Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Data-driven optimization model: Digikala case study

Document Type : conference paper

Authors
Sanaz Hamidi
Seyed Mohammad Taghi Fatemi Ghomi
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Abstract
Increasing software as a service (SaaS) requires the provision of more updated models for services, so trying to develop a model customized for the customer is important. We used the linear Knapsack problem model proposed by Mike Hewitt and Emma Frejinger in 2020. Then historical data of Digikala was applied and shown that how the model works on it.
Keywords
Optimization modeling
statistical learning
mixed integer linear programming
third-party logistics
Subjects
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  • Receive Date 17 November 2021
  • Revise Date 20 December 2021
  • Accept Date 27 December 2021