Meta partial benders decomposition for the logistics service network design problem

Supply chain transportation operations often account for a large proportion of product total cost to market. Such can be optimized by solving the Logistics Service Network Design Problem (LSNDP), wherein logistics service provider seeks cost-effectively source and fulfill customer demands products within multi-echelon distribution network. However, many industrial settings yield instances LSNDP that are too solved in reasonable run-times off-the-shelf optimization solvers. We introduce an exact Benders decomposition algorithm based on partial decompositions strengthens master problem with information derived from aggregated subproblem data. More specifically, proposed Meta Partial Decomposition intelligently switches one another changing both amount include as well how it is aggregated. Through extensive computational study, we show approach outperforms existing benchmark methods demonstrate benefits dynamically refining course decomposition-based scheme.