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A queueing theory-based inexact stoc...

Wang, Dan.

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A queueing theory-based inexact stochastic optimization approach for municipal solid waste management under uncertainty.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A queueing theory-based inexact stochastic optimization approach for municipal solid waste management under uncertainty./
作者:	Wang, Dan.
面頁冊數:	150 p.
附註:	Source: Masters Abstracts International, Volume: 46-03, page: 1689.
Contained By:	Masters Abstracts International46-03.
標題:	Engineering, Environmental. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR33559
ISBN:	9780494335598

A queueing theory-based inexact stochastic optimization approach for municipal solid waste management under uncertainty.
Wang, Dan.

A queueing theory-based inexact stochastic optimization approach for municipal solid waste management under uncertainty. - 150 p.

Source: Masters Abstracts International, Volume: 46-03, page: 1689.

Thesis (M.A.Sc.)--The University of Regina (Canada), 2007.

In this thesis, a queueing theory-based inexact stochastic optimization (QISO) approach is proposed to support municipal solid waste (MSW) management under uncertainty. Issues of capacity planning for waste management facilities and allocation for waste flows are addressed through the developed QISO. The overall cost for sizing and operating the waste-management facilities and transporting the generated waste flows is minimized. Based on the queueing theory, information of waste generation distributions can be taken into account. The reliability of satisfying the system constraints under uncertainty can also be analyzed. Thus the costs of potentially violating the constraints with different risk levels can also be quantified.

ISBN: 9780494335598Subjects--Topical Terms:

783782
Engineering, Environmental.

A queueing theory-based inexact stochastic optimization approach for municipal solid waste management under uncertainty.
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245 1 2 $a A queueing theory-based inexact stochastic optimization approach for municipal solid waste management under uncertainty.
300 $a 150 p.
500 $a Source: Masters Abstracts International, Volume: 46-03, page: 1689.
502 $a Thesis (M.A.Sc.)--The University of Regina (Canada), 2007.
520 $a In this thesis, a queueing theory-based inexact stochastic optimization (QISO) approach is proposed to support municipal solid waste (MSW) management under uncertainty. Issues of capacity planning for waste management facilities and allocation for waste flows are addressed through the developed QISO. The overall cost for sizing and operating the waste-management facilities and transporting the generated waste flows is minimized. Based on the queueing theory, information of waste generation distributions can be taken into account. The reliability of satisfying the system constraints under uncertainty can also be analyzed. Thus the costs of potentially violating the constraints with different risk levels can also be quantified.
520 $a The developed method is then applied to a hypothetical case to show the detailed procedures for solving a MSW management problem. It is demonstrated that the developed method is effective in tackling complex uncertainties in a MSW management system. Subsequently, the method is applied to the City of Regina for supporting long-term MSW management and planning. Three scenarios are analyzed based on different waste-management policies. Scenario 1 is based on a relatively conservative policy for waste minimization and diversion; the flow to the landfill would be decreasing along with time while those to composting and recycling facilities would keep increasing, such that the obtained plan could satisfy the required diversion goal. Scenarios 2 and 3 are based on a relatively aggressive policy for waste minimization and diversion, with the final diversion goal by the end of planning period being 55%. Each facility is expanded in each period in scenario 2, while MRF and composting facility are expanded only once in scenario 3. Correspondingly, a range of decision alternatives will be generated under the three scenarios. These will support extensive analyses of the tradeoffs between environmental and economic objectives under various system conditions.
520 $a Compared with the previous optimization methods that deal with uncertainties, the developed method has four advantageous features. Firstly, it can tackle uncertainties expressed as both probability distributions and interval values. Secondly, more complexities of waste generation rate can be taken into account. Thirdly, a linkage to preregulated governmental policies is provided in the optimization process. Finally, the attempt to consider both facility sizing and waste-flow allocation through integration with the queueing theory represents an improvement for dynamic planning of waste management systems.
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