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A Facility Location Model for MSW Management Systems Under Uncertainty: A Case Study of Nashik City, India.
- Source :
- Procedia Environmental Sciences; 2016, Vol. 35, p90-100, 11p
- Publication Year :
- 2016
-
Abstract
- In India, a substantial fraction (∼0.85) of total expenditure on MSW management is spent on collection of waste. Nevertheless, the average waste collection efficiency is about 70% in Indian urban areas. Waste collection efficiency can be increased by utilising staff and collection vehicles effectively with transfer stations (TSs), though choosing best sites (in an economically optimal manner) for TSs siting is a major task. Also, many parameters (e.g., waste-generation rate, running cost of facilities, transportation cost, revenues etc.) in MSW management system are associated with uncertainties. To study the apportionment of these uncertain input parameters into the results, a comprehensive sensitivity analysis has been performed. Furthermore, the effect of interaction among most sensitive parameters is examined through design of experiment. Also, a facility location two stage stochastic model has been proposed to select the best sites for TSs siting, which is basically a framework of facility location problems involving uncertainty. The developed model is applied on the city of Nashik, Maharashtra, India. The model has 9186 constraints and 8152 variables, which has been written in AMPL (a mathematical programming language). KNITRO 5.2 (nonlinearinterior-point trust region optimizer) have been used to solve the developed model. Sensitivity analysis abduces waste generation as the most sensitive kind of parameter followed by the unit transportation cost of waste from source to TSs. Furthermore, multivariate SA identifies the importance of a facility location framework involving uncertainty. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18780296
- Volume :
- 35
- Database :
- Supplemental Index
- Journal :
- Procedia Environmental Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 117268836
- Full Text :
- https://doi.org/10.1016/j.proenv.2016.07.052