Having worked in the operations research domain, I thought I had leveraged a variety of methods to formulate optimization problems, and there may not be new approaches to learn.

Until I stumbled upon this today. https://lnkd.in/gKTrFX_C

While several approaches to optimizing eCommerce networks exist, this is a new one for me. But then, I have not dabbled in optimization for a few years now.

In traditional manufacturing supply chains, demand forecasts are relatively stable and often aggregated (e.g., monthly orders). But in e-commerce, demand:

1. Fluctuates heavily due to flash sales, influencer promotions, or seasonal spikes,

2. Is highly localized (city or micro-region level), and

3. Interacts dynamically with return rates, which themselves are stochastic.

Traditional optimization assumes fixed demand values (like deterministic D_i for customer i), while this paper introduces an optimization framework that treats D_i as an uncertain parameter.

Treating D_i as uncertain is not new. What is new in this paper is the inclusion of a specific parameter and then leveraging an optimization approach that makes the best use of it.

The paper defines uncertain demand (and returns) within interval bounds, forming what’s called a Box uncertainty set:

D_i \in [D_i^0 – \Delta_i, \, D_i^0 + \Delta_i]

Where

a. D_i^0: nominal (forecasted) demand at customer i
b. Delta_i: maximum deviation allowed (based on historical volatility or confidence interval)

To prevent the optimization from assuming all demands hit their worst-case simultaneously (which would make the solution too conservative), they introduce a budget-of-uncertainty parameter \Gamma_D, following the Bertsimas–Sim robust optimization approach.