before LLM fucks things up
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Pagwin
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.python-version
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.python-version
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3.13
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README.md
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README.md
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548
main.py
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main.py
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"""
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City Resource Optimization -> CP-SAT (Google OR-Tools)
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Maximise Electrum * Brass * Steel after the gains of step 5.
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The objective is a product of three variables, so this is a constraint-
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programming / nonlinear problem, hence CP-SAT (cp_model) rather than the
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LP/MIP solver. Read the comments at:
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- PARAMETERS (initial pools + arrival schedule + bonus mode)
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- "OBJECTIVE IS SET HERE" (the product being maximised -- tweak freely)
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"""
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from ortools.sat.python import cp_model
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# ======================================================================
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# PARAMETERS -- edit these
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# ======================================================================
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# Starting resource pools at the start of step 1: (Electrum, Brass, Steel, Capital)
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INITIAL = (3, 3, 3, 3)
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# Arrival schedule. Key = step (1..5), value = list of city types that arrive
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# at the START of that step. Types: 'H' Hub, 'F' Foundry, 'M' Metropolis, 'N' Monument.
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# Total cities across all steps must be <= 7. Arriving cities act that same step.
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ARRIVALS = {
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1: ["H", "F", "H", "N", "F"],
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2: ["M"],
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3: [],
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4: [],
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5: [],
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}
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# Collect Bonus (b) adds +1 to whatever a Collect gives. On a foundry that is
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# +1 of the resource collected (i.e. the chosen vat's value + 1). This is the
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# same uniform rule as Hub (+1 Capital) and Metropolis (+1 resource pick).
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NUM_STEPS = 5
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MAX_RES = (
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200 # upper bound on any resource pool (raise if you expect more; affects speed)
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)
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MAX_VAT = 15 # upper bound on a foundry vat value (1 + 2*nsteps is plenty)
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# ======================================================================
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# MODEL
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# ======================================================================
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def solve(
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initial=INITIAL,
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arrivals=ARRIVALS,
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max_res=MAX_RES,
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max_vat=MAX_VAT,
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time_limit=60.0,
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num_workers=8,
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verbose=True,
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):
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# ---- build the city list -----------------------------------------
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cities = [] # list of (arrival_step, arrival_type)
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for s in range(1, NUM_STEPS + 1):
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for typ in arrivals.get(s, []):
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cities.append((s, typ))
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N = len(cities)
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assert N <= 7, f"At most 7 cities allowed, got {N}"
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m = cp_model.CpModel()
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def AND(a, b):
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"""Boolean AND of two 0/1 vars, returned as a new 0/1 var."""
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c = m.NewBoolVar("")
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m.AddMultiplicationEquality(c, [a, b])
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return c
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# ---- state variables ---------------------------------------------
