First the original story today doing the rounds on the Internet on bees solving a TSP while traversing a sequence of flowers for nectar. Their approach resembles the ant-colony / swarm-optimization approach, and while this 'bee story' is truly astounding, the author further states the computers have to explore every possible path to select the best and that takes days. Obviously, they did not hear about Operations Research, and the fantastic work done on inventing efficient algorithms for solving gigantic and difficult TSP problems to provable (near-) global optimality in quick time. The fantastic book on TSP by Dr. Applegate, Dr. Bixby, et al. gives us a fascinating blow-by-blow account of the work on this topic from the TSP past to the present. You can even try out their Concorde solver. The state-of-the-art is truly amazing and based on decades of breakthrough ideas.

Yet, article after article that seem to come out every couple of years assumes that if a problem is NP-Hard, then it is almost "impossible to solve efficiently in practice" and enumeration is the only way. Nope. A pure application of software programming and hardware strategies certainly does not work or scale. OR leads the way.

An incredible amount of success in OR practice has come via successfully tackling precisely such large-scale and ultra-complex NP-Hard problems. Large-scale TSP-structured problems have been routinely solved in Supply-chain planning, vehicle routing, aircraft routing, etc. Not only are problems solved well and quickly, but we also know how well and how much improvement is still possible. These small optimality gaps matter. A TSP solution that is 1% closer to optimality may save a company another million dollars.

Heuristics of unknown quality based on bees and ants are nice, but they don't really tell you if there is another solution that could your save your company another 10%. And if you were to change your input parameters, it may return an entirely different solution that makes such approaches largely unpractical for use within products. And adding a constraint the prohibits certain paths may well make such approaches useless. This Tab has discussed the practical problems with using such heuristics in previous posts here, and you will find more in Dr. Trick's OR Blog when TSP was "solved" last year by creatures with even smaller brains, i.e. bacteria :-)

There's even TSP art. Check this out as well. Let's give O.R. and humans some credit please!

## Monday, October 25, 2010

## Saturday, October 23, 2010

### Maximizing Dignity: The Traveling Angel Problem

Can one person feed 400 hungry mouths in the temple city of Madurai in South India, every day of the year?

Mr. Krishnan is a good human being. In today's world, we have to start looking for those first before we start looking for heroes. I was happy to see that he was nominated for the "CNN hero of the year" award for this work. Hopefully this publicity will translate into more help for his operation. The talented Mr. K, at the age of 22, was all set to be a five-star chef in Switzerland seven years ago, but chucked all that when he saw an hungry old man in his home town doing the unthinkable. Since then, without taking a single day off, he is up at 4am to cook tasty and fresh vegetarian food. He then drives around the town of Madurai (most famous for it's amazing Meenakshi Amman Kovil - the temple of a 1000 pillars) feeding the destitute and the hungry. He often hand-feeds them and gives them haircuts along with a meal. His focus is on restoring the dignity of a human being. Is that not fundamental? Simply put, in this temple city, Krishnan is the Goddess Annapoorani to these castaway people. Contribute generously to the Sakthi foundation's Askhaya trust if you can (paypal accepted too. cool). Even a small amount goes a long way.

O.R. is the far less important part here, but one wonders how this amazing person chooses his 125 mile driving route? He has a limited quantity of freshly prepared food stocked in his van that has to reach 400 desperately hungry mouths all over his city as effectively and efficiently as possible. OR should be helping in such practical situations. We can't always be for and about profit-maximization models for corporations, weapons and target acquisition for the military, or abstract academic problems. The logistics of Mr. K's 'meals on wheels' operation contains a classical vehicle routing / traveling salesman problem element. A more efficient operation and utilization of scarce resources means that more people can be fed and more shattered lives can rebuilt and dignity restored. The google map of the city of Madurai is shown below.

View Larger Map

Who will optimize Krishnan's supply chain?

