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Optimization Models for Long-Term Planning of Municipal Solid Waste Management Systems: A Review with An Emphasis on Mass Balances

M. K. Korucu1* and I. Kucukoglu2

  1. Department of Environmental Engineering, Bursa Technical University, Bursa 16310, Turkey
  2. Department of Industrial Engineering, Bursa Uludag University, Bursa 16059, Turkey

*Corresponding author. Tel.: +90-534-704-75-04; fax: +90-224-300-34-19. E-mail address: (M. K. Korucu).


The vast majority of decision-making approaches used for long-term planning of municipal solid waste management systems (LPMSWMS) are ground on scenario-based structures. However, the scenario-based structures may overlook many real-world possibilities because of their restricted mass balances. This study is the first attempt to review the current state of optimization models, which are used as a decision-making approach for LPMSWMS, by focusing on the mass balances. In line with this purpose, 146 peer-reviewed articles were examined based on a new literature evaluation scheme. According to the findings, it can be stated that a significant majority of the articles offer non-deterministic optimization models dealing with the uncertain nature of the LPMSWMS problems. Considering all optimization models examined in the study, most of the model formulations have linear mathematical forms in terms of objective and constraint functions. However, it is quite interesting that none of the models produced solutions for a management system alternative with an integrated (non-restricted) mass balance. Accordingly, it is very questionable whether the results obtained from the current models have the power to give the most suitable solution for an up-to-date management system. As a result of the review, it is highly recommended that the optimization models to be conducted for the LPMSWMS in the future should search for new mathematical approaches considering the integrated mass balances under certainty and/or uncertainty.

Keywords: decision-making, household waste, mathematical programming, optimization, uncertainty

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