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# Indexed [i][t]; t in 1..NUM_STEPS+1 for state (t=NUM_STEPS+1 == "after step 5").
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isH, isF, isM, isMon, present = {}, {}, {}, {}, {}
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hasA, hasB, hasD = {}, {}, {}
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vE, vB, vS = {}, {}, {} # foundry vats at start of step t
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# action variables, t in 1..NUM_STEPS
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col, ua, ub, ud = {}, {}, {}, {} # collect / upgrade a,b,d
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rH, rF, rM = {}, {}, {} # renovate -> Hub/Foundry/Metro
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cvE, cvB, cvS = {}, {}, {} # foundry: which vat collected
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mE, mB, mS, mC = {}, {}, {}, {} # metropolis: resources picked (+1 each)
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for i in range(N):
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a_step, a_type = cities[i]
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for t in range(1, NUM_STEPS + 2):
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isH[i, t] = m.NewBoolVar(f"isH_{i}_{t}")
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isF[i, t] = m.NewBoolVar(f"isF_{i}_{t}")
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isM[i, t] = m.NewBoolVar(f"isM_{i}_{t}")
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isMon[i, t] = m.NewBoolVar(f"isMon_{i}_{t}")
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present[i, t] = m.NewBoolVar(f"present_{i}_{t}")
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hasA[i, t] = m.NewBoolVar(f"hasA_{i}_{t}")
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hasB[i, t] = m.NewBoolVar(f"hasB_{i}_{t}")
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hasD[i, t] = m.NewBoolVar(f"hasD_{i}_{t}")
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vE[i, t] = m.NewIntVar(0, max_vat, f"vE_{i}_{t}")
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vB[i, t] = m.NewIntVar(0, max_vat, f"vB_{i}_{t}")
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vS[i, t] = m.NewIntVar(0, max_vat, f"vS_{i}_{t}")
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# presence: present from arrival step onward, persists
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m.Add(present[i, t] == (1 if t >= a_step else 0))
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# exactly one type iff present
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m.Add(isH[i, t] + isF[i, t] + isM[i, t] + isMon[i, t] == present[i, t])
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for t in range(1, NUM_STEPS + 1):
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col[i, t] = m.NewBoolVar(f"col_{i}_{t}")
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ua[i, t] = m.NewBoolVar(f"ua_{i}_{t}")
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ub[i, t] = m.NewBoolVar(f"ub_{i}_{t}")
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ud[i, t] = m.NewBoolVar(f"ud_{i}_{t}")
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rH[i, t] = m.NewBoolVar(f"rH_{i}_{t}")
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rF[i, t] = m.NewBoolVar(f"rF_{i}_{t}")
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rM[i, t] = m.NewBoolVar(f"rM_{i}_{t}")
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cvE[i, t] = m.NewBoolVar(f"cvE_{i}_{t}")
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cvB[i, t] = m.NewBoolVar(f"cvB_{i}_{t}")
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cvS[i, t] = m.NewBoolVar(f"cvS_{i}_{t}")
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mE[i, t] = m.NewIntVar(0, 3, f"mE_{i}_{t}")
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mB[i, t] = m.NewIntVar(0, 3, f"mB_{i}_{t}")
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mS[i, t] = m.NewIntVar(0, 3, f"mS_{i}_{t}")
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mC[i, t] = m.NewIntVar(0, 3, f"mC_{i}_{t}")
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# ---- per-step gain/cost accumulators (linear expressions) ---------
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gain_E = {t: [] for t in range(1, NUM_STEPS + 1)}
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gain_B = {t: [] for t in range(1, NUM_STEPS + 1)}
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gain_S = {t: [] for t in range(1, NUM_STEPS + 1)}
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gain_C = {t: [] for t in range(1, NUM_STEPS + 1)}
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cost_C = {t: [] for t in range(1, NUM_STEPS + 1)} # capital cost (collects)
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cost_S = {t: [] for t in range(1, NUM_STEPS + 1)} # steel cost (upgrades b,d)
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# ---- per-city logic -----------------------------------------------
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for i in range(N):
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a_step, a_type = cities[i]
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# initial type at arrival step
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init = {"H": isH, "F": isF, "M": isM, "N": isMon}
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m.Add(init[a_type][i, a_step] == 1)
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# no upgrades at arrival
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m.Add(hasA[i, a_step] == 0)
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m.Add(hasB[i, a_step] == 0)
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m.Add(hasD[i, a_step] == 0)
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# vats at arrival
