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.
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Time stamps:
0:00:00 - Introduction
0:02:18 - Background
0:12:08 - Why optimize? 1/2 Forecasting with Holt-Winters
0:17:32 - Why optimize? 2/2 - Vehicle routing problem
0:20:49 - The story so far
0:22:21 - Auxiliary Sciences (recap)
0:23:45 - Problems and solutions (recap)
0:27:12 - Mathematical optimization
0:28:09 - Convexity
0:34:42 - Stochasticity
0:42:10 - Multi-objective
0:46:01 - Solver design
0:50:46 - Deep (Learning) lessons
1:10:35 - Mathematical optimization
1:10:58 - "True" programming
1:12:40 - Local search
1:19:10 - Stochastic gradient descent
1:26:09 - Automatic differentiation
1:31:54 - Differential programming (circa 2018)
1:35:36 - Conclusion
1:37:44 - 4.3 Mathematical optimization for supply chain - Questions?
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