An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel
Inventories
- URL: http://arxiv.org/abs/2310.12183v1
- Date: Tue, 17 Oct 2023 23:10:57 GMT
- Title: An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel
Inventories
- Authors: Pavithra Harsha, Shivaram Subramanian, Ali Koc, Mahesh Ramakrishna,
Brian Quanz, Dhruv Shah, Chandra Narayanaswami
- Abstract summary: We introduce a new class of data-driven optimistic-robust bimodal inventory optimization (BIO) strategy.
Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management.
- Score: 10.353243563465124
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a new class of data-driven and distribution-free
optimistic-robust bimodal inventory optimization (BIO) strategy to effectively
allocate inventory across a retail chain to meet time-varying, uncertain
omnichannel demand. While prior Robust optimization (RO) methods emphasize the
downside, i.e., worst-case adversarial demand, BIO also considers the upside to
remain resilient like RO while also reaping the rewards of improved
average-case performance by overcoming the presence of endogenous outliers.
This bimodal strategy is particularly valuable for balancing the tradeoff
between lost sales at the store and the costs of cross-channel e-commerce
fulfillment, which is at the core of our inventory optimization model. These
factors are asymmetric due to the heterogenous behavior of the channels, with a
bias towards the former in terms of lost-sales cost and a dependence on network
effects for the latter. We provide structural insights about the BIO solution
and how it can be tuned to achieve a preferred tradeoff between robustness and
the average-case. Our experiments show that significant benefits can be
achieved by rethinking traditional approaches to inventory management, which
are siloed by channel and location. Using a real-world dataset from a large
American omnichannel retail chain, a business value assessment during a peak
period indicates over a 15% profitability gain for BIO over RO and other
baselines while also preserving the (practical) worst case performance.
Related papers
- Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation [55.75188191403343]
We introduce utility, which is a function predefined by each user and describes the trade-off between cost and performance of BO.
We validate our algorithm on various LC datasets and found it outperform all the previous multi-fidelity BO and transfer-BO baselines we consider.
arXiv Detail & Related papers (2024-05-28T07:38:39Z) - Neural Optimization with Adaptive Heuristics for Intelligent Marketing System [1.3079444139643954]
We propose a general framework for marketing AI systems, the Neural Optimization with Adaptive Heuristics (Noah) framework.
Noah is the first general framework for marketing optimization that considers both to-business (2B) and to-consumer (2C) products, as well as both owned and paid channels.
We describe key modules of the Noah framework, including prediction, optimization, and adaptive audiences, providing examples for bidding and content optimization.
arXiv Detail & Related papers (2024-05-17T01:44:30Z) - A Bargaining-based Approach for Feature Trading in Vertical Federated
Learning [54.51890573369637]
We propose a bargaining-based feature trading approach in Vertical Federated Learning (VFL) to encourage economically efficient transactions.
Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties.
arXiv Detail & Related papers (2024-02-23T10:21:07Z) - InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
Reward hacking, also termed reward overoptimization, remains a critical challenge.
We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective.
We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
arXiv Detail & Related papers (2024-02-14T17:49:07Z) - The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with
Trucks and UAVs [1.3812010983144802]
Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas.
This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time.
It introduces a novel dynamic post-disaster inventory allocation problem with trucks and unmanned aerial vehicles delivering relief goods.
arXiv Detail & Related papers (2023-11-30T19:03:04Z) - Model-based Causal Bayesian Optimization [74.78486244786083]
We introduce the first algorithm for Causal Bayesian Optimization with Multiplicative Weights (CBO-MW)
We derive regret bounds for CBO-MW that naturally depend on graph-related quantities.
Our experiments include a realistic demonstration of how CBO-MW can be used to learn users' demand patterns in a shared mobility system.
arXiv Detail & Related papers (2023-07-31T13:02:36Z) - Enhancing Supply Chain Resilience: A Machine Learning Approach for
Predicting Product Availability Dates Under Disruption [2.294014185517203]
COVID 19 pandemic and ongoing political and regional conflicts have a highly detrimental impact on the global supply chain.
accurately predicting availability dates plays a pivotal role in executing successful logistics operations.
We evaluate several regression models, including Simple Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest (RF), Gradient Boosting Machine (GBM) and Neural Network models.
arXiv Detail & Related papers (2023-04-28T15:22:20Z) - Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time.
We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance.
Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
arXiv Detail & Related papers (2023-03-28T00:23:23Z) - Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies.
Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times.
This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
arXiv Detail & Related papers (2022-02-26T02:07:25Z) - Offer Personalization using Temporal Convolution Network and
Optimization [0.0]
Increase in online shopping and high market competition has led to an increase in promotional expenditure for online retailers.
We propose our approach to solve the offer optimization problem at the intersection of consumer, item and time in retail setting.
arXiv Detail & Related papers (2020-10-14T10:59:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.