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Municipal solid waste management planning for Xiamen City, China: a stochastic fractional inventory-theory-based approach

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2017年第31期24卷 24243-24260页

作者： Chen, Xiujuan Huang, Guohe Zhao, Shan Cheng, Guanhui Wu, Yinghui Zhu, Hua Univ Regina Environm Syst Engn Fac Appl Sci & Engn Regina SK S4S 0A2 Canada Xiamen Univ Technol Dept Environm Engn Xiamen 361024 Peoples R China Univ Regina Inst Energy Environm & Sustainabil Res Regina SK S4S 0A2 Canada Shandong Univ Sch Environm Sci & Engn Jinan 250100 Shandong Peoples R China North China Elect Power Univ SC Energy & Environm Res Acad Beijing 102206 Peoples R China

In this study, a stochastic fractional inventorytheory- based waste management planning (SFIWP) model was developed and applied for supporting long-term planning of the municipal solid waste (MSW) management in Xiamen City, the special economic zone of Fujian Province, China. In the SFIWP model, the techniques of inventory model, stochastic linear fractional programming, and mixed-integer linear programming were integrated in a framework. Issues of waste inventory in MSW management system were solved, and the system efficiency was maximized through considering maximum net-diverted wastes under various constraintviolation risks. Decision alternatives for waste allocation and capacity expansion were also provided for MSW management planning in Xiamen. The obtained results showed that about 4.24 x 10(6) t of waste would be diverted from landfills when p(i) is 0.01, which accounted for 93% of waste in Xiamen City, and the waste diversion per unit of cost would be 26.327 x 10(3) t per $10(6). The capacities of MSW management facilities including incinerators, composting facility, and landfills would be expanded due to increasing waste generation rate.

关键词： MSW management Waste inventory Stochastic linear fractional programming chance-constrained programming

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chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive

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JOURNAL OF CONTAMINANT HYDROLOGY 2017年 200卷 15-23页

作者： Ouyang, Qi Lu, Wenxi Hou, Zeyu Zhang, Yu Li, Shuai Luo, Jiannan Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Coll Environm & Resources Changchun 130021 Peoples R China Inst Water Environm Sci Songliao Fujin Rd 11-16 Changchun 130000 Peoples R China Xian Univ Sci & Technol Sch Architecture & Civil Engn Xian 710054 Peoples R China

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG;and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. (C) 2017 Elsevier B.V. All rights reserved.

关键词： chance-constrained programming Groundwater remediation Multi-algorithm method Multi-gene genetic programming Multi-objective optimization Surrogate model

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Uncertain Solid Transportation Problem with Product Blending

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2017年第6期19卷 1916-1926页

作者： Gao, Yuan Kar, Samarjit Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Natl Inst Technol Durgapur Dept Math Durgapur 713209 India

In order to satisfy the required quality of demand points in a practical transportation system of chemical products such as petroleum and gasoline, blending of products should be considered. In this paper, we have considered a solid transportation problem with product blending in an uncertain environment, in which the fixed charges of vehicles and the unit transportation costs of products are assumed to be uncertain variables. Using different criteria to rank uncertain variables, we have constructed uncertain models for this problem, namely expected cost minimization model (ECMM) and chance-constrained uncertain model (CCUM). Within the framework of uncertainty theory, we prove that both of ECMM and CCUM can be transformed into the corresponding deterministic forms. Numerical examples are presented to illustrate the models, and the results are obtained by using the standard optimization solver Gurobi.

关键词： Solid transportation problem Product blending Uncertain programming Expected value model chance-constrained programming

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On Controllability of Demand Response Resources & Aggregators' Bidding Strategies

On Controllability of Demand Response Resources & Aggregator...

