Minor refactoring of a few models

realistic/SAT/SAT.py

@@ -75,5 +75,5 @@ def check(clause1, clause2, a, b, links):
     x = VarArray(size=e, dom=lambda i: range(1, 2 ** len(clauses[i])))
 
     satisfy(
-        (x[i], x[j]) in T for i, j in combinations(e, 2) if (T := dual_table(i, j),) is not None
+        (x[i], x[j]) in T for i, j in combinations(e, 2) if (T := dual_table(i, j)) is not None
     )

realistic/SdnChain/SdnChain.py

@@ -47,12 +47,7 @@
 def dp(i, j):
     l1 = [sl[k] for k, (a, b) in G if b[DOMAIN] == i and a[DOMAIN] == j]
     l2 = [sl[k] & pth[a[DOMAIN]][j] for k, (a, b) in G if b[DOMAIN] == i and a[DOMAIN] != j]
-    if len(l1) == 0 and len(l2) == 0:
-        return 0
-    if len(l1) == 0:
-        return Count(l2) >= 1
-    if len(l2) == 0:
-        return Count(l1) >= 1
+    assert len(l1) > 0 and len(l2) > 0
     return (Count(l1) >= 1) | (Count(l2) >= 1)
 
 
@@ -193,7 +188,7 @@ def dp(i, j):
         If(
             sd[a[DOMAIN]] == 0,
             Then=sl[i] == 0
-        ) for i, (a, b) in L
+        ) for i, (a, _) in L
     ],
 )
 

realistic/SdnChain/d10n780-1.json

@@ -20,8 +20,17 @@
   "start_domain": 9,
   "target_domain": 4,
   "vnflist": [9, 2, 0, 4, 9],
-  "vnf_arcs": [[0, 1], [1, 2], [1, 3], [3, 4]],
+  "vnf_arcs": [
+    [0, 1],
+    [1, 2],
+    [1, 3],
+    [3, 4]
+  ],
   "proximity_to_source": [0, 0, 0, 0, 0],
   "proximity_to_destination": [0, 0, 0, 0, 0],
-  "domain_constraints": [[8, 0, 0, 0], [8, 2, 0, 0], [0, 2, 0, 0]]
+  "domain_constraints": [
+    [8, 0, 0, 0],
+    [8, 2, 0, 0],
+    [0, 2, 0, 0]
+  ]
 }

realistic/SeatMoving/SeatMoving.py

@@ -29,7 +29,7 @@
 nSeats = len(seats)
 horizon = (2 * nSeats) // (nSeats - nPersons + 1) + 1  # maximum number of steps
 
-# x[t][i] is the person at the ith seat at time t i (-1 means 'empty seat')
+# x[t][i] is the person at the ith seat at time t (-1 means 'empty seat')
 x = VarArray(size=[horizon, nSeats], dom=range(-1, nPersons))
 
 # y[t][j] is the seat for the jth person at time t

realistic/ShipScheduling/ShipScheduling.py

@@ -147,7 +147,7 @@
     Sum(s[i] * d[i] * tonnesPerCmDraft[i] for i in range(nShips))
 )
 
-"""
+""" Comments
 1) Note that:
  If(len(outShips) > 0,
     Then=[Sum(Sum(tb[i][t]) + tbe[i] * (t == x[i]) for i in outShips) <= nTugs for t in range(horizon)]

realistic/SkillAllocation/SkillAllocation.py

@@ -23,8 +23,9 @@
 
 nTrainings, nMaxJobs, engineerSkills, engineerLocations, jobs = data
 nEngineers, nSkills, nJobs, nOverseasCap = len(engineerSkills), len(engineerSkills[0]), len(jobs), 5
+E, T, J = range(nEngineers), range(nTrainings), range(nJobs)
 
-qualifiedEngineers = [[e for e in range(nEngineers) if engineerSkills[e][job[0]] == 1] for job in jobs]
+qualifiedEngineers = [[e for e in E if engineerSkills[e][job[0]] == 1] for job in jobs]
 