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m.Add(vE[i, a_step] == 1)
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m.Add(vB[i, a_step] == 1)
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m.Add(vS[i, a_step] == 1)
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# before arrival: everything zero
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for t in range(1, a_step):
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for v in (isH, isF, isM, isMon, hasA, hasB, hasD, vE, vB, vS):
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m.Add(v[i, t] == 0)
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if t <= NUM_STEPS:
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for v in (col, ua, ub, ud, rH, rF, rM, cvE, cvB, cvS, mE, mB, mS, mC):
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m.Add(v[i, t] == 0)
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# action + transition logic for active steps
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for t in range(a_step, NUM_STEPS + 1):
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P = present[i, t]
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ren = m.NewBoolVar("") # any renovation this step
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m.Add(ren == rH[i, t] + rF[i, t] + rM[i, t])
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no_ren = m.NewBoolVar("")
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m.Add(no_ren == 1 - ren)
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# exactly one action while present
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m.Add(
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col[i, t]
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+ ua[i, t]
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+ ub[i, t]
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+ ud[i, t]
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+ rH[i, t]
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+ rF[i, t]
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+ rM[i, t]
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== P
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)
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# renovation must change type (renovate-to-X requires not-X now)
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m.Add(isH[i, t] == 0).OnlyEnforceIf(rH[i, t])
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m.Add(isF[i, t] == 0).OnlyEnforceIf(rF[i, t])
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m.Add(isM[i, t] == 0).OnlyEnforceIf(rM[i, t])
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# upgrade legality
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m.Add(hasA[i, t] == 0).OnlyEnforceIf(ua[i, t]) # can't re-acquire
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m.Add(hasB[i, t] == 0).OnlyEnforceIf(ub[i, t])
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m.Add(hasD[i, t] == 0).OnlyEnforceIf(ud[i, t])
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m.Add(isF[i, t] == 1).OnlyEnforceIf(ud[i, t]) # d is foundry-only
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# LLM allowed renovation into metropolis, need to prevent that now
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m.Add(rM[i, t] == 0)
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# Monument constraints due to maximizing resources and not wanting enemy to get free upgrades
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m.Add(col[i, t] == 0).OnlyEnforceIf(isMon[i, t])
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m.Add(ua[i, t] == 0).OnlyEnforceIf(isMon[i, t])
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m.Add(ub[i, t] == 0).OnlyEnforceIf(isMon[i, t])
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m.Add(ud[i, t] == 0).OnlyEnforceIf(isMon[i, t])
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# ---- type transition t -> t+1 ----
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m.Add(isH[i, t + 1] == isH[i, t]).OnlyEnforceIf(no_ren)
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m.Add(isF[i, t + 1] == isF[i, t]).OnlyEnforceIf(no_ren)
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m.Add(isM[i, t + 1] == isM[i, t]).OnlyEnforceIf(no_ren)
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for r, target in ((rH[i, t], isH), (rF[i, t], isF), (rM[i, t], isM)):
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m.Add(target[i, t + 1] == 1).OnlyEnforceIf(r)
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m.Add(isF[i, t + 1] == 0).OnlyEnforceIf(rH[i, t])
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m.Add(isM[i, t + 1] == 0).OnlyEnforceIf(rH[i, t])
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m.Add(isH[i, t + 1] == 0).OnlyEnforceIf(rF[i, t])
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m.Add(isM[i, t + 1] == 0).OnlyEnforceIf(rF[i, t])
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m.Add(isH[i, t + 1] == 0).OnlyEnforceIf(rM[i, t])
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m.Add(isF[i, t + 1] == 0).OnlyEnforceIf(rM[i, t])
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# ---- upgrade transition t -> t+1 ----
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# a, b survive renovation and are monotone
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m.AddMaxEquality(hasA[i, t + 1], [hasA[i, t], ua[i, t]])
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m.AddMaxEquality(hasB[i, t + 1], [hasB[i, t], ub[i, t]])
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# d is stripped on renovation, else monotone
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m.Add(hasD[i, t + 1] == 0).OnlyEnforceIf(ren)
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d_keep = m.NewBoolVar("")
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m.AddMaxEquality(d_keep, [hasD[i, t], ud[i, t]])
