Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce Article Swipe
Pranavi Pathakota
,
Kunwar Zaid
,
Anulekha Dhara
,
Hardik Meisheri
,
Shaun D Souza
,
Dheeraj Shah
,
Harshad Khadilkar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.08736
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.08736
We describe a novel decision-making problem developed in response to the demands of retail electronic commerce (e-commerce). 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. The large scale, high stochasticity, and large geographical spread of e-commerce supply chains make this setting ideal for a carefully designed data-driven decision-making algorithm. In this preliminary work, we focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to multiple customers in each time period. We compare the relative performance and computational efficiency of several baselines, including heuristics and mixed-integer linear programming. We show that a reinforcement learning based algorithm is competitive with these policies, with the potential of efficient scale-up in the real world.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.08736
- https://arxiv.org/pdf/2112.08736
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225974048
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225974048Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.08736Digital Object Identifier
- Title
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Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic CommerceWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-16Full publication date if available
- Authors
-
Pranavi Pathakota, Kunwar Zaid, Anulekha Dhara, Hardik Meisheri, Shaun D Souza, Dheeraj Shah, Harshad KhadilkarList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.08736Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2112.08736Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2112.08736Direct OA link when available
- Concepts
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Heuristics, Computer science, Supply chain, Node (physics), Product (mathematics), Key (lock), Order (exchange), Operations research, Scale (ratio), Business, Marketing, Computer security, Engineering, Physics, Geometry, Structural engineering, Mathematics, Finance, Quantum mechanics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.business | 24 |
| abstract_inverted_index.commerce | 15 |
| abstract_inverted_index.delivery | 32 |
| abstract_inverted_index.describe | 1 |
| abstract_inverted_index.designed | 75 |
| abstract_inverted_index.industry | 23 |
| abstract_inverted_index.learning | 128 |
| abstract_inverted_index.multiple | 91, 100 |
| abstract_inverted_index.products | 34, 92 |
| abstract_inverted_index.quantity | 45 |
| abstract_inverted_index.relative | 109 |
| abstract_inverted_index.response | 8 |
| abstract_inverted_index.scale-up | 141 |
| abstract_inverted_index.specific | 87 |
| abstract_inverted_index.algorithm | 130 |
| abstract_inverted_index.arbitrary | 94 |
| abstract_inverted_index.carefully | 74 |
| abstract_inverted_index.customers | 101 |
| abstract_inverted_index.developed | 6 |
| abstract_inverted_index.efficient | 140 |
| abstract_inverted_index.including | 117 |
| abstract_inverted_index.logistics | 20 |
| abstract_inverted_index.opportune | 38 |
| abstract_inverted_index.policies, | 135 |
| abstract_inverted_index.potential | 138 |
| abstract_inverted_index.warehouse | 98 |
| abstract_inverted_index.algorithm. | 78 |
| abstract_inverted_index.baselines, | 116 |
| abstract_inverted_index.challenge. | 54 |
| abstract_inverted_index.delivering | 90 |
| abstract_inverted_index.e-commerce | 65 |
| abstract_inverted_index.efficiency | 113 |
| abstract_inverted_index.electronic | 14 |
| abstract_inverted_index.heuristics | 118 |
| abstract_inverted_index.quantities | 95 |
| abstract_inverted_index.subproblem | 88 |
| abstract_inverted_index.competitive | 132 |
| abstract_inverted_index.data-driven | 76 |
| abstract_inverted_index.performance | 110 |
| abstract_inverted_index.preliminary | 81 |
| abstract_inverted_index.geographical | 62 |
| abstract_inverted_index.programming. | 122 |
| abstract_inverted_index.(e-commerce). | 16 |
| abstract_inverted_index.computational | 112 |
| abstract_inverted_index.cost-to-serve | 48 |
| abstract_inverted_index.mixed-integer | 120 |
| abstract_inverted_index.reinforcement | 127 |
| abstract_inverted_index.collaborators, | 25 |
| abstract_inverted_index.stochasticity, | 59 |
| abstract_inverted_index.decision-making | 4, 77 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |