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An inexact stochastic-fuzzy jointed chance-constrained programming for regional energy system management under uncertainty

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ENGINEERING OPTIMIZATION 2015年第6期47卷 788-804页

作者： Liu, Zhengping Huang, Guohe Li, Wei North China Elect Power Univ China Canada Energy & Environm Res Ctr Beijing Peoples R China Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada

Environmental problems associated with socio-economic development have been a growing concern facing many regional and/or national authorities. However, effective planning may encounter difficulties since uncertainties existing in a number of impact factors and pollution-related processes are often not well acknowledged and reflected. Combining chance-constrained programming and fuzzy credibility-constrained programming with interval parameters and stochastic programming, this study advances an inexact stochastic-fuzzy jointed chance-constrained programming method for planning regional economic and environmental systems under multiple uncertainties presented as intervals, fuzzy sets and probability distributions. The developed method has been applied to a case of long-term energy management system with multiple energy resources and three communities. Emissions of sulphur dioxide and nitrogen oxides are controlled and capacity expansion is scheduled. The results can help to identify desired alternatives for planning regional development strategies, where compromised schemes are provided under an integrated consideration of economic efficiency and environmental protection under multiple uncertainties.

关键词： decision making energy system environment chance-constrained programming fuzzy credibility-constrained programming uncertainty

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Archimedean Copulas in Joint chance-constrained programming 3

Archimedean Copulas in Joint Chance-Constrained Programming

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3rd International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Houda, Michal Lisser, Abdel Univ Paris 11 Rech Informat Lab F-91405 Orsay France

ISBN: (纸本)9783319175096;9783319175089

We investigate the problem of linear joint probabilistic constraints with normally distributed constraints in this paper. We assume that the rows of the constraint matrix are dependent, the dependence is driven by a convenient Archimedean copula. We describe main properties of the problem, show how dependence modeled through copulas translates to the model formulation, and prove that the resulting problem is convex for a sufficiently high probability level. We further develop an approximation scheme for this class of stochastic programming problems based on second-order cone programming.

关键词： chance-constrained programming Dependence Archimedean copulas Second-order cone programming

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A chance-constrained programming Model for Medical Resources Order and Distribution Scheduling Based on Time-Space Network

A Chance-Constrained Programming Model for Medical Resources...

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Zhang, Huixiang Liu, Ming Nanjing Univ Sci & Technol Dept Management Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479918911

This paper studies the medical resources order and distribution scheduling problem with stochastic demand. A chance-constrained programming model is proposed based on time-space network with the objective of minimizing the hospital's total operating costs. Generic algorithm is employed to solve the optimization model. The numerical test results show the good performance of the proposed method.

关键词： time-space network medical resources chance-constrained programming generic algorithm

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Sustainable and reliable design of autonomous driving lanes: A chance-constrained extended goal programming approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Chen, Yu Wang, Wei Hua, Xuedong Wang, David Z. W. Wang, Jian Southeast Univ Sch Transportat 02 Si Pai Lou Nanjing 211189 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

As autonomous vehicles (AVs) advance and gradually integrate into urban roadways, managing traffic in mixed environments, where human-driven vehicles (HVs) coexist with AVs, has become a critical challenge. Previous research has developed several optimal network design models for autonomous driving lanes (ADLs) to partially address this issue. However, these studies largely overlook uncertainties in AV market share or travel demand, which may lead to either excessive investment in underutilized infrastructure or traffic inefficiencies with insufficient capacity. To address these challenges, this study introduces a chance constrained programming (CCP) approach with sample approximation to effectively incorporate such uncertainties while accommodating varying risk preferences. To harmonize various sustainable development goals, such as network efficiency and social equity, CCP is further combined with extended goal programming, forming the proposed chance-constrained extended goal programming (CCEGP) model in this study for sustainable and reliable ADL design. Additionally, the routing behaviors of HV and AV users, each with differing levels of traffic awareness, are unified within a mixed crossnested logit-based stochastic user equilibrium model, thereby enhancing behavioral realism and model generalizability. The heuristic coati optimization algorithm is modified for solving, and case studies are conducted to validate its applicability.

