. The presented model is beneficial for the tire retreading industry, which processes millions of used tires annually. (C) 20

关键词： Used tires Functional interval Semi-infinite programming interval-parameter programming Uncertainty

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interval-parameter chance-constrained programming model for uncertainty-based decision making in tire retreading industry

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JOURNAL OF CLEANER PRODUCTION 2017年第Nov.20期167卷 1490-1498页

作者： Simic, Vladimir Dabic-Ostojic, Svetlana Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11000 Serbia

Tire retreading is an economically attractive and environmentally beneficial process for extending the lifetime of used tires. It reduces the negative influence of used tires on the environment as well as natural resources exploitation. According to the latest data, 4.7 million units of used truck tires were retreaded in 2014 in Europe. This paper proposes an interval-parameter chance-constrained programming model for uncertainty-based decision making in tire retreading industry. A case study is conducted in order to demonstrate its potentials and applicability. When constraints violation level increases (0.01, 0.05 and 0.10), the system profit obtained in the analyzed nine-year planning horizon correspondingly raises ((sic)[35.08, 82.03] x 10(6), (sic)[38.94, 90.78] x 10(6) and (sic)[40.87, 95.16] x 10(6) respectively). The uncertainty degree of the objective function under three considered system violation levels is around 80%, which demonstrates that used tires retreading management systems are highly uncertain. Profit per tonne of processed used tires is in (sic)[1594.64, 3730.84] interval. The developed model can efficiently handle uncertainties expressed as interval values and probability distributions. It can address multiple interrelationships among numerous components of used tires retreading management systems. The proposed model can examine various admissible risk levels of violating retreading capacities. It can provide a trade-off between economic efficiency of tires retreading management systems and capacity constraints violations. Compared with the conventional chance-constrained programming approach, the formulated model can incorporate much more uncertain information. The presented model can provide valuable support for tire retreading managers in avoiding inferior or simply wrong decisions. In fact, it can help tire retreading managers optimize long-term purchasing, retreading and inventory planning schemes, and allocation patterns of retreaded, re

关键词： Used tires Retreading Management Chance-constrained programming interval-parameter programming Uncertainty

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A copula-based interval-bistochastic programming method for regional water allocation under uncertainty

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AGRICULTURAL WATER MANAGEMENT 2019年 217卷 154-164页

作者： Chen, Shu Xu, Jijun Li, Qingqing Tan, Xuezhi Nong, Xizhi Changjiang River Sci Res Inst Water Resources Dept Wuhan Hubei Peoples R China Sun Yat Sen Univ Dept Water Resources & Environm Guangzhou Guangdong Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan Hubei Peoples R China

In regional water resources allcoation problems, the uncertainties of hydrological variables and socio-economic parameters are bringing huge challenges to water managers. So far, the stochastic programming models have difficulty in addressing uncertain problems, in which there are two correlated random variables. In order to address regional water resources allocation under two hydrological random variables and interval parameters, a couple-based interval-bistochastic programming (CIBSP) method is proposed. The CIBSP method includes several key steps: first, the marginal distributions of the two hydrological variables are analyzed and hydrological scenarios are set;then, the copula is chosen to analyze joint probability distribution of the two hydrological variables and the joint probabilities of scenarios are calculated;last, an interval-bistochastic model is formulated by incorporating two-stage stochastic programming, interval-parameter programming, and bivariate joint distribution. The CIBSP method can describe the probability of occurrence of a hydrological scenario and then make an optimized water allocation scheme under uncertainties. To demonstrate its applicability, the CIBSP method is applied to the Zhanghe Irrigation District located in China, to optimize available water allocation under the uncertainties of the annual inflow volume of the Zhanghe Reservoir and annual rainfall volume in the irrigated district. An appropriate water allocation plan can be obtained by the method, which provides a foundation to water managers for managing water resources in Zhanghe Irrigation District. Moreover, other two hypothetical situations named independence hypothetical situation and linear relationship hypothetical situation are discussed. The difference in the results between the two hypothetical situations and the case study indicates that an appropriate description of the joint probability of the annual inflow volume and annual rainfall volume by using CIBSP method

