Flexible supplier selection and order allocation in the big data era with various quantity discounts

Area of Science:

• Operations Research
• Supply Chain Management
• Computational Economics

Background:

• Supplier selection and order allocation models often simplify discount structures.
• Real-world scenarios involve diverse quantity discount types (no discount, all-unit, incremental, carload).
• Handling multiple discount types simultaneously presents significant modeling and computational challenges.

Purpose of the Study:

• To develop a flexible model for large-scale supplier selection and order allocation incorporating various quantity discount types.
• To address the literature gap concerning the simultaneous modeling of multiple discount structures.
• To create an efficient solution methodology for complex, real-world supply chain problems.

Main Methods:

• Formulation of the supplier selection and order allocation problem as a variant of the NP-hard knapsack problem.
• Application of a greedy algorithm, known for optimally solving the fractional knapsack problem.
• Development of three distinct greedy algorithms leveraging problem-specific properties and sorted lists.

Main Results:

• Achieved average optimality gaps of 0.1026%, 0.0547%, and 0.0234% for varying problem sizes.
• Demonstrated efficient solvability within centiseconds, densiseconds, and seconds for 1000, 10000, and 100000 suppliers, respectively.
• Validated the model's scalability and performance in handling large datasets.

Conclusions:

• The proposed greedy algorithms provide highly accurate and computationally efficient solutions for large-scale supplier selection and order allocation with diverse quantity discounts.
• The methodology effectively bridges the gap between theoretical models and practical supply chain complexities.
• Enables robust decision-making by leveraging big data analytics in dynamic procurement environments.