Mr. Krishnan is a good human being. In today's world, we have to start looking for those first before we start looking for heroes. I was happy to see that he was nominated for the "CNN hero of the year" award for this work. Hopefully this publicity will translate into more help for his operation. The talented Mr. K, at the age of 22, was all set to be a five-star chef in Switzerland seven years ago, but chucked all that when he saw an hungry old man in his home town doing the unthinkable. Since then, without taking a single day off, he is up at 4am to cook tasty and fresh vegetarian food. He then drives around the town of Madurai (most famous for it's amazing Meenakshi Amman Kovil - the temple of a 1000 pillars) feeding the destitute and the hungry. He often hand-feeds them and gives them haircuts along with a meal. His focus is on restoring the dignity of a human being. Is that not fundamental? Simply put, in this temple city, Krishnan is the Goddess Annapoorani to these castaway people. Contribute generously to the Sakthi foundation's Askhaya trust if you can (paypal accepted too. cool). Even a small amount goes a long way.

O.R. is the far less important part here, but one wonders how this amazing person chooses his 125 mile driving route? He has a limited quantity of freshly prepared food stocked in his van that has to reach 400 desperately hungry mouths all over his city as effectively and efficiently as possible. OR should be helping in such practical situations. We can't always be for and about profit-maximization models for corporations, weapons and target acquisition for the military, or abstract academic problems. The logistics of Mr. K's 'meals on wheels' operation contains a classical vehicle routing / traveling salesman problem element. A more efficient operation and utilization of scarce resources means that more people can be fed and more shattered lives can rebuilt and dignity restored. The google map of the city of Madurai is shown below.

View Larger Map

Who will optimize Krishnan's supply chain?

## Monday, October 18, 2010

### Chakravala - Decision Analytics in Ancient India

More than two thousand years ago, many priests in India had to double up as mathematicians. They practiced the Sanathana Dharma, 'the eternal way of ethical living' (or Hinduism as it is popularly known today). Hinduism is richly influenced by nature, as well as the earthly and celestial elements, and fire rituals were quite important in those times. Careful attention was paid to the geometry of the altar, since different shapes were required depending on the objective of the ritual. This naturally gave rise to analytics, and a 'textbook' in those days (800-200 BCE) was the 'Sulba Sutras' to help figure out the correct angles and lengths to optimally design them (that lead to the discovery of Pythagorean triples and trigonometry, among other things). Inevitably, the beautiful natural patterns inherent in numbers awoke the inner geek in some of these priests.

Among the many famous mathematicians who carried forward this rich Vedic tradition was an astronomer named Brahmagupta (~ 600 CE). He extensively explored solutions to linear Diophantine equations that are central to integer programming today. Of course, his bigger distinction is for 'much ado about nothing'. He is known to be the first human being to clearly define, publish, and use zero as a number! He also explored Diophantine equations of the second degree, and came up with ideas that led to a recursive, iterative solution method for such equations; again something that is very useful in modern numerical optimization. He generalized an idea discovered by Diophantus and used this to achieve some success in finding solutions to Pell's equation. This was generalized to the Chakravala (Sanskrit for 'cyclic') algorithm by Jayadeva (950CE) and Bhaskara II (1100CE). A key subroutine at an iteration involves a neat rational scaling operation, followed by the solving of a simple discrete optimization problem that finds an integer

The Chakravala turns out to be an easy-to-use iterative method to find good approximations for square roots of integers. Indeed, this approach has been recognized for its ingenuity and 'careful simplicity' that allows us to work with well-conditioned real numbers; ideas that we in the OR community know are critical to matrix refactoring within a successful dual simplex implementation, for example.

Among the many famous mathematicians who carried forward this rich Vedic tradition was an astronomer named Brahmagupta (~ 600 CE). He extensively explored solutions to linear Diophantine equations that are central to integer programming today. Of course, his bigger distinction is for 'much ado about nothing'. He is known to be the first human being to clearly define, publish, and use zero as a number! He also explored Diophantine equations of the second degree, and came up with ideas that led to a recursive, iterative solution method for such equations; again something that is very useful in modern numerical optimization. He generalized an idea discovered by Diophantus and used this to achieve some success in finding solutions to Pell's equation. This was generalized to the Chakravala (Sanskrit for 'cyclic') algorithm by Jayadeva (950CE) and Bhaskara II (1100CE). A key subroutine at an iteration involves a neat rational scaling operation, followed by the solving of a simple discrete optimization problem that finds an integer

*m*, such that it minimizes |*m*^{2}−*N|/*k*,*where N and k are input parameters. Furthermore, they recognize that such problems have degenerate solutions, and in some cases, find minimal integer feasible solutions using this approach. This attention to detail toward handling numerical issues stands out. Recognizing and tackling degeneracy is at the very heart of modern decision-analytics practice!The Chakravala turns out to be an easy-to-use iterative method to find good approximations for square roots of integers. Indeed, this approach has been recognized for its ingenuity and 'careful simplicity' that allows us to work with well-conditioned real numbers; ideas that we in the OR community know are critical to matrix refactoring within a successful dual simplex implementation, for example.

