Model Predictive Control for Scheduling and Routing in a Solid Waste Management System

Solid waste collection and hauling account for the greater part of the total cost in modern solid waste management systems. In a recent initiative, 3,300 Swedish recycling containers have been fitted with level sensors and wireless communication equipment thereby giving waste collection operators access to real-time information on the status of each container. In a previous study (Johansson, 2006), analytical modeling and discrete-event simulation have been used to evaluate different scheduling and routing policies utilizing the real-time data, and it has been shown that dynamic scheduling and routing policies exist that have lower operating costs, shorter collection and hauling distances, and reduced labor hours compared to the static policy with fixed routes and pre-determined pick-up frequencies employed by many waste collection operators today. This study aims at further refining the scheduling and routing policies by employing a model predictive control (MPC) framework on the system. In brief, the MPC controller should minimize an objective cost function consisting of fixed and variable collection and hauling costs for a fixed future horizon by calculating a sequence of tactical scheduling and routing decisions that satisfies system constraints using a receding horizon strategy

firedrakeproject/petsc4py: The Python interface to PETSc

firedrakeproject/petsc4py: The Python interface to PETScfiredrakeproject/petsc4py: The Python interface to PETScFiredrake_20190815.