Multi-Objective Optimization of Container Load Plans for Modulating Inventory Flow

Abstract

Conventional strategies for container load planning (CLP) predominantly emphasize maximizing container utilization, which can result in suboptimally-timed inventory arrival, increased inventory holding costs, and downstream operational inefficiencies. Using a real-world case study from a global footwear and apparel retailer, this research formulates a novel multi-objective mixed-integer linear programming (MOMILP) model that jointly considers container utilization, transportation and storage costs, and timing accuracy of inventory delivery. The proposed model utilizes a branch-and-bound algorithm to evaluate numerous load configurations, assessing the impact of different load rules and weighting parameters on transportation performance metrics and inventory flow. Results highlight the cruciality of prioritizing delivery precision in transportation management decisions, demonstrating that solely maximizing volume utilization can adversely affect overall cost efficiency when downstream inventory storage and operational requirements are considered. This work also provides a process map of load planning activities and identifies targeted operational improvements, such as consolidation bypass and purchase order (PO) partitioning, that can enhance inventory flow smoothness, reduce transportation costs, and support more responsive logistics networks. Collectively, this work extends existing CLP methodologies by incorporating delivery timing and inventory storage considerations into load planning decisions, offering practical enhancements for logistics optimization.