  Decision diagrams are an increasingly important tool in cutting-edge solvers
for discrete optimization. However, the field of decision diagrams is
relatively new, and is still incorporating the library of techniques that
conventional solvers have had decades to build. We drew inspiration from the
warm-start technique used in conventional solvers to address one of the major
challenges faced by decision diagram based methods. Decision diagrams become
more useful the wider they are allowed to be, but also become more costly to
generate, especially with large numbers of variables. In the original version
of this paper, we presented a method of peeling off a sub-graph of previously
constructed diagrams and using it as the initial diagram for subsequent
iterations that we call peel-and-bound. We tested the method on the sequence
ordering problem, and our results indicate that our peel-and-bound scheme
generates stronger bounds than a branch-and-bound scheme using the same
propagators, and at significantly less computational cost. In this extended
version of the paper, we also propose new methods for using relaxed decision
diagrams to improve the solutions found using restricted decision diagrams,
discuss the heuristic decisions involved with the parallelization of
peel-and-bound, and discuss how peel-and-bound can be hyper-optimized for
sequencing problems. Furthermore, we test the new methods on the sequence
ordering problem and the traveling salesman problem with time-windows (TSPTW),
and include an updated and generalized implementation of the algorithm capable
of handling any discrete optimization problem. The new results show that
peel-and-bound outperforms ddo (a decision diagram based branch-and-bound
solver) on the TSPTW. We also close 15 open benchmark instances of the TSPTW.
