Data-driven optimization model: Digikala case study

نویسندگان
Fatemi Ghomi, Seyed Mohammad Taghi ID 1
Hamidi, Sanaz ID 2

1 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

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چکیده

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.

کلیدواژه‌ها
Optimization modeling, statistical learning, mixed integer linear programming, Third-party Logistics