Comparing solution methods for the machine reassignment problem

The machine reassignment problem is defined by a set of machines and a set of processes. Each machine is associated with a set of resources, e.g. CPU, RAM etc., and each process is associated with a set of required resource values and a currently assigned machine. The task is to reassign the processes to machines while respecting a set of hard constraints in order to improve the usage of the machines, as defined by a complex cost function. We present a natural Constraint Programming (CP) formulation of the problem that uses a set of well-known global constraints. However, this formulation also requires new global constraints. Additionally, the instances are so large that systematic search is not practical especially when the time is limited. We therefore developed a CP formulation of the problem that is especially suited for a large neighborhood search approach (LNS). We also experimented with a mixed-integer programming (MIP) model for LNS. We first compare our formulations on a set of ROADEF'12 problem instances where the number of processes and machines are restricted to 1000 and 100 respectively. Both MIP and CP-based LNS approaches find solutions that are significantly better than the initial ones and compare well to other submitted entries. We further investigate their performances on larger instances where the number of processes and machines can be up to 50000 and 5000 respectively. The results suggest that our CP-based LNS can scale to large instances and is superior in memory use and the quality of solutions that can be found in limited time.