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m.Add(hasD[i, t + 1] == d_keep).OnlyEnforceIf(no_ren)
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# ---- collect sub-choices ----
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fcol = AND(isF[i, t], col[i, t])
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mcol = AND(isM[i, t], col[i, t])
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hcol = AND(isH[i, t], col[i, t])
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# foundry: exactly one vat chosen iff foundry collects
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m.Add(cvE[i, t] + cvB[i, t] + cvS[i, t] == fcol)
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# metropolis: pick (2 + hasB) resources (+1 each); else nothing
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npick = m.NewIntVar(0, 3, "")
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m.Add(npick == 2 + hasB[i, t]).OnlyEnforceIf(mcol)
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m.Add(npick == 0).OnlyEnforceIf(mcol.Not())
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m.Add(mE[i, t] + mB[i, t] + mS[i, t] + mC[i, t] == npick)
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# ================= COSTS =================
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# foundry & metropolis collect each cost 1 Capital
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cost_C[t].append(fcol)
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cost_C[t].append(mcol)
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# upgrades b,d cost 2 Steel, reduced by 1 if cost-reduction (a) already held
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for upg in (ub[i, t], ud[i, t]):
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c = m.NewIntVar(0, 2, "")
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both = AND(upg, hasA[i, t])
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m.Add(c == 2).OnlyEnforceIf(upg, hasA[i, t].Not())
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m.Add(c == 1).OnlyEnforceIf(both)
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m.Add(c == 0).OnlyEnforceIf(upg.Not())
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cost_S[t].append(c)
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# ================= GAINS =================
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# Hub collect: +2 Capital (+1 more if collect-bonus b)
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hub_gain = m.NewIntVar(0, 3, "")
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m.Add(hub_gain == 2 + hasB[i, t]).OnlyEnforceIf(hcol)
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m.Add(hub_gain == 0).OnlyEnforceIf(hcol.Not())
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gain_C[t].append(hub_gain)
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# Metropolis collect: +1 per pick
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gain_E[t].append(mE[i, t])
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gain_B[t].append(mB[i, t])
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gain_S[t].append(mS[i, t])
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gain_C[t].append(mC[i, t])
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# Foundry collect: gain chosen vat's value as that resource
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gEf = m.NewIntVar(0, max_vat, "")
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gBf = m.NewIntVar(0, max_vat, "")
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gSf = m.NewIntVar(0, max_vat, "")
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m.AddMultiplicationEquality(gEf, [cvE[i, t], vE[i, t]])
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m.AddMultiplicationEquality(gBf, [cvB[i, t], vB[i, t]])
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m.AddMultiplicationEquality(gSf, [cvS[i, t], vS[i, t]])
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gain_E[t].append(gEf)
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gain_B[t].append(gBf)
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gain_S[t].append(gSf)
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# Collect Bonus (b): adds +1 to the amount a Collect gives. For a
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# foundry that means +1 of the collected resource (vat value + 1),
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# the same uniform "+1 to what Collect gives" rule as Hub/Metro.
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gain_E[t].append(AND(cvE[i, t], hasB[i, t]))
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gain_B[t].append(AND(cvB[i, t], hasB[i, t]))
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gain_S[t].append(AND(cvS[i, t], hasB[i, t]))
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# ---- vat update producing vat[i, t+1] ----
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# increment added to the two non-collected vats (1, or 2 with upgrade d)
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inc = m.NewIntVar(1, 2, "")
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m.Add(inc == 1 + hasD[i, t])
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# vat_next = result of this step's action (only meaningful if foundry)
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vEn = m.NewIntVar(0, max_vat, "")
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vBn = m.NewIntVar(0, max_vat, "")
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vSn = m.NewIntVar(0, max_vat, "")
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# collect E: E->0, B,S += inc
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m.Add(vEn == 0).OnlyEnforceIf(cvE[i, t])
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m.Add(vBn == vB[i, t] + inc).OnlyEnforceIf(cvE[i, t])
