A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against restricted mass balances. (15th March 2020)

• Record Type: Journal Article
• Authors: Batur, Maliki Ejder; Cihan, Ahmet; Korucu, Mahmut Kemal; Bektaş, Nihal; Keskinler, Bülent
• Is Part Of: Waste management. Volume 105(2020)
• Page Start: 211
• Page End: 222
• Publication Date: 2020-03-15
• Subjects: Cost optimization -- Decision-making -- Mathematical modeling -- Municipal solid wastes; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2020.02.003
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: A novel programming model was formulated for essential decision layers of MSWM. Model was tested with a case study for a cost optimization application. Results showed the feasibility of new model for an ideal mass balance in MSWM. New approach is able to provide significant convenience for decision tools of MSWM. Abstract: Long-term planning of municipal solid waste management systems is a complex decision making problem which includes a large number of decision layers. Since all different waste treatment and disposal processes will show different responses to each municipal solid waste component, it is necessary to separately evaluate all waste components for all processes. This obligation creates an obstacle in the programming of mass balances for long-term planning of municipal solid waste management systems. The development of an ideal mixed integer linear programming model that can simultaneously respond to all essential decision layers including waste collection, process selection, waste allocation, transportation, location selection, and capacity assessment has not been made possible yet due to this important modeling obstacle. According to the current knowledge of the literature, all mixed integer linear programming studies aiming to address this obstacle so far have had to restrict many different possibilities in their mass balances. In this study, a novel mixed integer linear programming model was formulated. ALOMWASTE, the new model structure developed in this study, was built to take into consideration different process, capacity, and location possibilities that may occur in complex waste management processes at the same time. The results obtained from a case study showed the feasibility of new mixed integer linear programming model obtained in this study for the simultaneous solution of all essential decision layers in an unrestricted mass balance. The model is also able to provide significant convenience for the multi-objective optimization of financial-environmental-social costs and the solution of some uncertainty problems of decision-making tools such as life cycle assessment.