Update Readme for new CSP example

README.md

@@ -43,9 +43,8 @@ how the ALNS library may be used. Of particular interest are,
  instance of 131 cities to within 2.1% of optimality, using simple
  destroy and repair heuristics with a post-processing step.
 - The cutting-stock problem (CSP), [here][4]. We solve an instance with
- 180 orders, over 165 distinct beam sizes. The total stock available
- amounts to 165 beams of length 1000. We solve the instance to within
- 1.35% of optimality in only a very limited number of iterations.
+ 180 beams over 165 distinct sizes to within 1.35% of optimality in
+ only a very limited number of iterations.
 
 ## References
 - Pisinger, D., and Ropke, S. (2010). Large Neighborhood Search. In M.

examples/cutting_stock_problem.ipynb

@@ -113,11 +113,11 @@
  " Solution state for the CSP problem. It has two members, assignments and \n",
  " unassigned. Assignments is a list of lists, one for each beam in use.\n",
  " Each entry is another list, containing the ordered beams cut from this \n",
- " beam. Each such sublist must sum to less than or equal to BEAM_LENGTH.\n",
- " Unassigned is a list of ordered beams that are not currently assigned\n",
- " to one of the available beams.\n",
+ " beam. Each such sublist must sum to at most BEAM_LENGTH. Unassigned is \n",
+ " a list of ordered beams that are not currently assigned to one of the\n",
+ " available beams.\n",
  " \"\"\"\n",
- " \n",
+ "\n",
  " def __init__(self, assignments, unassigned=None):\n",
  " self.assignments = assignments\n",
  " self.unassigned = []\n",
@@ -136,21 +136,16 @@
  " \"\"\"\n",
  " Computes the total number of beams in use.\n",
  " \"\"\"\n",
- " return sum(1\n",
- " for assignment in self.assignments\n",
- " if len(assignment) != 0)\n",
+ " return len(self.assignments)\n",
  "\n",
  " def plot(self):\n",
  " \"\"\"\n",
  " Helper method to plot a solution.\n",
  " \"\"\"\n",
- " lengths = [sum(assignment)\n",
- " for assignment in self.assignments]\n",
- " \n",
  " _, ax = plt.subplots(figsize=(12, 6))\n",
- " \n",
+ "\n",
  " ax.barh(np.arange(len(self.assignments)), \n",
- " lengths, \n",
+ " [sum(assignment) for assignment in self.assignments], \n",
  " height=1)\n",
  "\n",
  " ax.set_xlim(right=BEAM_LENGTH)\n",
@@ -218,10 +213,10 @@
  " poorest assignments are removed first.\n",
  " \"\"\"\n",
  " state = state.copy()\n",
- " \n",
+ "\n",
  " def value(assignment): # helper method for sorting\n",
  " return BEAM_LENGTH - sum(assignment) if len(assignment) != 0 else 0\n",
- " \n",
+ "\n",
  " # Sort assignments by wastage, worst first\n",
  " worst_assignments = sorted(state.assignments, key=value, reverse=True)\n",
  "\n",

setup.py

@@ -2,7 +2,7 @@
 
 MAJOR = 1
 MINOR = 0
-MAINTENANCE = 3
+MAINTENANCE = 4
 MODIFIER = ""
 
 VERSION = "{0}.{1}.{2}{3}".format(MAJOR, MINOR, MAINTENANCE, MODIFIER)