Reference:
B. Kersbergen,
T. van den Boom, and
B. De Schutter,
"Distributed model predictive control for rescheduling of railway
traffic," Proceedings of the 17th International IEEE Conference on
Intelligent Transportation Systems (ITSC 2014), Qingdao, China,
pp. 2732-2737, Oct. 2014.
Abstract:
In this paper we introduce two distributed model predictive control
(DMPC) methods for the rescheduling of railway traffic. In each step
of the DMPC approach dispatching actions are determined that reduce
the amount of delay in the network as much as possible by solving a
mixed integer linear programming (MILP) problem. The constraints of
the MILP are based on a model of the railway traffic and network and
the possible dispatching actions. In the first method each subproblem
consists of the complete constraint matrix and the solver tries to
minimize the centralized cost function, but can only change a limited
number of binary variables (which correspond to the dispatching
actions). By limiting the number of binary variables each subproblem
is easier to solve than the centralized problem. For the second method
each subproblem consists of only a part of the problem and the solver
minimizes a local cost function, and it can only change the binary
variables for that part of the problem. This reduces the complexity of
the subproblems even further, but the solver can not determine the
effects of the binary variables on the solution quality of the other
subproblems. Both methods significantly reduce the time needed to
determine the dispatching actions. The average time needed to compute
the solution is 11.56 times shorter when using method 1 and 39.11
times shorter when using method 2. The solution found is on average
only 0.63% less optimal for method 1 and 1.27% less optimal for method
2.