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Power Systems Computation Conference

作者： K. Bruninx H. Pandzic H. Le Cadre E. Delarue Department of Mechanical Engineering KU Leuven Leuven Belgium EnergyVille Genk Belgium VITO Mol Belgium Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

European legislation encourages prosumers to generate, consume and sell self-generated electricity in local and large-scale electricity markets. Since the capacity of residential prosumers is typically insufficient to take part in wholesale markets, aggregators act as mediators. This allows prosumers to monetize the flexibility of their loads through demand response (DR). This paper analyzes the strategic behavior of a DR aggregator, responsible for the optimization of its DR providers' bids in a wholesale market. The DR resource consists of an aggregation of residential electric heating systems, governed by a physical load model and user-specified comfort constraints. The interaction between the aggregator and the DR providers is modelled via Nash Bargaining Theory. Possible deviations from the scheduled heating patterns are accounted for via chance constraints. Results show that limitedly controllable DR resources and a risk-averse aggregator may significantly reduce the monetary benefits of DR.

关键词： Aggregator Demand Response chance-constrained programming Nash Bargaining Theory

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考虑空间随机需求的急救站点选址规划

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管理科学 2020年第1期

作者：苏强杨微王秋根

随着人民生活水平的提高和人口老龄化加剧，公众对急救医疗服务的要求越来越高。为保证急救需求的响应及时性，急救站点的选址规划问题受到广泛关注。急救站点选址的依据是需求的分布，然而现有研究未能充分考虑急救需求在空间分布上的... 详细信息

随着人民生活水平的提高和人口老龄化加剧，公众对急救医疗服务的要求越来越高。为保证急救需求的响应及时性，急救站点的选址规划问题受到广泛关注。急救站点选址的依据是需求的分布，然而现有研究未能充分考虑急救需求在空间分布上的随机性影响，通常将其空间分布简化为若干个集中需求点，或将规划空间划分为若干矩形网格，然而这种需求刻画过于粗略，导致需求覆盖水平的计算不够准确，影响配置方案的有效性。针对该问题，本研究应用高斯混合模型解决了急救需求的空间分布刻画问题，创新性地提出基于高斯混合聚类的站点选址规划方法，考虑急救需求时空随机性，建立了相应的机会约束规划模型。实际数据的验证分析表明，该选址方法能够显著减少服务延误时间和次数，保证急救服务的响应及时性。

关键词：急救站点规划空间随机性高斯混合模型机会约束规划　　ambulance location planning spatial randomness Gaussian mixture model chance-constrained programming

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An MIP-Based Heuristic Approach to Determine Production Lot Size for Capacitated Single-Stage Production Processes with Stochastic Demand on Parallel Machines 6

An MIP-Based Heuristic Approach to Determine Production Lot ...

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6th International Conference on Industrial Technology and Management (ICITM)

作者： Chotayakul, Supatchaya Punyangarm, Varathorn Srinakharinwirot Univ Dept Ind Engn Ongkharak Thailand

ISBN: (纸本)9781509053308

This paper deals with the capacitated single-stage production lot sizing and scheduling problem with multiple items, setup time, stochastic demand and unrelated parallel machines. A stochastic mixed-integer linear programming model is proposed to formulate the problem. Based on the uncertain constraints, the chance constrained programming approach is used to transform them into equivalent deterministic constraints and then obtain an optimal solution by deterministic mixed-integer linear programming model. Due to the complexity of problem, the mixed-integer programming (MIP) of the equivalence deterministic model of the capacitated single-stage production lot sizing problem with multiple items, setup time, stochastic demand and unrelated parallel machines is reformulated as a shortest path reformulation problem. The proposed algorithm is evaluated through a numerical example. Computational results show that the proposed method gives optimal or near optimal solution and have good-quality result for the test problem.

关键词： component production lot size stochastic demand chance-constrained programming single-stage production process shortest path reformulation

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A new equivalent transformation for interval inequality constraints of interval linear programming

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FUZZY OPTIMIZATION AND DECISION MAKING 2016年第2期15卷 155-175页

作者： Chen, Mingzhi Wang, Sheng-Guo Wang, Paul P. Ye, Xiaoxiang Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Univ N Carolina Dept Engn Technol Charlotte NC 28223 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

In this paper, we have introduced a new approach to solve a class of interval linear programming (ILP) problems. Firstly, the novel concept of an interval ordering relation is further developed to make desired solution feasible. Secondly, according to the 3 law of normal distribution, a new equivalent transformation for constraints with the interval-valued coefficients of ILP is justified. Accordingly, the uncertainty stemmed from interval number could be replaced by the uncertainty of random variables. Consequently, the classical methodology of stochastic linear programming, a chance constrained programming model based on normal distribution is designed to work out the equivalent form of the original problem. This is because it allows us to carry out the optimization operation with a certain calibrated probability. A typical numerical example is given to illustrate how to apply equivalent transformation in order to realize ILP. Finally, we conclude this paper by elaborated comparisons among our method and selected existing solutions to advance our confidence of our research results as to their correctness and effectiveness.

