A Real-Time Scalable Heuristic DSS Framework for Capacity-Constrained Retail Allocation under Supply Chain Uncertainty

Abstract

The rapid proliferation of omnichannel retail strategies has fundamentally transformed store replenishment operations in uncertain supply chain environments. With retail stores increasingly acting as hybrid fulfillment centers, pooled inventory allocation must absorb uncertain order realizations, constrained receiving capacities, dynamic vehicle limits, multi-tiered product priorities, and planner-controlled outbound warehouse preferences. This study frames this commercial reality as an extended constrained variant of the Multidimensional Knapsack Problem (MKP). Recognizing that exact optimization techniques such as Mixed-Integer Linear Programming (MILP) are computationally prohibitive in large-scale real-time settings, we propose a real-time scalable heuristic embedded in a computationally efficient Decision Support System (DSS) framework based on set-oriented cumulative filtering. The framework evaluates cumulative flow-through deductions, third-party logistics routing integrations, category-specific volume caps, warehouse activation filters, and user-defined warehouse priority ranks. An extensive case study within a large retail network covering 212,278 order records from June 2025 to April 2026 demonstrates the impact of the proposed methodology. Using January 2026 as the go-live cutoff, weighted ship-to-order ratio improved from 54.1% to 67.8%, weighted same-day coverage improved from 24.3% to 37.8%, and store-days with order volumes above store limits were reduced by 48.6%. These findings indicate that the proposed real-time scalable heuristic and computationally efficient DSS framework provide practical, uncertainty-aware allocation support for volatile retail supply chains.