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Advances in Operations Research
Volume 2017 (2017), Article ID 2912483, 14 pages
https://doi.org/10.1155/2017/2912483
Research Article

Planning Solid Waste Collection with Robust Optimization: Location-Allocation, Receptacle Type, and Service Frequency

1Department of Industrial and Manufacturing Systems Engineering, University of Missouri, Columbia, MO 65211, USA
2Harry S. Truman School of Public Affairs, University of Missouri, Columbia, MO 65211, USA

Correspondence should be addressed to Ronald G. McGarvey

Received 8 August 2016; Revised 22 November 2016; Accepted 7 December 2016; Published 29 January 2017

Academic Editor: Mhand Hifi

Copyright © 2017 Maryam Nikouei Mehr and Ronald G. McGarvey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Consider the problem faced by a purchaser of solid waste management services, who needs to identify waste collection points, the assignment of waste generation points to waste collection points, and the type and number of receptacles utilized at each collection point. Receptacles whose collection schedule is specified in advance are charged a fixed fee according to the number of times the receptacle is serviced (emptied) per week. For other receptacles, the purchaser pays a fee comprised of a fixed service charge, plus a variable cost that is assessed on a per-ton-removed basis. We develop a mathematical programming model to minimize the costs that the purchaser pays to the waste management provider, subject to a level of service that is sufficient to collect all of the purchaser’s required waste. Examining historical data from the University of Missouri, we observed significant variability in the amount of waste serviced for nonscheduled receptacles. Because this variability has a significant impact on cost, we modified our model using robust optimization techniques to address the observed uncertainty. Our model’s highly robust solution, while slightly more expensive than the nonrobust solution in the most-optimistic scenario, significantly outperforms the nonrobust solution for all other potential scenarios.