关键词： Copula interval-parameter programming Bivariate joint distribution Two-stage stochastic programming Uncertainty Water allocation

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An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2015年第4期22卷 2964-2975页

作者： Li, W. Wang, B. Xie, Y. L. Huang, G. H. Liu, L. N China Elect Power Univ Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Dalhousie Univ Dept Civil & Resource Engn Halifax NS B3H 4R2 Canada

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

关键词： Two-stage stochastic programming interval-parameter programming Risk-aversion Water allocation Uncertainty

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An inexact two-stage fractional energy systems planning model

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ENERGY 2018年 160卷 275-289页

作者： Song, Tangnyu Huang, Guohe Zhou, Xiong Wang, Xiuquan Univ Regina Environm Syst Engn Program Regina SK S4S 0A2 Canada Univ Prince Edward Isl Sch Climate Change & Adaptat Charlottetown PE C1A 4P3 Canada

In this study, an inexact two-stage fractional energy systems planning model (ITF-ESP) is developed through an integration of interval-parameter programming (IPP), two-stage stochastic programming (TSP), fractional programming (FP), and mixed integer linear programming (MILP) methods. Since the proposed model could not be solved directly, it is converted into two interactive sub-models. In order to obtain more precise interval solutions, the sub-model corresponding to f(-) is solved first. The developed ITF-ESP model is considered as an efficient approach to address dual-objective optimization problems involving capacity expansion issues and policy scenario analysis, as well as uncertainties described as intervals and probability distributions. Effectiveness of the ITF-ESP model is demonstrated through a case study within a Chinese context. The results indicate that although the non-renewable technologies would still play a major role in electricity generation, the renewable technologies are becoming increasingly significant. Comparisons of the ITF-ESP model and the least-cost model are conducted to illustrate the advantages of the proposed ITF-ESP model in reflecting trade-offs between economic development and environmental protection. In addition, compared with the chance-constrained two-stage fractional optimization model (CTFO), interval solutions obtained from the ITF-ESP model can provide multiple alternative management plans in terms of electricity generation and capacity expansion. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Two-stage programming Linear fractional programming interval-parameter programming Energy systems planning Uncertainties

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An interval-parameter two-stage optimization model for CO_(2) collection,distribution,transportation,utilization,and storage planning

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Advances in Climate Change Research 2021年第6期12卷 828-847页

作者： ZHAI Ming-Yang JIA Hai-Feng Michela CATENA LI Qi-An School of Environment Tsinghua UniversityBeijing100084China International Joint Laboratory on Low Carbon Clean Energy Innovation Ministry of EducationBeijing100084China

Geological carbon dioxide (CO_(2)) utilization and storage have been widely recognized as one of the important options to deliver greenhouse gas emissions reduction. Reasonable planning is critical to promote CO_(2) utilization and storage. However, CO_(2) emissions gas collection exhibits a stochastic probability distribution, and CO_(2) utilization and storage features fluctuation demands, which have gone beyond current determine planning techniques. To fulfill the current research gap, this study develops an interval-parameter two-stage programming-based CO_(2) collection, distribution, transportation, utilization, and storage optimization model, integrating interval parameter planning and two-stage planning into a general framework. Therefore, the model can address uncertainties expressed as random probabilistic distributions and discrete intervals, tackle dynamic facilities capacity expansion issues, develop optimal predefined CO_(2) distribution policy, and generate recourse schemes to address gas shortage or gas surplus issues. The model is examined by a typical hypnotical case study in China. The results revealed that the model could generate a set of first-stage reasonable CO_(2) distribution and facilities capacity expansion schemes to maximum system beneﬁts and the highest feasibility. Besides, a set of two-stage CO_(2) outsourcing purchases and facilities capacity expansion in reserve storage regions solutions were also generated to address the gas oversupplies and shortage issues. The modeling approach enriches the current CO_(2) utilization and storage distribution research content under multiple uncertainties.

关键词： CO_(2)gas collection Transportation CO_(2)geological utilization and storage Supply and distribution planning interval-parameter programming Two-stage programming Uncertainties

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