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Let's say there are 10 jobs and 15 workers. The objective is to assign jobs to workers which could satisfy the jobs’ requirement and minimizing total job processing time.

For each iteration, a job is selected randomly and assigned to worker with next less processing time than the current assigned worker. For example, current assigned worker, let’s say worker 3: processing time is 10. The next less processing time is 8 at worker 5, so the job is assigned to worker 5.

My question is, how do I determine the stopping criteria for the iterations? For the time being, I just set the number of iterations to the number of jobs or to the number of workers.

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    If this is a homework assignment which it sounds like some programming assignment I would think the stopping criteria would be for you to decide and support with why you decided on that specific criteria. If it's not a homework assignment the stopping criteria should still be your choice and you would need to support it with why you chose to stop after x iterations such as the total job processing time has not changed after 200 iterations therefore I ended the code since there was no change. Upon repeated tests the total job processing time supports my stopping criteria of x iterations. – Khaltazar Dec 17 '14 at 01:50
  • thank you @Khaltazar, this is not homework assignment, I'm trying to understand and implement hill climbing and the scenario given is a simplified one. For the time being, I tested with different number of iterations until it can't be improved anymore by trial and error. I'm just wondering if the number of parameters involved, for example the number of tasks, plays any role in determining the stopping criteria. – user3562921 Dec 17 '14 at 02:03

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