关键词： Autonomous Vehicle Autonomous Driving Lane Uncertain AV Market Share chance-constrained programming chance-constrained Extended Goal programming Social Equity

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Design of Energy Dispatch Strategy of Active Distribution Network Using chance-constrained programming

Design of Energy Dispatch Strategy of Active Distribution Ne...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xie Hua Chen Yuxi Wang Jian Agelidis, Vassilios Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Collaborat Innovat Ctr Vehicles Beijing Beijing Peoples R China Univ New S Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

ISBN: (纸本)9781467381321

Active distribution network plays an important role in increasing the utilization level of the renewable energy and realizing the flexible intelligent distribution network management. In this paper, aiming to improve the economy and reliability of the active distribution network, the energy dispatch strategy is designed by the method of the chance-constrained programming. Considering the uncertainty of the photovoltaic power generation and wind power generation, the random simulation technique and the penalty function method are selected to build the energy scheduling mathematical model. The operation constraints are set for wind turbines, photovoltaic power generation unit, the active power energy storage devices and the exchange power between the superior power system and the active distribution network. The energy dispatch strategy is given by the improved particle swarm algorithm. An actual system in a certain area is chosen to verify the effectiveness of the proposed method in this paper.

关键词： active distribution network chance-constrained programming random simulation technique improved particle swarm algorithm

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Type-2 fuzzy chance-constrained linear fractional programming model for a water resource management system: A case study of Taiyuan city, China

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JOURNAL OF WATER AND CLIMATE CHANGE 2023年第11期14卷 4121-4145页

作者： Wu, Wenyu Zhao, Xuehua Zhang, Xueyou Yao, Liushan Liu, Xin Taiyuan Univ Technol Coll Water Resources Sci & Engn Taiyuan 030024 Peoples R China

Uncertainties arising from extreme climate events and human activities pose a challenge to the efficient allocation of water resources. In this study, a type-2 fuzzy chance-constrained linear fractional programming (T2F-CCLFP) is developed to support the water resource management system under uncertainty by incorporating type-2 fuzzy sets, chance-constrained programming, and fractional programming into a comprehensive multi-objective optimization framework. The model enables the trade-off between economic, social, and environmental sustainability and provides water supply solutions associated with different levels of fuzzy uncertainty and risk of violating constraints. The T2F-CCLFP model is applied to Taiyuan, Shanxi Province, China, to support its water resource management. Results reveal that: (i) the industrial structure is transitioning toward diverse industries from energy and heavy industry dominance;(ii) external water transfer will be the major water-supply sources for the city in the future, accounting for 55 and 50% of the total water supply in 2025 and 2030, respectively;(iii) the water-supply security of the city is enhanced by provoking the utilization of reclaimed water (the annual growth rate is 13.9%). The results are helpful for managers in adjusting the current industry structure, enhancing water supply security, and contributing to the sustainable development of socio-economic and water systems.

关键词： chance-constrained programming fractional programming type-2 fuzzy sets uncertainty water-resource allocation

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COTS products selection using fuzzy chance-constrained multiobjective programming

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APPLIED INTELLIGENCE 2015年第4期43卷 732-751页

作者： Mehlawat, Mukesh Kumar Gupta, Pankaj Univ Delhi Dept Operat Res Delhi 110007 India

