Experimental Optimization - Lecture 2.2

00:54 Introduction
02:23 Falsifiability
08:25 The story so far
09:38 Modelling approaches : Mathematical Optimization (MO)
11:25 Overview of Mathematical Optimization
14:04 Mainstream supply chain theory (recap)
19:56 Extent of the Mathematical Optimization Perspective
23:29 Rejection heuristics
30:54 The day after
32:43 Redeeming qualities?
36:13 Modelling approaches : Experimental Optimization (EO)
38:39 Overview of Experimental Optimization
42:54 Root causes of insanity
51:28 Identify insane decisions
58:51 Improve the instrumentation
01:01:13 Improve and repeat
01:04:40 The practice of EO
01:11:16 Recap
01:14:14 Conclusion
01:16:39 2.2 Experimental Optimization - Questions?


Far from the naïve Cartesian perspective where optimization would just be about rolling-out an optimizer for a given score function, supply chain requires a much more iterative process. Each iteration is used to identify “insane” decisions that are to be investigated. The root cause is frequently improper economic drivers, which need to be re-assessed in regards to their unintended consequences. The iterations stop when the numerical recipes no longer produce insane results.


  • The Logic of Scientific Discovery, Karl Popper, 1934