def formulate(cp):
prob = dippy.DipProblem(
"Coke",
display_mode='xdot',
# layout = 'bak',
display_interval=None,
)
# create variables
LOC_SIZES = [(l, s) for l in cp.LOCATIONS for s in cp.SIZES]
buildVars = LpVariable.dicts("Build", LOC_SIZES, cat=LpBinary)
# create arcs
flowVars = LpVariable.dicts("Arcs", cp.ARCS)
BIG_M = max(sum(cp.supply.values()), sum(cp.demand.values()))
for a in cp.ARCS:
flowVars[a].bounds(0, BIG_M)
# objective
prob += 1e6 * lpSum(buildVars[(l, s)] * cp.build_costs[s] \
for (l, s) in LOC_SIZES) + \
lpSum(flowVars[(s, d)] * cp.transport_costs[(s, d)] \
for (s, d) in cp.ARCS), "min"
# plant availability - assumes that SIZES are numeric,
# which they should be
for loc in cp.LOCATIONS:
prob += lpSum(flowVars[(loc, i)] for i in cp.CUSTOMERS) \
<= lpSum(buildVars[(loc, s)] * s for s in cp.SIZES)
# one size
for loc in cp.LOCATIONS:
prob += lpSum(buildVars[(loc, s)] for s in cp.SIZES) == 1
# conserve flow (mines)
# flows are in terms of tonnes of coke
for m in cp.MINES:
prob += lpSum(flowVars[(m, j)] for j in cp.LOCATIONS) \
<= cp.supply[m]
# conserve flow (locations)
# convert from coal to coke
for loc in cp.LOCATIONS:
prob += lpSum(flowVars[(m, loc)] for m in cp.MINES) - \
cp.conversion_factor * \
lpSum(flowVars[(loc, c)] for c in cp.CUSTOMERS) \
>= 0
for c in cp.CUSTOMERS:
prob += lpSum(flowVars[(loc, c)] for loc in cp.LOCATIONS) \
>= cp.demand[c]
prob.cp = cp
prob.buildVars = buildVars
prob.flowVars = flowVars
return prob
def formulate(bpp):
prob = dippy.DipProblem(
"Bin Packing",
display_mode='xdot',
# layout = 'bak',
display_interval=None,
)
assign_vars = LpVariable.dicts("x", [(i, j) for i in bpp.ITEMS
for j in bpp.BINS],
cat=LpBinary)
use_vars = LpVariable.dicts("y", bpp.BINS, cat=LpBinary)
waste_vars = LpVariable.dicts("w", [(j, k) for j in bpp.BINS
for k in bpp.LIMITS], 0, None)
prob += lpSum(use_vars[j] for j in bpp.BINS), "min_bins"
for j in bpp.BINS:
for k in bpp.LIMITS:
prob += lpSum(bpp.volume[i, k] * assign_vars[i, j] for i in bpp.ITEMS) \
+ waste_vars[j, k] == bpp.capacity[k] * use_vars[j]
for i in bpp.ITEMS:
prob += lpSum(assign_vars[i, j] for j in bpp.BINS) == 1
for i in bpp.ITEMS:
for j in bpp.BINS:
prob += assign_vars[i, j] <= use_vars[j]
for n in range(0, len(bpp.BINS) - 1):
prob += use_vars[bpp.BINS[n]] >= use_vars[bpp.BINS[n + 1]]
# Attach the problem data and variable dictionaries to the DipProblem
prob.bpp = bpp
prob.assign_vars = assign_vars
prob.use_vars = use_vars
prob.waste_vars = waste_vars
return prob
import sys
#sys.path.append("C:\\COIN\\GIMPy\\GrUMPy\\trunk")
#sys.path.append("C:\\COIN\\GIMPy\\trunk")
import coinor.pulp as pulp
from pulp import *
import dippy
try:
import path
except ImportError:
pass
prob = dippy.DipProblem("Dippy Example",
display_mode='pygame',
display_interval=1)
x1 = LpVariable("x_1", 0, None, LpInteger)
x2 = LpVariable("x_2", 0, cat=LpInteger)
prob += -x1 - x2, "min"
prob += -2 * x1 + 2 * x2 >= 1
prob += -8 * x1 + 10 * x2 <= 13
dippy.Solve(prob, {
'CutCGL': 0,
})
for var in prob.variables():