We’re generating the data that we might get from a shop floor to run, test, and validate our machine learning models. We first have here a discrete event simulation model for our manufacturing system. Each production order is seen as an agent, which then goes through different processes with a queue (waiting time) and delays (firstly production time, secondly logistics time).
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But sometimes we have one process, for example, printing (code 5A, after the second Select5Output), with three different machines, which do not have a particular capacity. It’s time when we divide our order into parts and send them to those machines (very randomly, subjectively).
The data we take is from flowchart_process_states_log in Database.
The data we take is from flowchart_process_states_log in Database.
My questions here are:
How can we define the number of products in each order? Ex. we’re printing card, for one order it may be 10k, for another 8k or 33k. Can we define it as agent’s parameter? Then how can we vary them (stochastically, no exact number needed).
How can we split those 10k cards into three different machines? And then how to get back an complete agent with 10k? The Agent ID should remain the same as we trace and analyse them in ML model. Is it reasonable to see an order as an agent?
How can we multiply the number of our agent after a process? Ex. After cutting 10k pieces we have 20k.
We have the distribution for delay ex. triangle distribution. But we want some disturbances, when it suddenly takes 2 days for that delay instead of 3-4 hours as normal. How to do it?
Thank you in advance for your effort. Every help is highly appreciated, because we're here and learning together. Thank you !!
I'm an undergrad student in the Philippines and were currently using Anylogic Software for our thesis. May I ask how to put a chart/time plot that consists the traffic flow (vehicle per hour) so that we can justify that our study area is congested? Thank you and have a good day.
The are a number of options you can use but the simplest option is to count the total number of cars in the system.
For this simply add a Road Network Descriptor and select the road network you want it to represent
Then you add a time plot, and set the value that needs to be plotted as roadNetworkDescriptor.size() and set the update time to hour
I'm new to Tableau Desktop, so I'm guessing what I want to do is simple, but I don't know how to do it.
Basically, I have basketball data that gives me players total points scored over several seasons with different NBA teams. I'm trying to sort that data by team, based on the amount that each player scored for each specific team.
Right now, I have the data sorted by team, player, and the total number of points scored. The problem is - I don't actually want the total sum. (E.g. right now Shaq is listed first under the Celtics because he has the most career points out of anyone who played for the Celtics, but not for the Celtics themselves.)
Can someone tell me how I would go about sorting by sum points by team?
This is actually such a common issue that Tableau references a solution in their official training material. If my understanding of your requirement is correct, the following should solve the problem.
http://kb.tableau.com/articles/knowledgebase/finding-top-n-within-category
In an online manager game (like Hattrick), I want to simulate matches between two teams.
A team consists of 11 players. Every player has a strength value between 1 and 100. I take these strength values of the defensive players for each team and calculate the average. That's the defensive quality of a team. Then I take the strengths of the offensive players and I get the offensive quality.
For each attack, I do the following:
$offFactor = ($attackerTeam_offensive-$defenderTeam_defensive)/max($attackerTeam_offensive, $defenderTeam_defensive);
$defFactor = ($defenderTeam_defensive-$attackerTeam_offensive)/max($defenderTeam_defensive, $attackerTeam_offensive);
At the moment, I don't know why I divide it by the higher one of both values. But this formula should give you a factor for the quality of offense and defense which is needed later.
Then I have nested conditional statements for each event which could happen. E.g.: Does the attacking team get a scoring chance?
if ((mt_rand((-10+$offAdditionalFactor-$defAdditionalFactor), 10)/10)+$offFactor >= 0)
{ ... // the attack succeeds
These additional factors could be tactical values for example.
Do you think this is a good way of calculating a game? My users say that they aren't satisfied with the quality of the simulations. How can I improve them? Do you have different approaches which could give better results? Or do you think that my approach is good and I only need to adjust the values in the conditional statements and experiment a bit?
I hope you can help me. Thanks in advance!
Here is a way I would do it.
Offensive/Defensive Quality
First lets work out the average strength of the entire team:
Team.Strength = SUM(Players.Strength) / 11
Now we want to split out side in two, and work out the average for our defensive players, and our offensive players.]
Defense.Strength = SUM(Defensive_Players.Strength)/Defensive_Players.Count
Offense.Strength = SUM(Offense_Players.Strength)/Offense_Players.Count
Now, we have three values. The first, out Team average, is going to be used to calculate our odds of winning. The other two, are going to calculate our odds of defending and our odds of scoring.
A team with a high offensive average is going to have more chances, a team with a high defense is going to have more chance at saving.
Now if we have to teams, lets call them A and B.
Team A, have an average of 80, An offensive score of 85 and a defensive score of 60.
Team B, have an average of 70, An offensive score of 50 and a defensive score of 80.
Now, based on the average. Team A, should have a better chance at winning. But by how much?
Scoring and Saving
Lets work out how many times goals Team A should score:
A.Goals = (A.Offensive / B.Defensive) + RAND()
= (85/80) + 0.8;
= 1.666
I have assumed the random value adds anything between -1 and +1, although you can adjust this.
As we can see, the formula indicates team A should score 1.6 goals. we can either round this up/down. Or give team A 1, and calculate if the other one is allowed (random chance).
Now for Team B
B.Goals = (B.Offensive / A.Defensive) + RAND()
= (50/60) + 0.2;
= 1.03
So we have A scoring 1 and B scoring 1. But remember, we want to weight this in A's favour, because, overall, they are the better team.
So what is the chance A will win?
