This thesis presents the vehicle routing problem with cross-docking for perishable products (VRPCD-PP) where a set of homogeneous vehicles are used to transport the orders from suppliers to customers via a single cross-dock. The objective of the model is to minimize the summation of logistical cost and freshness-quality cost respecting the time window constraints and truck capacity constraints. Two separated robust counterparts and the combined formulation are developed when the travel times of the network and freshness-life of products are uncertain. In this thesis, the mixed integer linear programming formulation for robust VRPCD-PP under uncertain freshness-life and traveling time is introduced. The proposed mathematical model utilizes the compact formulation in defining the worst-case scenario of the polyhedral uncertainty set instead of using the standard dualization technique. In addition, the Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm is developed to solve the deterministic and robust formulation for large instances. Computational experiments using Wen’s dataset for VRPCD show the proposed algorithm provides reasonable solutions to the deterministic problem. Moreover, the results of robust solutions are analyzed through the price of robustness to obtain a more reliable solution for the practical decision making process.

This thesis presents the vehicle routing problem with cross-docking for perishable products (VRPCD-PP) where a set of homogeneous vehicles are used to transport the orders from suppliers to customers via a single cross-dock. The objective of the model is to minimize the summation of logistical cost and freshness-quality cost respecting the time window constraints and truck capacity constraints. Two separated robust counterparts and the combined formulation are developed when the travel times of the network and freshness-life of products are uncertain. In this thesis, the mixed integer linear programming formulation for robust VRPCD-PP under uncertain freshness-life and traveling time is introduced. The proposed mathematical model utilizes the compact formulation in defining the worst-case scenario of the polyhedral uncertainty set instead of using the standard dualization technique. In addition, the Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm is developed to solve the deterministic and robust formulation for large instances. Computational experiments using Wen’s dataset for VRPCD show the proposed algorithm provides reasonable solutions to the deterministic problem. Moreover, the results of robust solutions are analyzed through the price of robustness to obtain a more reliable solution for the practical decision making process.

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