## Tuesday, October 12, 2010

### Popular Mechanics and the Frankenstein design principle

Popular Mechanics is a favorite magazine for the sheer variety of mechanical gizmos it throws up inside its pages (check out the Jaguar supercar). It is a permanent fixture in many auto-service shop waiting rooms where we spend a good part of a lifetime. The October 2010 issue of Popular Mechanics was a pleasant surprise - not one but two OR-related topics were covered. The first one was a short article on optimizing waiting experience in queues. The metriclastic US population (is that a new word?) that shuns neat divisions by 10, rightly resents having to spell Q using 5 letters, one of which is Q, and simply calls it a 'line'. On the other hand, 'line theory' is not too informative. Pop-mech doesn't mention OR explicitly, but we know that queuing theory and OR are inseparable.

The queuing article (online version here), among other things, mentions that a smart researcher in Taiwan, Pen-Yuan Liao, derived an equation to compute a 'Balking Index' that tells you when and how many customers are likely to flee a long line and 'defect' to a better one in a multi-Q system. Obviously, information like this helps determine optimal staffing levels to meet the required service levels, minimize costs, and improve the customer experience. Apart from just analyzing people standing in line, queuing models have many and diverse applications and is a whole field of study. There's also some expert comments in the magazine article by Dr. Richard Larson from MIT. His name would be familiar to the OR fraternity.

The second article talks about risk management in the context of the Gulf-Coast oil spill. This tab's summary of the article from an OR perspective is this: When it comes to designing and operating complex systems, there needs to be a greater emphasis on managing conditional expectations associated with low-probability high-consequence events. This is in addition to tracking traditional risk metrics (minimizing expected cost, probability of failure, etc), i.e. we should be tracking multiple risk objectives, something i recall working on many years ago as a grad student.

The Frankenstein principle

Dr. Petroski, a civil engineering professor at the Duke University quotes in the article, "When you have a a robust system, you tend to relax". And it's proven to be true, sadly. BP continually pushed the risk-envelope to boost profits without paying adequate attention to the associated safety trade-off. In part, this was due to a false sense of safety in a historically robust system. There's always a first time, and shockingly, there was no plan 'B' in the event of a catastrophic failure. Bhopal, Chernobyl, Gulf coast and a few more like these have occurred in just the last 30 years. BP engineers were left having to prove that components would fail rather than answer the question "is it safe to operate?" - two completely different propositions. This practice of hastening project completion by placing an unfair burden of proof on the scientists and engineers may be widespread.

The Frankenstein design principle extends Murphy's law. It simply states that if for some crazy reason, you want to build something monstrously complex, then at least design it assuming apriori that at some point it will fall apart and come back to snack on your aposteriori.

The queuing article (online version here), among other things, mentions that a smart researcher in Taiwan, Pen-Yuan Liao, derived an equation to compute a 'Balking Index' that tells you when and how many customers are likely to flee a long line and 'defect' to a better one in a multi-Q system. Obviously, information like this helps determine optimal staffing levels to meet the required service levels, minimize costs, and improve the customer experience. Apart from just analyzing people standing in line, queuing models have many and diverse applications and is a whole field of study. There's also some expert comments in the magazine article by Dr. Richard Larson from MIT. His name would be familiar to the OR fraternity.

The second article talks about risk management in the context of the Gulf-Coast oil spill. This tab's summary of the article from an OR perspective is this: When it comes to designing and operating complex systems, there needs to be a greater emphasis on managing conditional expectations associated with low-probability high-consequence events. This is in addition to tracking traditional risk metrics (minimizing expected cost, probability of failure, etc), i.e. we should be tracking multiple risk objectives, something i recall working on many years ago as a grad student.