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m.Add(vSn == vS[i, t] + inc).OnlyEnforceIf(cvE[i, t])
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# collect B
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m.Add(vBn == 0).OnlyEnforceIf(cvB[i, t])
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m.Add(vEn == vE[i, t] + inc).OnlyEnforceIf(cvB[i, t])
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m.Add(vSn == vS[i, t] + inc).OnlyEnforceIf(cvB[i, t])
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# collect S
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m.Add(vSn == 0).OnlyEnforceIf(cvS[i, t])
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m.Add(vEn == vE[i, t] + inc).OnlyEnforceIf(cvS[i, t])
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m.Add(vBn == vB[i, t] + inc).OnlyEnforceIf(cvS[i, t])
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# foundry but not collecting (upgrade / renovate-away): vats unchanged
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f_noncollect = AND(isF[i, t], col[i, t].Not())
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m.Add(vEn == vE[i, t]).OnlyEnforceIf(f_noncollect)
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m.Add(vBn == vB[i, t]).OnlyEnforceIf(f_noncollect)
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m.Add(vSn == vS[i, t]).OnlyEnforceIf(f_noncollect)
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# assign vat[i, t+1]:
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# renovate-to-foundry -> reset to 1
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# continuing foundry -> vat_next
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# otherwise (not foundry next) -> 0
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cont_F = AND(isF[i, t], no_ren) # stays a foundry next step
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for vnext, vn in (
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(vE[i, t + 1], vEn),
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(vB[i, t + 1], vBn),
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(vS[i, t + 1], vSn),
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):
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m.Add(vnext == 1).OnlyEnforceIf(rF[i, t])
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m.Add(vnext == vn).OnlyEnforceIf(cont_F)
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m.Add(isF[i, t + 1] == 0).OnlyEnforceIf(
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rF[i, t].Not(), cont_F.Not()
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) # tautology guard
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not_F_next = isF[i, t + 1].Not()
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m.Add(vE[i, t + 1] == 0).OnlyEnforceIf(not_F_next)
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m.Add(vB[i, t + 1] == 0).OnlyEnforceIf(not_F_next)
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m.Add(vS[i, t + 1] == 0).OnlyEnforceIf(not_F_next)
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# ---- resource pool recursion --------------------------------------
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E = {1: m.NewIntVar(initial[0], initial[0], "E1")}
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B = {1: m.NewIntVar(initial[1], initial[1], "B1")}
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S = {1: m.NewIntVar(initial[2], initial[2], "S1")}
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C = {1: m.NewIntVar(initial[3], initial[3], "C1")}
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for t in range(1, NUM_STEPS + 1):
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E[t + 1] = m.NewIntVar(0, max_res, f"E_{t + 1}")
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B[t + 1] = m.NewIntVar(0, max_res, f"B_{t + 1}")
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S[t + 1] = m.NewIntVar(0, max_res, f"S_{t + 1}")
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C[t + 1] = m.NewIntVar(0, max_res, f"C_{t + 1}")
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# costs are paid from the START-of-step pool (must work out before gains)
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m.Add(C[t] - sum(cost_C[t]) >= 0)
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m.Add(S[t] - sum(cost_S[t]) >= 0)
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# next pool = start - costs + gains
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m.Add(E[t + 1] == E[t] + sum(gain_E[t]))
|
||||
m.Add(B[t + 1] == B[t] + sum(gain_B[t]))
|
||||
m.Add(S[t + 1] == S[t] - sum(cost_S[t]) + sum(gain_S[t]))
|
||||
m.Add(C[t + 1] == C[t] - sum(cost_C[t]) + sum(gain_C[t]))
|
||||
|
||||
finalE, finalB, finalS = E[NUM_STEPS + 1], B[NUM_STEPS + 1], S[NUM_STEPS + 1]
|
||||
|
||||
# ---- Phase 1: real per-resource ceilings (fast LINEAR maximisations) ----
|
||||
# The triple product E*B*S has a very loose relaxation, so proving optimality
|
||||
# directly is slow. We first find the true max each resource can reach on its
|
||||
# own (a linear objective, solved to optimality quickly), then clamp the final
|
||||
# pools to those ceilings. This tightens the product's propagation enough to
|
||||
# prove optimality, and is valid because no feasible solution can exceed an
|
||||
# individual resource's standalone maximum.
|
||||
def _ceiling(var):
|
||||
s = cp_model.CpSolver()
|
||||
s.parameters.max_time_in_seconds = 20.0
|
||||
s.parameters.num_search_workers = num_workers
|
||||
m.Maximize(var)
|
||||
st = s.Solve(m)
|
||||
return (
|
||||
int(s.ObjectiveValue())
|
||||
if st in (cp_model.OPTIMAL, cp_model.FEASIBLE)
|
||||
else max_res
|
||||
)
|
||||
|
||||
capE = _ceiling(finalE)
|
||||
capB = _ceiling(finalB)
|
||||
capS = _ceiling(finalS)
|
||||
m.Add(finalE <= capE)
|
||||
m.Add(finalB <= capB)
|
||||
m.Add(finalS <= capS)
