This paper presents a heuristic for solving very large routing problems (th
ousands of customers and hundreds of vehicles) with side constraints such a
s time windows. When applied to traditional benchmarks (Solomon's), we obta
in high quality results with short resolution time (a few seconds). We also
introduce a LDS (Limited Discrepancy Search) variation that produces state
-of-the-art results. The heart of this heuristic is a combination of a look
-ahead insertion algorithm, an incremental local optimization scheme and a
constraint solver for constrained traveling salesman problems. The incremen
tality means that instead of visiting some large neighborhood after an init
ial solution has been found, a limited number of moves is examined, after e
ach insertion, on the partial solution. This incremental version is not onl
y faster, it also yields better results than using local optimization once
a full solution has been built. We also show how additional constraints can
be used in order to guide the insertion process. Because of its use of sep
arate CP (Constraint Programming) modules, this method is flexible and may
be used to solve large dispatching problems that include many additional co
nstraints such as setup times (asymmetrical distance) or skill matching.