LOADING WITH SHAPE CHANGEABLE ITEMS AND ITS APPLICATION IN OPTIMIZING MILK-RUN LOGISTICS IN SAIPA GROUP AUTOMOTIVE COMPANY

Milk-run logistics is a consolidation method in which vehicles are dispatched in specified time periods to collect orders from various suppliers and deliver them to assembly lines following predefined routes. Pallets of an order can sit in different arrangements in the vehicle and hence, their loading shape can be changeable. Choosing one of these shapes for the order and arranging its pallets in the vehicle as a unified cube can be handled by decision variables. The set of possible shapes for an order varies as the vehicle type changes and this imposes complexities for the case of heterogeneous fleet. Following such an observation, we introduce the idea of shape changeable loading/packing and the required set of constraints to attain a mixed integer linear formulation with the objective of minimizing total transportation costs. Besides loading issues, other considerations such as extra half cost for reverse distribution of empty pallets, order time windows, and heterogeneous fleet are considered. Given the grouping nature of the problem, a Grouping Evolution Strategy (GES) algorithm is proposed that utilizes an efficient constructive best-fit heuristic to ensure feasibility of routing and shape changeable 2D loading of orders into vehicles. Effectiveness of our approach is tested using real-world data obtained from SAIPA Group automotive company. Extensive computations signified the worth of milk-run logistics in comparison with direct shipping strategy followed by the SAIPA's logistics division. Our simulations approve that there exists a capacity for reducing the cost of direct shipment by average amount of 25% via employing milk-run strategy. Moreover, using the more complex shape changeable loading rationale can reduce the costs by 10% compared to a more straight loading method followed by SAIPA. The joint employment of milk-run logistics and shape changeable loading can result in a 32% reduction in costs on average, compared to the current shipment strategy followed by SAIPA.