Experimental Optimization - Lecture 2.2

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


00:00 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?