Online Order Fulfillment with Replenishment
Zi Ling, Jiashuo Jiang, Linwei Xin

TL;DR
This paper analyzes the joint impact of replenishment and fulfillment policies in online order systems, revealing conditions where replenishment has a greater influence and introducing a look-ahead algorithm to improve performance.
Contribution
It develops a regret-based framework to compare replenishment and fulfillment improvements and introduces a novel look-ahead online algorithm for better decision-making.
Findings
Replenishment cycle length affects regret stability.
Regret-based analysis identifies regimes favoring replenishment or fulfillment improvements.
The look-ahead algorithm outperforms myopic strategies in numerical tests.
Abstract
In modern e-commerce and service operations, firms must jointly manage inventory replenishment and real-time order fulfillment to maximize profit under demand uncertainty. While each component has been studied extensively in isolation, their interaction remains underexplored. This paper investigates a fundamental operational question: which lever plays a more decisive role in overall system performance, replenishment or fulfillment? We model the system as a one-location online order fulfillment problem with lost sales and stochastic customer arrivals, each offering heterogeneous rewards. Replenishment follows either a base-stock or constant-order policy, while real-time fulfillment decisions are made using online algorithms. Our core performance metric is the expected average profit per replenishment cycle, evaluated across all combinations of these policies and algorithms. Our main…
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Taxonomy
TopicsSupply Chain and Inventory Management · Advanced Queuing Theory Analysis · Vehicle Routing Optimization Methods
