도심 공동물류 택배터미널의 리소스 제약을 고려한 최대 처리량 산출을 위한 수리모델 연구
A Mathematical Model for Resource Constrained Maximum Throughput in Urban Joint Courier Terminals
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The rapid growth of e-commerce urgently demands the optimization of urban joint courier terminals, which operate under critical constraints on space, equipment, and delivery resources. This study proposes a comprehensive Mixed-Integer Linear Programming (MILP) model to calculate the theoretical maximum throughput (P) of such a terminal. The model systematically integrates real-world constraints across multiple stages: individual carrier supply limits (), inbound vehicle capacity, classification equipment (scanners, chutes), limited operating time (), capacity () and availability () of the last-mile delivery fleet (Truck, Eco-friendly, Subway), and the restricted terminal space ().Validation using Python library revealed that the maximum throughput of 195 units was fundamentally constrained, not by the classification speed or extended operational hours, but by the binding limits of the last-mile delivery fleet and the terminal footprint. Investment scenario analysis demonstrated that increasing classification capacity (Scanner) or operation time (Time) was an inefficient investment, as P remained constant. Conversely, enhancing delivery mode capacity (Truck, Eco) immediately increased P to the system's secondary ceiling (200 units, defined by space and total supply), confirming the core bottleneck was at the final delivery stage. Furthermore, deteriorating space constraints (Space) caused the most significant throughput drop, proving space is the most critical resource constraint. This quantitative evidence is vital for strategic planning, urging investment priority toward expanding last-mile capability and efficiently utilizing urban space. The MILP structure provides a robust foundation for future AI-driven operations and Digital Twin integration.
Abstract
The rapid growth of e-commerce urgently demands the optimization of urban joint courier terminals, which operate under critical constraints on space, equipment, and delivery resources. This study proposes a comprehensive Mixed-Integer Linear Programming (MILP) model to calculate the theoretical maximum throughput (P) of such a terminal. The model systematically integrates real-world constraints across multiple stages: individual carrier supply limits (), inbound vehicle capacity, classification equipment (scanners, chutes), limited operating time (), capacity () and availability () of the last-mile delivery fleet (Truck, Eco-friendly, Subway), and the restricted terminal space ().Validation using Python library revealed that the maximum throughput of 195 units was fundamentally constrained, not by the classification speed or extended operational hours, but by the binding limits of the last-mile delivery fleet and the terminal footprint. Investment scenario analysis demonstrated that increasing classification capacity (Scanner) or operation time (Time) was an inefficient investment, as P remained constant. Conversely, enhancing delivery mode capacity (Truck, Eco) immediately increased P to the system's secondary ceiling (200 units, defined by space and total supply), confirming the core bottleneck was at the final delivery stage. Furthermore, deteriorating space constraints (Space) caused the most significant throughput drop, proving space is the most critical resource constraint. This quantitative evidence is vital for strategic planning, urging investment priority toward expanding last-mile capability and efficiently utilizing urban space. The MILP structure provides a robust foundation for future AI-driven operations and Digital Twin integration.