The Frankenstein principle

Dr. Petroski, a civil engineering professor at the Duke University quotes in the article, "When you have a a robust system, you tend to relax". And it's proven to be true, sadly. BP continually pushed the risk-envelope to boost profits without paying adequate attention to the associated safety trade-off. In part, this was due to a false sense of safety in a historically robust system. There's always a first time, and shockingly, there was no plan 'B' in the event of a catastrophic failure. Bhopal, Chernobyl, Gulf coast and a few more like these have occurred in just the last 30 years. BP engineers were left having to prove that components would fail rather than answer the question "is it safe to operate?" - two completely different propositions. This practice of hastening project completion by placing an unfair burden of proof on the scientists and engineers may be widespread.

The Frankenstein design principle extends Murphy's law. It simply states that if for some crazy reason, you want to build something monstrously complex, then at least design it assuming apriori that at some point it will fall apart and come back to snack on your aposteriori.

## Wednesday, October 6, 2010

### Video analysis - followup to previous post on "Should Steve Smith have gone for the run-out?"

Updated Oct 7: youtube video.

As can be seen from the video footage of the last few minutes of the test match, Steve Smith came incredibly close to settling the issue by taking the initiative in a moment of cricketing chaos. He was fielding at point, and fired in the throw at a pretty acute angle - a bold gamble. He brushed against legend but it ended up being India's greatest sporting victory. Another great article by Australian sports writer Peter Roebuck, can be read here.

As can be seen from the video footage of the last few minutes of the test match, Steve Smith came incredibly close to settling the issue by taking the initiative in a moment of cricketing chaos. He was fielding at point, and fired in the throw at a pretty acute angle - a bold gamble. He brushed against legend but it ended up being India's greatest sporting victory. Another great article by Australian sports writer Peter Roebuck, can be read here.

## Tuesday, October 5, 2010

### OR and cricket: Should Steve Smith have gone for the run-out? - India versus Australia, 2010

Nearly a year ago, the mathletics blog of Dr. Wayne Winston has an interesting analysis of whether Pats coach Bellicheck should have gone for it on a critical 4th down, and put the coach's decision down to his confidence in Tom Brady. Yesterday, some thing similar (but far more serious) happened on the last day (D5) of one of the greatest test cricket matches in history.

The result

Check out the amazing scorecard. In the 120-year history of test cricket, there have been only 12 such finishes.

The match situation

It is day 5 of the contest. About 26 hours of play time has passed and we are into the bottom of the 4th and final innings. India is batting and has lost 9 of their 10 wickets on a wicked last-day pitch with widening cracks and the ball spitting off the pitch. Many have gotten out to bouncers. Just an hour ago, India was down and out having lost 8 wickets with 92 runs still to get. A remarkable partnership between two injured players brings the match to a screaming knife edge. Australia requires 1 wicket to win. India needs 6 runs. At the crease is the injured Indian genius VVS Laxman overcoming back spasms with painkillers and with sheer grit, he is attempting to steal a miraculous win. But the one who is taking strike is P Ojha, a spin-bowler and India's no.11 player, who can't really bat. Bowling is Australian fast bowler Mitchell Johnson who can bowl past speeds of 95mph (there are others who can crank it up to 100mph).

The event

Here's cricinfo's description of the third-from-last ball of the match (edited version here):

Johnson to Ojha, 4 runs, 90.5 mph, Lbw Shout And oh boy what we get .. Four Over throws! That looked out. Was there some wood on leather? Oh well ... What an insane little game this is! .. Steve Smith fires the throw and the ball misses the stumps and runs through the vacant covers. No Aussie fielder could back that up. But that throw was on. Had he hit - and he didn't miss by much - Ojha would have been run out. .....

(india wins 2 balls later)

Post-mortem

Many cricket fans have criticized Steve Smith's decision to take a shy at the stumps. The Australian captain himself felt it was the right call and praised the rookie. If he had hit the wicket it would have been match over. Indeed several sports writers have called it a gutsy and worthy call. I know that if it was an Indian fielder instead, he would have been roasted by India's trigger-happy media.

Probability Model

Probability that australia wins = A. Probability that India wins = 1-A.