 # x[i] is the engineer assigned to the ith job
 x = VarArray(size=nJobs, dom=range(nEngineers))
@@ -34,13 +35,13 @@
 
 satisfy(
     # computing new skills
-    [y[e][t] in {-1}.union(s for s in range(nSkills) if engineerSkills[e][s] == 0) for e in range(nEngineers) for t in range(nTrainings)],
+    [y[e][t] in {-1}.union(s for s in range(nSkills) if engineerSkills[e][s] == 0) for e in E for t in T],
 
     # not exceeding the maximum number of jobs per engineer
-    [Sum(x[i] == e for i in range(nJobs)) <= nMaxJobs for e in range(nEngineers)],
+    [Sum(x[i] == e for i in J) <= nMaxJobs for e in E],
 
     # not exceeding the maximum number of oversea jobs per engineer
-    [Sum(x[i] == e for i in range(nJobs) if jobs[i][4] == 1) <= nOverseasCap for e in range(nEngineers)],
+    [Sum(x[i] == e for i in J if jobs[i][4] == 1) <= nOverseasCap for e in E],
 
     # ensuring that a qualified engineer is assigned to each job
     [
@@ -50,13 +51,13 @@
                 both(
                     y[e][t] == jobs[i][0],
                     x[i] == e
-                ) for e in range(nEngineers) for t in range(nTrainings)
+                ) for e in E for t in T
             )
-        ) for i in range(nJobs)
+        ) for i in J
     ]
 )
 
 minimize(
     # minimizing the number of new skills
-    Sum(y[e][t] >= 0 for e in range(nEngineers) for t in range(nTrainings))
+    Sum(y[e][t] >= 0 for e in E for t in T)
 )

realistic/Smelt/Smelt.py

@@ -53,13 +53,13 @@
 def production_rule(i):
     assert rt[i] in range(4)
     if rt[i] == 0:
-        return If(f[i], Then=re[rk1[i]] + rd[i] <= rs[rk2[i]])
+        return re[rk1[i]] + rd[i] <= rs[rk2[i]]
     if rt[i] == 1:
-        return If(f[i], Then=re[rk1[i]] + rd[i] >= rs[rk2[i]])
+        return re[rk1[i]] + rd[i] >= rs[rk2[i]]
     if rt[i] == 2:
-        return If(f[i], Then=rs[rk1[i]] - rd[i] <= rs[rk2[i]])
+        return rs[rk1[i]] - rd[i] <= rs[rk2[i]]
     if rt[i] == 3:
-        return If(f[i], Then=re[rk1[i]] - rd[i] <= re[rk2[i]])
+        return re[rk1[i]] - rd[i] <= re[rk2[i]]
 
 
 satisfy(
@@ -97,7 +97,12 @@ def production_rule(i):
     ],
 
     # managing production rules
-    [production_rule(i) for i in range(nRules)],
+    [
+        If(
+            f[i],
+            Then=production_rule(i)
+        ) for i in range(nRules)
+    ],
 
     # computing the make-span
     [re[i] <= m for i in range(nRecipes)]
@@ -107,7 +112,7 @@ def production_rule(i):
     Sum(f[i] == 0 for i in range(nRules)) * H + m
 )
 
-"""
+""" Comments
 1) It seems that it would be simpler to change 0 and 1 for f
 """
 

realistic/Soccer/Soccer.py

@@ -25,16 +25,17 @@
 
 from pycsp3 import *
 
+print(data)
 games, iPoints, positions = data
 nGames, nTeams, nPositions = len(games), len(iPoints), len(positions)
 
-pt = [0, 1, 3]
+P = [0, 1, 3]
 
