Abstract
Efficiently solving the truck loading problem (TLP) is crucial for achieving urban logistics objectives. Many heuristics, mainly based on wall-building and layer-building concepts, are presented in literature to solve this NP-hard problem. The current research presents a two-step novel prioritization-stacking heuristic algorithm for solving the TLP. The new algorithm primarily aims to improve the truck space utilization and reduce inventory costs at origin and destination points. In particular, the current work considers developing countries conditions in which demand is uncertain, cost minimization is prioritized over customer service, and routing optimization is complicated by a lot of traffic jams. The performance of the proposed heuristic is evaluated by applying it to a real-life case study from the Jordanian market. Additionally, the stacking part of the proposed heuristic is benchmarked with a literature data sample. Both implementations have proved the validity of the suggested heuristic algorithm.
Highlights
Optimizing transportation activities is one of the most essential areas of logistics planning; it can greatly improve the overall logistics customer service level and reduce the total logistics cost
Step one of the heuristic selects the items to be packed in the truck based on a multi criteria index, step two explains how the items should be packed within the 3D truck space
This paper presented a novel heuristic algorithm for the truck loading problem (TLP) where the conditions of high demand uncertainty and cost criterion are dominating
Summary
Optimizing transportation activities is one of the most essential areas of logistics planning; it can greatly improve the overall logistics customer service level and reduce the total logistics cost. Step one of the heuristic selects the items to be packed in the truck based on a multi criteria index, step two explains how the items should be packed within the 3D truck space. This heuristic specially/ applies to cases of: 1) high demand uncertainty, resulting in high inventory costs; 2) high priority of cost criterion, reducing the cost (transportation and inventory costs) is more important than providing better service (expressed for example in low transportation cycle time), and 3) high complexity of routing optimization due to traffic jams; cases that are typically encountered in developing countries.
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