"""
vrp.py:  solve the vehicle routing problem.
            
approach:
    - start with assignment model
    - add cuts until all components of the graph are connected

Copyright (c) by Joao Pedro PEDROSO and Mikio KUBO, 2012
"""
import math
import random
import networkx
from gurobipy import *

def solve_vrp(V,c,m,q,Q):
    """solve_vrp -- solve the vehicle routing problem.
       - start with assignment model (depot has a special status)
       - add cuts until all components of the graph are connected
    Parameters:
        - V: set/list of nodes in the graph
        - c[i,j]: cost for traversing edge (i,j)
        - m: number of vehicles available
        - q[i]: demand for customer i
        - Q: vehicle capacity
    Returns the optimum objective value and the list of edges used.
    """
    
    def addcut(cut_edges):
        """addcut: add constraint to eliminate infeasible solutions
        Parameters:
            - cut_edges: list of edges in the current solution, except connections to depot
        Returns True is a cut was added, False otherwise
        """
        G = networkx.Graph()
        G.add_edges_from(cut_edges)
        Components = networkx.connected_components(G)
        cut = False
        for S in Components:
            S_card = len(S)
            q_sum = sum(q[i] for i in S)
            NS = int(math.ceil(float(q_sum)/Q))
            S_edges = [(i,j) for i in S for j in S if i<j and (i,j) in cut_edges]
            if S_card >= 3 and (len(S_edges) >= S_card or NS > 1):
                add = model.addConstr(quicksum(x[i,j] for i in S for j in S if j > i) <= S_card-NS)
                model.update()
                cut = True
        return cut

    model = Model("vrp")
    x = {}
    for i in V:
        for j in V:
            if j > i and i == V[0]:       # depot
                x[i,j] = model.addVar(ub=2, vtype="I", name="x(%s,%s)"%(i,j))
            elif j > i:
                x[i,j] = model.addVar(ub=1, vtype="I", name="x(%s,%s)"%(i,j))
    model.update()

    model.addConstr(quicksum(x[V[0],j] for j in V[1:]) == 2*m, "DegreeDepot")
    for i in V[1:]:
        model.addConstr(quicksum(x[j,i] for j in V if j < i) +
                        quicksum(x[i,j] for j in V if j > i) == 2, "Degree(%s)"%i)

    model.setObjective(quicksum(c[i,j]*x[i,j] for i in V for j in V if j>i), GRB.MINIMIZE)

    model.update()
    # model.Params.OutputFlag = 0 # silent mode
    
    EPS = 1.e-6
    while True:
        model.optimize()
        edges = []
        for (i,j) in x:
            if x[i,j].X > EPS:
                if i != V[0] and j != V[0]:
                    edges.append((i,j))
        if addcut(edges) == False:
            break

    return model.ObjVal,edges


def distance(x1,y1,x2,y2):
    """distance: euclidean distance between (x1,y1) and (x2,y2)"""
    return math.sqrt((x2-x1)**2 + (y2-y1)**2)

def make_data(n):
    """make_data: compute matrix distance based on euclidean distance"""
    V = range(1,n+1)
    x = dict([(i,random.random()) for i in V])
    y = dict([(i,random.random()) for i in V])
    c,q = {},{}
    Q = 100
    for i in V:
        q[i] = random.randint(10,20)
        for j in V:
            if j > i:
                c[i,j] = distance(x[i],y[i],x[j],y[j])
    return V,c,q,Q
    
            
if __name__ == "__main__":
    import sys

    n = 19
    m = 3
    seed = 1
    random.seed(seed)
    V,c,q,Q = make_data(n)
    z,edges = solve_vrp(V,c,m,q,Q)
    print "Optimal solution:",z
    print "Edges in the solution:"
    print sorted(edges)