关键词： Interval linear programming (ILP) Stochastic linear programming Ordering relation of intervals Modeling the interval uncertainty Variate transformation chance-constrained programming

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Interval-parameter chance-constraint programming model for end-of-life vehicles management under rigorous environmental regulations

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WASTE MANAGEMENT 2016年 52卷 180-192页

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

As the number of end-of-life vehicles (ELVs) is estimated to increase to 79.3 million units per year by 2020 (e.g., 40 million units were generated in 2010), there is strong motivation to effectively manage this fast-growing waste flow. Intensive work on management of ELVs is necessary in order to more successfully tackle this important environmental challenge. This paper proposes an interval-parameter chance-constraint programming model for end-of-life vehicles management under rigorous environmental regulations. The proposed model can incorporate various uncertainty information in the modeling process. The complex relationships between different ELV management sub-systems are successfully addressed. Particularly, the formulated model can help identify optimal patterns of procurement from multiple sources of ELV supply, production and inventory planning in multiple vehicle recycling factories, and allocation of sorted material flows to multiple final destinations under rigorous environmental regulations. A case study is conducted in order to demonstrate the potentials and applicability of the proposed model. Various constraint-violation probability levels are examined in detail. Influences of parameter uncertainty on model solutions are thoroughly investigated. Useful solutions for the management of ELVs are obtained under different probabilities of violating system constraints. The formulated model is able to tackle a hard, uncertainty existing ELV management problem. The presented model has advantages in providing bases for determining long-term ELV management plans with desired compromises between economic efficiency of vehicle recycling system and system-reliability considerations. The results are helpful for supporting generation and improvement of ELV management plans. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： End-of-life vehicle Management chance-constrained programming Interval programming Optimization Uncertainty

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Gamma distribution approach in chance-constrained stochastic programming model

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2011年第1期2011卷 1-13页

作者： Atalay, Kumru D. Apaydin, Aysen Fac Med Deptment Med Educ TR-06490 Ankara Turkey Fac Sci Dept Stat TR-06100 Ankara Turkey

In this article, a method is developed to transform the chance-constrained programming problem into a deterministic problem. We have considered a chance-constrained programming problem under the assumption that the random variables a(ij) are independent with Gamma distributions. This new method uses estimation of the distance between distribution of sum of these independent random variables having Gamma distribution and normal distribution, probabilistic constraint obtained via Essen inequality has been made deterministic using the approach suggested by Polya. The model studied on in practice stage has been solved under the assumption of both Gamma and normal distributions and the obtained results have been compared.

关键词： chance-constrained programming Essen inequality Gamma distribution

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A chance-constrained Optimization Model for Determining Renewables Penetration Limit in Power Systems

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第7期44卷 701-712页

作者： Xiang, Yue Liu, Youbo Liu, Junyong Yang, Wei Sichuan Univ Sch Elect Engn & Informat 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

In this article, an optimization model determining renewables penetration limit in power systems is presented. The penetration limit is defined as the enabling renewables output with quantified maximum capacity avoiding the violation of power system operation constraints. Thus, an optimal power flow (OPF)-based model with chance constraints is built and a framework including a Monte Carlo-based genetic algorithm is designed. Moreover, a transient stability verification and correction strategy based on trajectory sensitivity is proposed and modularized in the extended framework. The feasibility of the proposed methodology is verified using several test scenarios, and some related factors are investigated as well. The results indicate that renewables penetration limit can be increased by improving those studied factors.

关键词： renewable generation penetration limit chance-constrained programming transient stability constraint optimization

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