An Adaptive Tabu Search Algorithm for Solving the Two-Dimensional Loading Constrained Vehicle Routing Problem with Stochastic Customers

In practical logistic distributions, uncertainties may exist in each distribution process, and sometimes suppliers have to take undesirable measures deal with the subsequent schedule variances. light of uncertainty customers logistics widely applied two-dimensional loading patterns transportation, we propose formulate a loading-constrained vehicle routing problem stochastic (2L-VRPSC), where customer has known probability presence customers’ demands are set non-stackable items. A modeling platform 2L-VRPSC is established based on Monte Carlo simulation scenario analysis minimize expected total transportation cost. To achieve this, an enhanced adaptive tabu search (EATS) algorithm incorporating multi-order bottom-fill-skyline (MOBFS) packing heuristic proposed, EATS searches for optimal combination MOBFS checks feasibility route guides feasible solutions. The used (2L-VRP) benchmarks under different configurations considering items’ sequential rotation constraints experiments, which demonstrates comparable efficiency proposed EATS-MOBFS solving 2L-VRP. Furthermore, results experiments new instances verify versatility approach, capable providing more solutions some real-life scenarios uncertain information.