A robust ordering strategy for retailers facing a free shipping option

Area of Science:

• Operations Research
• Supply Chain Management
• E-commerce Logistics

Background:

• Free shipping with conditions is a key marketing strategy for e-businesses.
• Retailers face uncertain customer demand influenced by various external factors.
• Existing models may not adequately address demand uncertainty in free shipping offers.

Purpose of the Study:

• To develop a robust optimization model for centralized ordering with conditional free shipping.
• To analyze the impact of stochastic demand on free shipping strategies.
• To provide a stable and computationally efficient solution for e-commerce businesses.

Main Methods:

• Modeling the centralized ordering problem with stochastic demand.
• Applying robust optimization techniques to handle uncertainty.
• Utilizing partial demand information (mean, support, deviation).
• Comparing robust optimization with linear decision rules.

Main Results:

• The robust optimization method provides equivalent uncertainty constraints with good mathematical properties.
• The proposed method demonstrates better stability and computational efficiency compared to linear decision rules.
• The cost-threshold relationship is analyzed across three distinct periods.
• The robustness of the optimal solution is sensitive to minimum quantity parameters.

Conclusions:

• Robust optimization offers a stable and effective approach for managing conditional free shipping under uncertain demand.
• The model provides valuable insights for e-businesses seeking to optimize their shipping strategies.
• Further analysis of minimum quantity parameters can refine solution robustness.