In this paper, we study the commercial off-the-shelf (COTS) products selection problem in COTS-based modular software systems with fuzzy parameters using chance-constrained multiobjective programming. The criteria used in this work are total cost, size, execution time, software reliability, delivery time, and compatibility issues among available COTS products. The COTS selection model presented herein simultaneously minimizes the total cost, size, and execution time of the modular software system at a credibility that is no less than pre-determined confidence levels. We propose a hybrid approach that combines the technique for order preference by similarity to ideal solution (TOPSIS) and the compensatory fuzzy approach based on the "fuzzy and" aggregation operator. TOPSIS is used to reduce the multiobjective problem with conflicting and non-commensurable objective functions to a bi-objective problem with conflicting but commensurable objective functions. The resulting bi-objective problem is solved using the compensatory fuzzy approach. The obtained compromise solutions are both compensatory and pareto-optimal for the multiobjective COTS products selection problem. A small-scale real-life case study of modular software development is presented to demonstrate the applicability of the proposed model and solution approach in real-life applications of COTS selection. A detailed performance analysis and comparison with some existing related fuzzy programming approaches are performed using distance measures to illustrate the superiority of the proposed hybrid interactive fuzzy-programming approach over existing approaches.

关键词： COTS selection chance-constrained programming Credibility theory Fuzzy sets Multiobjective optimization

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Proportional Fair Resource Allocation Based on chance-constrained programming for Cognitive OFDM Network

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WIRELESS PERSONAL COMMUNICATIONS 2014年第2期79卷 1591-1607页

作者： Xu, Lei Lv, Tong-ming Li, Qian-mu Yang, Yu-wang Li, Ya-ping Zhang, Xiao-fei Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Engn Nanjing Jiangsu Peoples R China

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS-PU couldn't be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.

关键词： Cognitive OFDM network Proportional fair resource allocation chance-constrained programming Hybrid immune optimization algorithm

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An inexact inventory-theory-based chance-constrained programming model for solid waste management

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2014年第8期28卷 1939-1955页

作者： Chen, XiuJuan Huang, GuoHe Suo, MeiQin Zhu, Hua Dong, Cong Univ Regina Environm Syst Engn Program Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Hebei Univ Engn Sch Urban Construct Handan 056038 Hebei Peoples R China North China Elect Power Univ SC Energy & Environm Res Acad Beijing 102206 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada North China Elect Power Univ Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China

In this study, an inexact inventory-theory-based chance-constrained programming (IICP) model is proposed for planning waste management systems. The IICP model is derived through introducing inventory theory model into a general inexact chance-constrained programming framework. It can not only tackle uncertainties presented as both probability distributions and discrete intervals, but also reflect the influence of inventory problem in decision-making problems. The developed method is applied to a case study of long-term municipal solid waste (MSW) management planning. Solutions of total waste allocation, waste allocation batch and waste transferring period associated different risk levels of constraint violation are obtained. The results can be used to identify inventory-based MSW management planning with minimum system cost under various constraint-violation risks. Compared with the ICP model, the developed IICP model can more actually reflect the complexity of MSW management systems and provide more useful information for decision makers.

关键词： Inventory Uncertainty Interval chance-constrained programming Waste management

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Adaptive Sampling Detection Based Immune Optimization Approach and Its Application to chance constrained programming 8

Adaptive Sampling Detection Based Immune Optimization Approa...

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8th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yang, Kai Zhang, Zhuhong Guizhou Univ Coll Comp Sci Guiyang 550025 Peoples R China Guizhou Univ Coll Elect & Informat Guiyang 550025 Peoples R China

ISBN: (纸本)9783319122854

This work investigates a bio-inspired adaptive sampling immune optimization algorithm to solve linear or nonlinear chance-constrained optimization problems without any noisy information. In this optimizer, an efficient adaptive sampling detection scheme is developed to detect individual's feasibility, while those high-quality individuals in the current population can be decided based on the reported sample-allocation scheme;a clonal selection-based time-varying evolving mechanism is established to ensure the evolving population strong population diversity and noisy suppression as well as rapidly moving toward the desired region. The comparative experiments show that the proposed algorithm can effectively solve multi-modal chance-constrained programming problems with high efficiency.

关键词： chance-constrained programming immune optimization feasibility detection sample-allocation multimodality

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