title: How to set SetGlobalSpanCostCoefficient and the capacity parameter in AddDimension properly?
body: <p>I'm using OR-Tool to solve a VRP problem. I have experimented a bit with the exemple problem in the doc and managed to write a functioning program, but, I do not understand the purpose of the SetGlobalSpanCostCoefficient and how to set it properly. According to <a href="http://google.github.io/or-tools/python/ortools/constraint_solver/pywrapcp.html#pywrapcp.RoutingDimension.SetGlobalSpanCostCoefficient" rel="noreferrer">this site</a>,
  it is the coefficient between the global span cost and the difference between the max and min dimension value in all routes.
  So, is this global cost the sum of all the routes's costs and is it calculated from the 'capacity' parameter of the Dimension and used like  a maximum capacity limiter.</p>
  
  <p>The problem in my code is that somme vihecles are not used unless I tweak the capacity (maximum route distance) in the AddDimension function and that globalSpanCostcoefficient manually.
  I have a 1000 nodes : 
  <img src="https://i.stack.imgur.com/jB99Y.png" alt="">
  With a distribution of distances (in meters) that looks likes this :
  <img src="https://i.stack.imgur.com/x80B7.png" alt=""></p>
  
  <pre class="lang-py prettyprint-override"><code>
      # Add Distance constraint.
      distance_dimension_name = 'Distance'
      routing.AddDimension(
          transit_callback_index,
          0,  # no slack
          25000,  # vehicle maximum travel distance
          True,  # start cumul to zero
          distance_dimension_name)
      distance_dimension = routing.GetDimensionOrDie(distance_dimension_name)
      distance_dimension.SetGlobalSpanCostCoefficient(100)
  
  </code></pre>
  
  <p>Here, I get a max route distance of 2610m with 5 and 6 vehicles and it clusters in two routes.
  I have tried to add a counting dimension like it is described <a href="https://activimetrics.com/blog/ortools/counting_dimension/" rel="noreferrer">here</a> but it becomes too slow even for a 100 nodes and the results are the same with 5 vehicles.</p>