There are two scenarios:

1. If successful hit (probability p), then match over, australia win with probability 1.

2. If miss (probability 1-p) then there are two sub-outcomes:

2a. if fielder backing up, no overthrows, and australia win again with probability A (reset).

2b. if no protection, then overthrows, and australia win with probability A' < a =" p(1-A)"> A.

If (2b) is given:

p(win given hit) = p + (1-p)A'

This is statistically a good decision provided p + (1-p)A' >= A. A simple condition where this holds true would be if he were such a good fielder that statitically he hits the wicket more than 100A % of the time, i.e. if he felt that his chance of hitting the wicket was at least as good as the chance that Australia currently has of winning the game (fixing p = A would result in the LHS being > A)

Let's play with some numbers

1. Let's assume that with every run scored, the chance of success for Australia proportionally drops, so the cost of each overthrow run is A/6. For a worst-case 4 overthrows, this gives A' = A-4A/6 = A/3

2. so steve's accuracy rate had to satisfy:

p(1-A/3) >= 2A/3, or p >= 2A/(3-A)

3. If we assume that with just a single wicket to get, but only 6 runs to score, its anybody's game, and set A= 0.5. then steve's accuracy rate had to be atleast 1/2.5.

4. On the other hand, if you start with the premise that A is lower, say 25%, then steve only required an 18% accuracy to justify the throw. In other words, if you perceive that you have little chance of winning, then it is certainly a great idea to take the risk.

Modeling Ideas

You can also calculate 'A' in a more sophisticated manner by looking at the competing counting process to determine P(wicket falls before 6 runs are scored). The probability that a wicket falls in some 'n' balls is a geometric distribution. Each ball is a Bernoulli trial. The distribution of runs per ball could be Poisson. India scored 6 runs in the previous 3 overs (18 balls), so purely statistically, you can extrapolate that to say there's about 18 balls to get the last wicket. All said and done, a 50-50 chance is the most practical choice for 'A' here.

Conclusion

If Steve Smith was generally able to hit the stumps 40% of the time (i.e. slightly less than even chance), then it would have a good call from a statistical point of view. I haven't gotten a chance to review to video to see if it was an easy versus difficult angle to hit the wicket, but on average, 40% does look like a fairly high required conversion rate.

Statistically it was not a great call especially if he knew there was nobody to back up. All the pressure previously built up on the batsman was released. But cricket is played on the field and making a match-defining split-second decision after 26 hours of exhilarating play is no easy ask. No guts, no glory. It's the Aussie way and the glorious game of cricket is better off with that choice, (especially since India won :-)

The result

Check out the amazing scorecard. In the 120-year history of test cricket, there have been only 12 such finishes.

The match situation

It is day 5 of the contest. About 26 hours of play time has passed and we are into the bottom of the 4th and final innings. India is batting and has lost 9 of their 10 wickets on a wicked last-day pitch with widening cracks and the ball spitting off the pitch. Many have gotten out to bouncers. Just an hour ago, India was down and out having lost 8 wickets with 92 runs still to get. A remarkable partnership between two injured players brings the match to a screaming knife edge. Australia requires 1 wicket to win. India needs 6 runs. At the crease is the injured Indian genius VVS Laxman overcoming back spasms with painkillers and with sheer grit, he is attempting to steal a miraculous win. But the one who is taking strike is P Ojha, a spin-bowler and India's no.11 player, who can't really bat. Bowling is Australian fast bowler Mitchell Johnson who can bowl past speeds of 95mph (there are others who can crank it up to 100mph).

The event

Here's cricinfo's description of the third-from-last ball of the match (edited version here):

Johnson to Ojha, 4 runs, 90.5 mph, Lbw Shout And oh boy what we get .. Four Over throws! That looked out. Was there some wood on leather? Oh well ... What an insane little game this is! .. Steve Smith fires the throw and the ball misses the stumps and runs through the vacant covers. No Aussie fielder could back that up. But that throw was on. Had he hit - and he didn't miss by much - Ojha would have been run out. .....