|
||||
|
||||
# ======================================================================
|
||||
# OBJECTIVE IS SET HERE -- maximise Electrum * Brass * Steel (post step 5)
|
||||
# To change the objective, edit the three "final" pools and/or the product
|
||||
# below. (finalE/finalB/finalS are the pools after step 5's gains.)
|
||||
# ======================================================================
|
||||
## NOTE: product can be changed here
|
||||
prodEB = m.NewIntVar(0, capE * capB, "prodEB")
|
||||
m.AddMultiplicationEquality(prodEB, [finalE, finalB])
|
||||
obj = m.NewIntVar(0, capE * capB * capS, "obj")
|
||||
m.AddMultiplicationEquality(obj, [prodEB, finalS])
|
||||
m.Maximize(obj)
|
||||
|
||||
# ---- Phase 2: solve the product to optimality ----
|
||||
solver = cp_model.CpSolver()
|
||||
solver.parameters.max_time_in_seconds = time_limit
|
||||
solver.parameters.num_search_workers = num_workers
|
||||
status = solver.Solve(m)
|
||||
|
||||
if verbose:
|
||||
print(f"(resource ceilings used: E<={capE} B<={capB} S<={capS})")
|
||||
_report(
|
||||
solver,
|
||||
status,
|
||||
cities,
|
||||
N,
|
||||
isH,
|
||||
isF,
|
||||
isM,
|
||||
isMon,
|
||||
col,
|
||||
ua,
|
||||
ub,
|
||||
ud,
|
||||
rH,
|
||||
rF,
|
||||
rM,
|
||||
cvE,
|
||||
cvB,
|
||||
cvS,
|
||||
mE,
|
||||
mB,
|
||||
mS,
|
||||
mC,
|
||||
hasA,
|
||||
hasB,
|
||||
hasD,
|
||||
vE,
|
||||
vB,
|
||||
vS,
|
||||
E,
|
||||
B,
|
||||
S,
|
||||
C,
|
||||
finalE,
|
||||
finalB,
|
||||
finalS,
|
||||
)
|
||||
return solver, status
|
||||
|
||||
|
||||
def _report(
|
||||
solver,
|
||||
status,
|
||||
cities,
|
||||
N,
|
||||
isH,
|
||||
isF,
|
||||
isM,
|
||||
isMon,
|
||||
col,
|
||||
ua,
|
||||
ub,
|
||||
ud,
|
||||
rH,
|
||||
rF,
|
||||
rM,
|
||||
cvE,
|
||||
cvB,
|
||||
cvS,
|
||||
mE,
|
||||
mB,
|
||||
mS,
|
||||
mC,
|
||||
hasA,
|
||||
hasB,
|
||||
hasD,
|
||||
vE,
|
||||
vB,
|
||||
vS,
|
||||
E,
|
||||
B,
|
||||
S,
|
||||
C,
|
||||
finalE,
|
||||
finalB,
|
||||
finalS,
|
||||
):
|
||||
print("status:", solver.StatusName(status))
|
||||
if status not in (cp_model.OPTIMAL, cp_model.FEASIBLE):
|
||||
return
|
||||
typ_name = {}
|
||||
for i in range(N):
|
||||
for t in range(1, NUM_STEPS + 1):
|
||||
if solver.Value(isH[i, t]):
|
||||
typ_name[i, t] = "Hub"
|
||||
elif solver.Value(isF[i, t]):
|
||||
typ_name[i, t] = "Foundry"
|
||||
elif solver.Value(isM[i, t]):
|
||||
typ_name[i, t] = "Metro"
|
||||
elif solver.Value(isMon[i, t]):
|
||||
typ_name[i, t] = "Monument"
|
||||
else:
|
||||
typ_name[i, t] = "-"
|
||||
|
||||
def action_str(i, t):
|
||||
if solver.Value(col[i, t]):
|
||||
if typ_name[i, t] == "Foundry":
|
||||
v = (
|
||||
"E"
|
||||
if solver.Value(cvE[i, t])
|
||||
else "B"
|
||||
if solver.Value(cvB[i, t])
|
||||
else "S"
|
||||
)
|
||||
return f"Collect vat {v}"
|
||||
if typ_name[i, t] == "Metro":
|
||||
picks = []
|
||||
for nm, var in (("E", mE), ("B", mB), ("S", mS), ("C", mC)):
|
||||
picks += [nm] * solver.Value(var[i, t])
|
||||
return "Collect {" + ",".join(picks) + "}"
|
||||
return "Collect (+Capital)"
|
||||
if solver.Value(ua[i, t]):
|
||||
return "Upgrade a (cost-reduction)"
|
||||
if solver.Value(ub[i, t]):
|
||||
return "Upgrade b (collect-bonus)"
|
||||
if solver.Value(ud[i, t]):
|
||||
return "Upgrade d (vat-increment)"
|
||||
if solver.Value(rH[i, t]):
|
||||
return "Renovate -> Hub"
|
||||
if solver.Value(rF[i, t]):
|
||||
return "Renovate -> Foundry"
|
||||
if solver.Value(rM[i, t]):
|
||||
return "Renovate -> Metro"
|
||||
return "(inactive)"
|
||||
|
||||
print("\nPer-step pools (start of step):")
|
||||
