Monday, November 30, 2009

O.R. Practice: Doomed by Success?

Did you hear about the dissident O.R practitioner who was sentenced to 'a death by a 1000 cuts' ? Legend has it that his body was found remarkably intact, integral ....

The claim here is that an commercial O.R solution to a real-life problem has a finite shelf-life. The graph of potential improvement for a product is concave, and follows the law of diminishing returns. Most of our recent posts have focused on the need to ensure that the first solution has the 'O.R. inside' stamp, since entrenched heuristics of unknown quality are surprisingly resistant to replacement by more smart-logic based O.R methods.

But what happens after you have something with O. R inside? As a former colleague's professor asked him 'So do you sit around waiting for the model to break?' The answer is sometimes yes. Other times, our twitching O.R genes compel us to keep improving upon the solution and after a while, the effort is not worth the improvement. The better the prior effort was, the less likely that you will be allowed to tinker with it any further. Pretty soon it's set in stone and it just becomes an automaton. After a while, it may even cease to be of much competitive value to a company and the functions are likely to be outsourced to a cookie-cutter vendor.

A car designer can spend an entire career endlessly tweaking cars, but an O.R practitioner has to diversify and cannot expect to retire with the same company by endlessly tweaking a product that she or he created and cherished much. O.R. is such a nebulous and ill-defined field in practice that your next manager or director may not have clue as to what the heck your field is, let alone what it is that you have been doing so far. Without strong backing from the highest levels within the management ("Edelman VPs"), the best O.R. efforts can come to nought or go straight to conference and we, the practitioners, have to move on to a different job.

Anyway, this is just one person's take. It would be instructive to hear the experiences of other practitioners.

Wednesday, November 18, 2009

Theory of Inadvertent Cutting Planes and 2-D LSP

The perils of employing heuristics of unknown quality are often disregarded in practice, all in the interest of 'time to market' and 'practical' solutions for NP-Hard optimization problems. See, for example, Dr. Gerald Brown's papers and presentations along with the late Dr. Rick Rosenthal on this topic. (also see old post on 'the paradox of optimality'). Importantly, Dr. Brown reminds us of the huge difference between 'known unknowns' and 'unknown unknowns', before we start to make the poor assumption that NP-Hard automatically implies a quick, randomized heuristic approach. Dr. Michael Trick's recent blog entry on NP-Hardness is illuminating. Such heuristics do have a role to play in O.R. practice, depending on the business problem at hand. We attempt to illustrate, to the non-technical audience in particular, using a simple example:
The 2-D Laughing Stock Problem

PICTURE 1: shows the feasible region (a polygon), the optimal solution, and the one the heuristic algorithm found.

PICTURE 2: shows the new constraint added by the user that reduces the feasible space. The previous heuristic solution is infeasible now. Solver re-optimizes.

PICTURE 3: shows the new heuristic solution that is near-global optimal. The bewildering user experience so far is that he/she has added a highly restrictive constraint, yet the app ended up with a dramatically better solution, one even better than the "optimal". Imagine driving a car that has such heuristics built into its steering response.

Sunday, November 1, 2009

On decisioneering and dealing with sneering detractors

Part of an O.R practitioners job involves selling O.R to non-believers in the organization. Yet many of us in the O.R comfort-zone are firm non-believers that there even exist such non-believers. After all, isn't 'science of better' or its applied counterpart 'decisioneering' self-explanatory? It isn't. The 'analytics' bandwagon is going to ensure that. Last time we looked at the identity crisis facing the poor OR guy. Today, we'll examine more related aspects.

When we say a product has got 'O.R inside', what do we really mean? Is it because it's been autographed by that lost O.R scientist whose owlish ^oo^ spectacles always makes u think 'infinite loop', or, is it the bullet-proof C++ codes of O.R algorithms, the fiendishly reformulated optimization model, or the brand-new, low-latency, 16M$, 32-node, 64-bit, 128-GB SMP RAM parallel machine (yummy!) that smashes thru all your Lagrangian subproblems in a jiffy? or perhaps it's all in the GUROBI or CPLEX solvers that implements the fundamental algorithms?

The old bilateral debate of man v machine, in this context, starts with 'Math v Programming', and in true O.R fashion, cascades into some NP-complete combinatorial debate. heh. The obvious answer to many may be 'all the above', but called me biased - I feel that its the well-trained O.R grad, her/his model and solution approach that seals the deal here. Everything else is essentially a commodity, and can be quickly purchased, and therefore form the supporting cast (The real answer of course is 'none of the above'. It's the power point decks that made it all happen).

Seriously, a practitioner has to have all the soft skills to ensure that O.R gets some small share of credit in such projects, especially when things go right. After all, when its fails, its because of the O.R inside. It's because of you. Everything else was purchased and they work just fine! Suddenly, you alone know which constraint is hurting profits the most, or why a few more discrete variables kill run-times, or if the exponential service time assumption holds. Which brings me to probabilistic 'OR inside' models in practice (more on that another day). By design, its going to give you 'wrong' answers some of the time - unlike deterministic models that provide the illusion of correctness all the time. Good luck selling that!