A Finite Dominating Set Approach for the Multi-Item Multi-Period Order Allocation Problem under All-Unit Quantity Discounts and Blending Ratios

TL;DR

This paper introduces a finite dominating set approach to efficiently solve a complex multi-item, multi-period order allocation problem with all-unit quantity discounts and blending ratios, ensuring optimality and reducing computational effort.

Contribution

It develops a novel finite dominating set method that guarantees optimal solutions for a nonlinear, multi-item procurement problem, simplifying the modeling process.

Findings

Achieves up to 99% faster solutions for large instances.

Demonstrates dynamic adaptation to cost parameter changes.

Identifies cost-sensitive ingredients affecting total costs.

Abstract

This study addresses the multi-item multi-period order allocation problem under all-unit quantity discounts (AUQD) and blending ratios. A manufacturer makes a single product that requires mixing/assembling multiple ingredients/components with pre-determined blending ratios. We consider a single supplier offering quantity-based discounts which introduces non-linearities to the problem. The objective is to minimize procurement cost which includes purchasing, inventory, and ordering costs. We develop a solution procedure that systematically generates a finite dominating set (FDS) of order quantities guaranteed to include an optimal solution to the problem. A Mixed Integer Linear Programming (MILP) model based on the FDS. Our procedure guarantees optimality and eliminates the need for nonlinear discount modeling. Numerical experiments demonstrate that the proposed MILP achieves optimal solutions with significantly reduced computational effort, up to 99% faster for large-scale instances compared to conventional formulations. Sensitivity analyses reveal that the model dynamically adapts to changes in holding costs, shifting between bulk-purchasing and just-in-time strategies, and identifying cost-sensitive ingredients that drive total system cost.