Optimization of empty container allocation for inland freight stations considering stochastic demand.

Area of Science:

• Logistics and Supply Chain Management
• Operations Research
• Maritime Economics

Background:

• The post-COVID-19 epidemic era (PCEE) presents significant uncertainty in empty container supply, impacting liner companies.
• Accurate allocation of owned and leased empty containers to inland freight stations is crucial but challenging due to unpredictable demand and inflexible relocation.

Purpose of the Study:

• To develop a model for optimizing empty container allocation without prior knowledge of demand probability distributions.
• To jointly optimize the quantities of self-owned and leased empty containers for each inland freight station.

Main Methods:

• A novel model for empty container allocation is proposed.
• A largest-debt-first policy is employed to simplify the allocation model.
• A differential evolutionary (DE) algorithm is utilized to solve the simplified model and compared with traditional methods.

Main Results:

• The proposed largest-debt-first policy demonstrates superior cost control in high demand fluctuation scenarios compared to traditional methods.
• The differential evolutionary (DE) algorithm shows effectiveness in exploring optimal solutions for the allocation model.
• Experimental results validate the model's utility in managing operational and management costs.

Conclusions:

• The developed theory and method enable carriers to establish optimal regional empty container stock levels.
• The approach effectively determines the optimal quantities of self-owned and leased empty containers for allocation.
• This contributes to improved efficiency and cost-effectiveness in empty container management for liner companies.