
December 8th, 2011, 07:15 PM
#1
Optimization Problem
Consider a group of n robots, and m tasks. Tasks can be different and robots are specialized, hence a given robot can only perform certain tasks. For each robot, you are given a list of tasks that the robot is capable of performing. Assume each task takes one day to perform from start to finish, and once a robot starts working on a task, it completes it before starting on another task. Also, the robots cannot multitask. Finally, assume that there are no dependencies among tasks and any subset of tasks can be worked on concurrently by qualified robots.
Formulate a solution based on previous algorithms to maximize number of tasks that can be completed in a single day based on the constraints above.
Thank you for your help! I just need help figuring out which optimization algorithm would be best for this problem so I can move forward with writing code for the algorithm.

December 9th, 2011, 01:02 AM
#2
Re: Optimization Problem
Sounds related to, but not exactly equal to, the Nurse Scheduling Problem: https://en.wikipedia.org/wiki/Nurse_scheduling_problem
Best Regards,
BioPhysEngr
http://blog.biophysengr.net

All advice is offered in good faith only. You are ultimately responsible for effects of your programs and the integrity of the machines they run on.

December 9th, 2011, 01:03 AM
#3
Re: Optimization Problem
Also: there is a whole library of literature on scheduling algorithms. You might Google around a bit to see what others have done.
Best Regards,
BioPhysEngr
http://blog.biophysengr.net

All advice is offered in good faith only. You are ultimately responsible for effects of your programs and the integrity of the machines they run on.

December 9th, 2011, 01:16 AM
#4
Re: Optimization Problem
Hi BioPhysEngr,
I dont think that is the solution I was looking for regarding this problem. I believe a graphing algorithm such as BFS or DFS is supposed to be used to implement the solution to this problem. Any ideas on what graphing algorithm might be best for this problem?
Thanks!
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