Mathematical optimization for supply chain - Lecture 4.3


00:18 Introduction
02:18 Background
12:08 Why optimize? 1/2 Forecasting with Holt-Winters
17:32 Why optimize? 2/2 - Vehicle routing problem
20:49 The story so far
22:21 Auxiliary Sciences (recap)
23:45 Problems and solutions (recap)
27:12 Mathematical optimization
28:09 Convexity
34:42 Stochasticity
42:10 Multi-objective
46:01 Solver design
50:46 Deep (Learning) lessons
01:10:35 Mathematical optimization
01:10:58 “True” programming
01:12:40 Local search
01:19:10 Stochastic gradient descent
01:26:09 Automatic differentiation
01:31:54 Differential programming (circa 2018)
01:35:36 Conclusion
01:37:44 4.3 Mathematical optimization for supply chain - Questions?

Description

Mathematical optimization is the process of minimizing a mathematical function. Nearly all the modern statistical learning techniques - i.e. forecasting if we adopt a supply chain perspective - rely on mathematical optimization at their core. Moreover, once the forecasts are established, identifying the most profitable decisions also happen to rely, at its core, on mathematical optimization. Supply chain problems frequently involve many variables. They are also usually stochastic in nature. Mathematical optimization is a cornerstone of a modern supply chain practice.

References

  • The Future of Operational Research Is Past, Russell L. Ackoff, February 1979
  • LocalSolver 1.x: A black-box local-search solver for 0-1 programming, Thierry Benoist, Bertrand Estellon, Frédéric Gardi, Romain Megel, Karim Nouioua , September 2011
  • Automatic differentiation in machine learning: a survey, Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind, last revised February 2018