Chance A Will Win = (A.Average / B.Average)
= 80 / 70
= 1.14
So we can see the odds are 14% (.14) in favor of A winning the match. We can use this value to see if there is any change in the final score:
if Rand() <= 0.14 then Final Score = A 2 - 1 B Otherwise A 1 - 1 B
If our random number was 0.8, then the match is a draw.
Rounding Up and Further Thoughts
You will definitely want to play around with the values. Remember, game mechanics are very hard to get right. Talk to your players, ask them why they are dissatisfied. Are there teams always losing? Are the simulations always stagnant? etc.
The above outline is deeply affected by the randomness of the selection. You will want to normalise it so the chances of a team scoring an extra 5 goals is very very rare. But a little randomness is a great way to add some variety to the game.
There are ways to edit this method as well. For example instead of the number of goals, you could use the Goal figure as the number of scoring chances, and then have another function that worked out the number of goals based on other factors (i.e. choose a random striker, and use that players individual stats, and the goalies, to work out if there is a goal)
I hope this helps.
The most basic tactical decision in football is picking formation, which is a set of three numbers which assigns the 10 outfield players to defence, midfield and attack, respectively, e.g. 4/4/2.
If you use average player strength, you don't merely lose that tactic, you have it going backwards: the strongest defence is one with a single very good player, giving him any help will make it more likely the other team score. If you have one player with a rating of 10, the average is 10. Add another with rating 8, and the average drops (to 9). But assigning more people to defence should make it stronger, not weaker.
So first thing, you want to make everything be based on the total, not the average. The ratio between the totals is a good scale-independent way of determining which teams is stronger and by how much. Ratios tend to be better than differences, because they work in a predictable way with teams of any range of strengths. You can set up a combat results table that says how many goals are scored (per game, per half, per move, or whatever).
The next tactical choice is whether it is better to have one exceptional player, or several good ones. You can make that matter that by setting up scenarios that represent things that happen in game, e.g. a 1 on 1, a corner, or a long ball. The players involved in a scenario are first randomly chosen, then the result of the scenario is rolled for. One result can be that another scenario starts (midfield pass leads to cross leads to header chance).
The final step, which would bring you pretty much up to the level of actual football manager games, is to give players more than one type of strength rating, e.g., heading, passing, shooting, and so on. Then you use the strength rating appropriate to the scenario they are in.
The division in your example is probably a bad idea, because it changes the scale of the output variable depending on which side is better. Generally when comparing two quantities you either want interval data (subtract one from the other) or ratio data (divide one by the other) but not both.
A better approach in this case would be to simply divide the offensive score by the defensive score. If both are equal, the result will be 1. If the attacker is better than the defender, it will be greater than 1, and if the defender is stronger, it will be less than one. These are easy numbers to work with.
Also, instead of averaging the whole team, average parts of the team depending on the formations or tactics used. This will allow teams to choose to play offensively or defensively and see the pros and cons of this.
And write yourself some better random number generation functions. One that returns floating point values between -1 and 1 and one that works from 0 to 1, for starters. Use these in your calculations and you can avoid all those confusing 10s everywhere!
You might also want to ask the users what about the simulation they don't like. It's possible that, rather than seeing the final outcome of the game, they want to know how many times their team had an opportunity to attack but the defense regained control. So instead of
"Your team wins 2-1"
They want to see match highlights:
"Your team wins 2-1:
- scored at minute 15,
- other team took control and went for tried for a goal at minute 30,
but the shoot was intercepted,
- we took control again and $PLAYER1 scored a beautiful goal!
... etc
You can use something like what Jamie suggests for a starting point, choose the times at random, and maybe pick who scored the goal based on a weighted sampling of the offensive players (i.e. a player with a higher score gets a higher chance of being the one who scored). You can have fun and add random low-probability events like a red card on a player, someone injuring themselves, streakers across the field...
The average should be the number of players... using the max means if you have 3 player teams:
[4 4 4]
[7 4 1]
The second one would be considered weaker. Is that what you want? I think you would rather do something like:
(Total Scores / Total Players) + (Max Score / Total Players), so in the above example it would make the second team slightly better.
I guess it depends on how you feel the teams should be balanced.
On a website, everything is tagged with keywords assigned by the staff (it's not a community driven site, due to its nature). I am able to determine which tags a user is most active in (or, what tags they view the most). However, I'm not sure how to choose the list. A few options present themselves, but they don't seem right to me.
Take the top n (or m < n if they have fewer than n viewed tags) tags
Take the top n tags where n is a percentage of the total tags viewed
Take the top n tags with m views where n and m are percentages of total tags viewed and total page views
Take all of the tags, regardless of views
The goal is to identify what is most interesting to the user and show them other things that they might be interested in, with respect to the tags that are assigned to the content.
You could look at machine learning algorithms to find algorithms with which to evaluate the effectiveness of your choice.
Like for instance: http://en.wikipedia.org/wiki/Supervised_learning#Approaches_and_algorithms Stuff like nearest neighbour and bayes could help you improve your suggestions.
This is however overkill for just suggesting "Would you like to look at this too?", but it's an interesting approach to providing better tie-ins. It would, however require some method to figure out whether or not your users value your suggestions (e.g. "I like this!"-links or log-analysis based on time spent on links, etc.)
A simple solution is to try several reports and check which report is more informative. The nature of your site and your data may mean that some reports are unexpectedly useful and some are not. If a report get a 'flat' area chart for example - look for something else.
Even better give the consumers of the reports a choice and an ability to provide feedback. Tune the reports based on what they will be really looking for.
P.S. I would go fro the "Take the top n tags with m views where n and m are percentages of total tags viewed and total page views" report first