(india wins 2 balls later)

Post-mortem

Many cricket fans have criticized Steve Smith's decision to take a shy at the stumps. The Australian captain himself felt it was the right call and praised the rookie. If he had hit the wicket it would have been match over. Indeed several sports writers have called it a gutsy and worthy call. I know that if it was an Indian fielder instead, he would have been roasted by India's trigger-happy media.

Probability Model

Probability that australia wins = A. Probability that India wins = 1-A.

There are two scenarios:

1. If successful hit (probability p), then match over, australia win with probability 1.

2. If miss (probability 1-p) then there are two sub-outcomes:

2a. if fielder backing up, no overthrows, and australia win again with probability A (reset).

2b. if no protection, then overthrows, and australia win with probability A' < a =" p(1-A)"> A.

If (2b) is given:

p(win given hit) = p + (1-p)A'

This is statistically a good decision provided p + (1-p)A' >= A. A simple condition where this holds true would be if he were such a good fielder that statitically he hits the wicket more than 100A % of the time, i.e. if he felt that his chance of hitting the wicket was at least as good as the chance that Australia currently has of winning the game (fixing p = A would result in the LHS being > A)

Let's play with some numbers

1. Let's assume that with every run scored, the chance of success for Australia proportionally drops, so the cost of each overthrow run is A/6. For a worst-case 4 overthrows, this gives A' = A-4A/6 = A/3

2. so steve's accuracy rate had to satisfy:

p(1-A/3) >= 2A/3, or p >= 2A/(3-A)

3. If we assume that with just a single wicket to get, but only 6 runs to score, its anybody's game, and set A= 0.5. then steve's accuracy rate had to be atleast 1/2.5.

4. On the other hand, if you start with the premise that A is lower, say 25%, then steve only required an 18% accuracy to justify the throw. In other words, if you perceive that you have little chance of winning, then it is certainly a great idea to take the risk.

Modeling Ideas

You can also calculate 'A' in a more sophisticated manner by looking at the competing counting process to determine P(wicket falls before 6 runs are scored). The probability that a wicket falls in some 'n' balls is a geometric distribution. Each ball is a Bernoulli trial. The distribution of runs per ball could be Poisson. India scored 6 runs in the previous 3 overs (18 balls), so purely statistically, you can extrapolate that to say there's about 18 balls to get the last wicket. All said and done, a 50-50 chance is the most practical choice for 'A' here.

Conclusion

If Steve Smith was generally able to hit the stumps 40% of the time (i.e. slightly less than even chance), then it would have a good call from a statistical point of view. I haven't gotten a chance to review to video to see if it was an easy versus difficult angle to hit the wicket, but on average, 40% does look like a fairly high required conversion rate.

Statistically it was not a great call especially if he knew there was nobody to back up. All the pressure previously built up on the batsman was released. But cricket is played on the field and making a match-defining split-second decision after 26 hours of exhilarating play is no easy ask. No guts, no glory. It's the Aussie way and the glorious game of cricket is better off with that choice, (especially since India won :-)

## Monday, October 4, 2010

### Is this umbrella optimally designed?

Building retail pricing products for a living, i spend an inordinate amount of time analyzing online deals. And you get to see lots of innovative new gizmos on sale. The umbrella shown in the video below was on sale and caught my attention.

It was introduced in the market a couple of years ago and has won multiple design awards. It's aerodynamic and looks like a bicycle helmet extended into an umbrella. I checked out the wind tunnel tests - impressive. It doesn't turn inside out. It's got an eye-protective design built-in and also provides better frontal vision. If you are a geek, you'd feel that you would never want to be caught under the old dome again. It is well-marketed. Check out these product stress test videos. I mean the 300 Spartans could have survived using the Senz as a shield.

Let's apply some practical OR tests.

First look at the actual human feedback at Amazon.com. There aren't too many data points sadly. Some like it and some don't. Next we look at the analytics hidden inside the design of the umbrella. We also examine its external functionality. A really nice analytical innovation allows the umbrella to not fight the wind but mildly orient itself along the path of least resistance. Several stress tests empirically prove this.

Conclusion: Handles wind really well and better vision, so you don't feel like you are in a tent.