-lb_score = min(iPoints[i] + sum(min(pt) for j in range(nGames) if i in games[j]) for i in range(nTeams))
-ub_score = max(iPoints[i] + sum(max(pt) for j in range(nGames) if i in games[j]) for i in range(nTeams))
+lb_score = min(iPoints[i] + sum(min(P) for j in range(nGames) if i in games[j]) for i in range(nTeams))
+ub_score = max(iPoints[i] + sum(max(P) for j in range(nGames) if i in games[j]) for i in range(nTeams))
 
 # points[j][0] and points[j][1] are the points for the two teams (indexes 0 and 1) of the jth game
-points = VarArray(size=[nGames, 2], dom=pt)
+points = VarArray(size=[nGames, 2], dom=P)
 
 # score[i] is the final score of the ith team
 score = VarArray(size=nTeams, dom=range(lb_score, ub_score + 1))
@@ -51,15 +52,14 @@
 satisfy(
     # assigning rights points for each game
     [
-        (points[j][0], points[j][1]) in {
-            (0, 3),
-            (1, 1),
-            (3, 0)
-        } for j in range(nGames)
+        Table(
+            scope=(points[j][0], points[j][1]),
+            supports={(0, 3), (1, 1), (3, 0)}
+        ) for j in range(nGames)
     ],
 
     # computing final points
-    [score[i] - Sum(points[j][0 if i == games[j][0] else 1] for j in range(nGames) if i in games[j]) == iPoints[i] for i in range(nTeams)],
+    [score[i] - Sum(points[j][0 if i == game[0] else 1] for j, game in enumerate(games) if i in game) == iPoints[i] for i in range(nTeams)],
 
     # computing worst positions (the number of teams with greater total points)
     [wp[i] == Sum(score[j] >= score[i] for j in range(nTeams)) for i in range(nTeams)],

realistic/StableGoods/StableGoods.py

@@ -52,9 +52,9 @@
 satisfy(
     # computing the distribution of goods
     [
-        Sum(
+        quantities[j] == Sum(
             (tg[i] == j) * ng[i] for i in range(nPersons)
-        ) + z[j] == quantities[j] for j in range(nGoods)
+        ) + z[j] for j in range(nGoods)
     ]
 )
 
@@ -105,6 +105,5 @@ def T(p1, p2):
 
 """ Comments
 1) z * values is a shortcut for Sum(z[g] * values[g] for g in range(nGoods)) 
-
 2) the table variant is more efficient
 """

realistic/SteinerTree/SteinerTree.py

@@ -78,7 +78,10 @@
                 prt[i] == NO,
                 dst[i] == 0
             ],
-            Else=If(i != root, Then=prt[i] != NO)
+            Else=If(
+                i != root,
+                Then=prt[i] != NO
+            )
         ) for i in range(n)
     ],
 

realistic/StochasticVRP/StochasticVRP.py

@@ -44,11 +44,8 @@
 satisfy(
     # pre-assigning a few variables
     [
-        [prv[k][i] == i + nVehicles - 1 for k in range(nSetups) for i in range(nCustomers + 1, nCustomers + nVehicles)],
-        [prv[k][nCustomers] == nCustomers + 2 * nVehicles - 1 for k in range(nSetups)],
-
-        [nxt[k][i] == i - nVehicles + 1 for k in range(nSetups) for i in range(nCustomers + nVehicles, nCustomers + 2 * nVehicles - 1)],
-        [nxt[k][-1] == nCustomers for k in range(nSetups)],
+        [prv[k][i] == v for k in range(nSetups) for i in StartDepots if (v := i + nVehicles - 1 + (nVehicles if i == nCustomers else 0),)],
+        [nxt[k][i] == v for k in range(nSetups) for i in EndDepots if (v := i - nVehicles + 1 - (nVehicles if i == nNodes - 1 else 0),)],
 
         [veh[i] == i - nCustomers for i in StartDepots],
         [veh[i] == i - nCustomers - nVehicles for i in EndDepots],
@@ -90,6 +87,6 @@
     Sum(weights[i] * Maximum(arr[i]) for i in range(nSetups))
 )
 
-"""
+""" Comments
 1) note that data have been transferred in JSON
 """