Simulation-based Optimization of Solid Waste Management and Recycling Programs

ID: 268 Eric D. Antmann, Nurcin Celik*, Xiaoran Shi, and Yading Dai Department of Industrial Engineering, The University of Miami, Coral Gables, FL, USA *Telephone: (305) 284-2391, E-mail: [email protected] Abstract Solid waste produced as a by-product of our daily activities poses a major threat to societies as populations grow and economic development advances. Consequently, the effective management of solid waste has become a matter of critical importance for communities. In this study, we propose a simulation-based decision-making and optimization framework for the analysis and development of effective solid waste management and recycling programs, under uncertainty. The components of the proposed framework include a database and two modules: an assessment module and a resource allocation optimization module. The assessment module identifies the sources of uncertainties in the system and develops a parameterization of them for incorporation into the resource allocation optimization module. This module involves a discrete-continuous model of the system under consideration, with respect to the waste types and characteristics, costs, environmental impacts, types, location and capacities of processing facilities, and their capabilities. Then, the multi-criteria problem of the allocation of limited resources is solved via the optimization mechanism embedded in the resource allocation optimization module. Here, the optimum solution is defined by the user and infinitely variable. The proposed decision making framework has been successfully demonstrated for the Miami-Dade County Solid Waste Management System in the State of Florida.Solid waste produced as a by-product of our daily activities poses a major threat to societies as populations grow and economic development advances. Consequently, the effective management of solid waste has become a matter of critical importance for communities. In this study, we propose a simulation-based decision-making and optimization framework for the analysis and development of effective solid waste management and recycling programs, under uncertainty. The components of the proposed framework include a database and two modules: an assessment module and a resource allocation optimization module. The assessment module identifies the sources of uncertainties in the system and develops a parameterization of them for incorporation into the resource allocation optimization module. This module involves a discrete-continuous model of the system under consideration, with respect to the waste types and characteristics, costs, environmental impacts, types, location and capacities of processing facilities, and their capabilities. Then, the multi-criteria problem of the allocation of limited resources is solved via the optimization mechanism embedded in the resource allocation optimization module. Here, the optimum solution is defined by the user and infinitely variable. The proposed decision making framework has been successfully demonstrated for the Miami-Dade County Solid Waste Management System in the State of Florida.