print(" step: E B S C")
|
||||
for t in range(1, NUM_STEPS + 2):
|
||||
label = f"after5" if t == NUM_STEPS + 1 else f"start {t}"
|
||||
print(
|
||||
f" {label:>8}: {solver.Value(E[t]):3} {solver.Value(B[t]):3} "
|
||||
f"{solver.Value(S[t]):3} {solver.Value(C[t]):3}"
|
||||
)
|
||||
|
||||
print("\nActions:")
|
||||
for i in range(N):
|
||||
a_step, a_type = cities[i]
|
||||
print(f" City {i} (arrives step {a_step} as {a_type}):")
|
||||
for t in range(a_step, NUM_STEPS + 1):
|
||||
ups = "".join(
|
||||
n
|
||||
for n, h in (("a", hasA), ("b", hasB), ("d", hasD))
|
||||
if solver.Value(h[i, t])
|
||||
)
|
||||
extra = (
|
||||
f" vats(E{solver.Value(vE[i, t])},B{solver.Value(vB[i, t])},S{solver.Value(vS[i, t])})"
|
||||
if typ_name[i, t] == "Foundry"
|
||||
else ""
|
||||
)
|
||||
print(
|
||||
f" step {t}: [{typ_name[i, t]:7}] {action_str(i, t):28}"
|
||||
f" upg[{ups}]{extra}"
|
||||
)
|
||||
|
||||
fe, fb, fs = solver.Value(finalE), solver.Value(finalB), solver.Value(finalS)
|
||||
print(f"\nFINAL E={fe} B={fb} S={fs} product = {fe * fb * fs}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
solve()
|
||||
9
pyproject.toml
Normal file
9
pyproject.toml
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
[project]
|
||||
name = "solve"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"ortools>=9.15.6755",
|
||||
]
|
||||
217
uv.lock
Normal file
217
uv.lock
Normal file
|
|
@ -0,0 +1,217 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version < '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version < '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "absl-py"
|
||||
version = "2.4.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "immutabledict"
|
||||
version = "4.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1d/e6/718471048fea0366c3e3d1df3acfd914ca66d571cdffcf6d37bbcd725708/immutabledict-4.3.1.tar.gz", hash = "sha256:f844a669106cfdc73f47b1a9da003782fb17dc955a54c80972e0d93d1c63c514", size = 7806, upload-time = "2026-02-15T10:32:34.668Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a3/ce/f9018bf69ae91b273b6391a095e7c93fa5e1617f25b6ba81ad4b20c9df10/immutabledict-4.3.1-py3-none-any.whl", hash = "sha256:c9facdc0ff30fdb8e35bd16532026cac472a549e182c94fa201b51b25e4bf7bf", size = 5000, upload-time = "2026-02-15T10:32:33.672Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" }
|
||||
wheels = [
|
||||
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||||
{ url = "https://files.pythonhosted.org/packages/1b/30/a80189bcc7f5e4258b3fbc3968d909d1756f54d023299ecc39ad6fdb9ef8/numpy-2.4.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb", size = 14693902, upload-time = "2026-05-18T23:34:33.013Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/97/12/70b5d0d7c15e1ebb8a6a84a8caa1d19e181d84fb58bb6d70aca29099dec1/numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f", size = 5198992, upload-time = "2026-05-18T23:34:36.132Z" },
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||||
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||||
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||||
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||||
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||||
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/b5/cd/9cc4dc876fb065d5c220aae4d5e14826b2715331bb7618ce1fb07a679d99/numpy-2.4.6-cp313-cp313-win_amd64.whl", hash = "sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359", size = 12318598, upload-time = "2026-05-18T23:34:58.928Z" },
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Loading…
Reference in a new issue