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Learning to Minimize Cost to Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce

Published: 04 January 2023 Publication History

Abstract

In the retail industry, electronic commerce (e-commerce) has grown quickly in the last decade and has further accelerated as a result of movement restrictions during the pandemic. While working with logistics and retail industry business collaborators, we found that the cost of delivery of products from the most opportune node in the supply chain (a quantity called the cost-to-serve or CTS) is a key challenge. In this paper, we formally define CTS as a decision-making problem. We then focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to the customer doorstep. We find that a reinforcement learning (RL) formulation is able to exceed the performance of the state of the art rule based policies, while being significantly faster than traditional optimisation approaches such as mixed-integer linear programming. We hypothesise that scaling up the RL based methodology will have a significant impact on the operating margins of retailers in the ‘new normal’.

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cover image ACM Other conferences
CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)
January 2023
357 pages
ISBN:9781450397971
DOI:10.1145/3570991
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 January 2023

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  1. cost-to-serve
  2. e-commerce
  3. reinforcement learning

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CODS-COMAD 2023

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