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An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China
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作者 Yi XU Shunze WU Hongkuan ZANG Guiguang HOU 《Frontiers of Environmental Science & Engineering》 SCIE EI CAS CSCD 2014年第2期239-255,共17页
Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and p... Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming (IJMP) method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city's existing waste-management practices and the long- term planning of the city's waste-management facilities. 展开更多
关键词 interval analysis mixed integer joint prob- abilistic constraint planning uncertainty waste manage- ment
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On Ideal Convergence of Double Sequences in Probabilistic Normed Spaces
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作者 Vijay KUMAR Bernardo LAFUERZA-GUILLÉN 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2012年第8期1689-1700,共12页
The notion of ideal convergence is a generalization of statistical convecgence which has been intensively investigated in last few years. For an admissible ideal ∮ C N × N, the aim of the present paper is to int... The notion of ideal convergence is a generalization of statistical convecgence which has been intensively investigated in last few years. For an admissible ideal ∮ C N × N, the aim of the present paper is to introduce the concepts of ∮-convergence and :∮*-convergence for double sequences on probabilistic normed spaces (PN spaces for short). We give some relations related to these notions and find condition on the ideal ∮ for which both the notions coincide. We also define ∮-Cauchy and :∮*- Cauchy double sequences on PN spaces and show that ∮-convergent double sequences are ∮-Cauchy on these spaces. We establish example which shows that our method of convergence for double sequences on PN spaces is more general. 展开更多
关键词 Ideal convergence double sequence statistical convergence continuous t-norm and prob- abilistic normed spaces
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