However, there's something missing in these tests. Let us turn to robust optimization analysis. All the talk is about gale force winds, wind tunnels. etc. What about some simple vertical rain at terminal velocity? How would it perform in Cherrapunji, India or the town of Mawsynram in the nearby Indian state of Meghalaya, which received 1000 inches of rain in 1985. How does it handle those good old pesky, incessant drizzles?How about sleet and randomized rain loads? How about the inevitable sidewalk drenching from the tires of a car on a wet road?

Conclusion - It appears that it is does not perform the basic, everyday function of an "umbrella for a rainy day" as robustly as it tackles wind. There are a couple of feedbacks in Amazon which highlight this basic limitation.

Question: In a randomized rain situation, can i run in a straight line with an aerodynamic umbrella like the Senz to introduce a favorable directional bias in the forces and improve its rain-performance?

Note that when it is steadily raining, and without an umbrella of any kind, the amount of water that your body/dress absorbs is practically the same, whether u walk or run to cover a fixed distance.

In many places in the world, people regularly use an umbrella to block out the hot sun. Since the total area is relatively smaller when compared to the traditional umbrella, it covers less.

Conclusion: less protection against the sun.

Finally, the most interesting comment was a person who said two people couldn't fit under this umbrella. It's going to be a disastrous end to a date when your partner is under your aerodynamic umbrella and getting all wet in the rain.

Final conclusion: The old dome is on average, more robust for most normal rain/sun/slush situations, and is much less expensive. The inner geek will compel you to get a Senz for the cool wind analytics. Or if you are some weather channel tv journo who shows up in hurricane/tornado hit areas. But guys, don't take the Senz with your girlfriend on a rainy day, and certainly not your wife (and most certainly not both - under any umbrella. sorry, couldn't resist).

It was introduced in the market a couple of years ago and has won multiple design awards. It's aerodynamic and looks like a bicycle helmet extended into an umbrella. I checked out the wind tunnel tests - impressive. It doesn't turn inside out. It's got an eye-protective design built-in and also provides better frontal vision. If you are a geek, you'd feel that you would never want to be caught under the old dome again. It is well-marketed. Check out these product stress test videos. I mean the 300 Spartans could have survived using the Senz as a shield.

Let's apply some practical OR tests.

First look at the actual human feedback at Amazon.com. There aren't too many data points sadly. Some like it and some don't. Next we look at the analytics hidden inside the design of the umbrella. We also examine its external functionality. A really nice analytical innovation allows the umbrella to not fight the wind but mildly orient itself along the path of least resistance. Several stress tests empirically prove this.

Conclusion: Handles wind really well and better vision, so you don't feel like you are in a tent.

However, there's something missing in these tests. Let us turn to robust optimization analysis. All the talk is about gale force winds, wind tunnels. etc. What about some simple vertical rain at terminal velocity? How would it perform in Cherrapunji, India or the town of Mawsynram in the nearby Indian state of Meghalaya, which received 1000 inches of rain in 1985. How does it handle those good old pesky, incessant drizzles?How about sleet and randomized rain loads? How about the inevitable sidewalk drenching from the tires of a car on a wet road?

Conclusion - It appears that it is does not perform the basic, everyday function of an "umbrella for a rainy day" as robustly as it tackles wind. There are a couple of feedbacks in Amazon which highlight this basic limitation.

Question: In a randomized rain situation, can i run in a straight line with an aerodynamic umbrella like the Senz to introduce a favorable directional bias in the forces and improve its rain-performance?

Note that when it is steadily raining, and without an umbrella of any kind, the amount of water that your body/dress absorbs is practically the same, whether u walk or run to cover a fixed distance.

In many places in the world, people regularly use an umbrella to block out the hot sun. Since the total area is relatively smaller when compared to the traditional umbrella, it covers less.

Conclusion: less protection against the sun.

Finally, the most interesting comment was a person who said two people couldn't fit under this umbrella. It's going to be a disastrous end to a date when your partner is under your aerodynamic umbrella and getting all wet in the rain.

Final conclusion: The old dome is on average, more robust for most normal rain/sun/slush situations, and is much less expensive. The inner geek will compel you to get a Senz for the cool wind analytics. Or if you are some weather channel tv journo who shows up in hurricane/tornado hit areas. But guys, don't take the Senz with your girlfriend on a rainy day, and certainly not your wife (and most certainly not both - under any umbrella. sorry, couldn't resist).

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