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    <p>I agree with Jiri...you want a heuristic algorithm that gets reasonably close with an acceptable solution quickly and then refines from there.</p> <p>I've worked at companies before that had delivery routing software and the approach they took was to use a genetic algorithm to solve very similar, though much larger scale, problem. Take a <a href="http://www.obitko.com/tutorials/genetic-algorithms/" rel="nofollow noreferrer">look here</a> if you're not familiar with the approach. From that site:</p> <ol> <li>[Start] Generate random population of n chromosomes (suitable solutions for the problem)</li> <li>[Fitness] Evaluate the fitness f(x) of each chromosome x in the population</li> <li><p>[New population] Create a new population by repeating following steps until the new population is complete</p> <p>[Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)</p> <p>[Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents.</p> <p>[Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).</p> <p>[Accepting] Place new offspring in a new population</p></li> <li>[Replace] Use new generated population for a further run of algorithm</li> <li>[Test] If the end condition is satisfied, stop, and return the best solution in current population</li> <li>[Loop] Go to step 2</li> </ol> <p>The trick to this is encoding your constraints into a "chromosome" and writing the "fitness" function. The fitness function has to take inputs of the results of a potential solution and spit out a score of how good a solution that is or throw it out if it violates any of the constraints.</p> <p>As mentioned by Jiri, the advantage of this solution is that it comes up with a workable, though maybe not the best, answer very quickly and the longer you let it run, the better the solution gets.</p>
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    1. POVehicle Routing / Resource Scheduling Algorithm Design
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    1. USRobert Neville
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    1. COThanks for the responses guys. I've used some simple GAs before for some other tasks so I'm familiar with the approach. The Fitness function shouldn't be too difficult - its a simple cost equation. I'll have to put some thought into encoding the chromosome, as it needs to capture - car - trailer - start time - task ID - any "empty runs" to balance the trailers
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      1. USwill-hart
 

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