Part 5: Circuit Constraint, TSP and LNS

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Theoretical Questions

Circuit Constraint

The Circuit constraint enforces a Hamiltonian circuit on a successor array. In the following example, the successor array a=[2,4,1,5,3,0] has for each index i (0 ≤ i ≤ 5) a directed edge that originates from i and ends in a[i]:


All the successors must be different. However, enforcing the AllDifferent constraint is not enough as we must also guarantee that a proper Hamiltonian circuit (without sub-circuits) is formed. This can be done efficiently and incrementally by keeping track of any paths (non-closed circuits) that appear during search. Note that each node is on at most one path. For your implementation, use the following arrays of stateful integers as the data structure to keep track of the paths:

IntVar [] x;
StateInt [] dest;
StateInt [] orig;
StateInt [] lengthToDest;


  • dest[i] is the last (non-fixed) node that can be reached from node i if node i is fixed and on a path; otherwise it is i;
  • orig[i] is the first (fixed) node that can reach node i if node i is on a path; otherwise it is i;
  • lengthToDest[i] is the length of the path from node i to node dest[i] if node i is on a path; otherwise it is 0.

Consider the following example where edges originating from fixed nodes are colored grey:


Before node 5 has been fixed, the green edge has not yet been added, so we have:

dest = [2,1,2,5,5,5];
orig = [0,1,0,4,4,4];
lengthToDest = [1,0,0,1,2,0];

After node 5 has been fixed, the green edge has been added, so we have:

dest = [2,1,2,2,2,2];
orig = [4,1,4,4,4,4];
lengthToDest = [1,0,0,3,4,2];

In your implementation, you must update the stateful integers in order to reflect the changes after the addition of new edges. An edge is added whenever a node becomes fixed: you can use the CPIntVar.whenBind(…) method to run some code block when this event occurs.

The filtering algorithm is to prevent closing each path that would have a length less than n (the total number of nodes) as that would result in a non-Hamiltonian circuit. Since node 4 (the origin of a path) has a length to its destination (node 2) of 4 (<6), the destination node 2 cannot have the origin node 4 as a successor and the red edge is deleted. This filtering was introduced in [TSP1998] for solving the TSP with CP.

Implement a propagator Check that your propagator passes the tests

[TSP1998]Pesant, G., Gendreau, M., Potvin, J. Y., & Rousseau, J. M. (1998). An exact constraint logic programming algorithm for the traveling salesman problem with time windows. Transportation Science, 32(1), 12-29.

Custom Search for TSP

Modify in order to implement a custom search strategy. Use the following code as skeleton code:

DFSearch dfs = makeDfs(cp, () -> {
    IntVar xs = selectMin(succ,
            xi -> xi.size() > 1, // filter
            xi -> xi.size()); // variable selector
    if (xs == null)
        return EMPTY;

    int v = xs.min(); // value selector (TODO)
    return branch(() ->, v)),
            () ->, v)));
  • The unbound variable selected is one with a smallest domain (first-fail).
  • The selected variable is then assigned the minimum value in its domain.

This value selection strategy is not well-suited for the TSP (and VRP in general). The one you design should be more similar to the decision you would make manually in a greedy fashion. For instance, you can select as a successor for xi a closest city in its domain.

Hint: Since there is no iterator on the domain of a variable, you can iterate from its minimum value to its maximum one by using a for loop and checking that the value of the current iteration is in the domain using the contains method. You can also use your iterator from Part 2: Domains, Variables, Constraints.

You can also implement a min-regret variable selection strategy: it selects a variable with the largest difference between a closest successor city and a second-closest one. The idea is that it is critical to decide the successor for this city first, because otherwise one will regret it the most.

Observe the first solution obtained to the provided instance and its objective value: is it better than upon naive first-fail? Also observe the time and number of backtracks necessary for proving optimality: by how much did you reduce the computation time and number of backtracks?

LNS applied to TSP

Implement and apply LNS by modifying Use the provided 17x17 distance matrix for this exercise.

What you should do:

  • Record the assignment of the current best solution. Hint: Use the onSolution call-back on the DFSearch object.
  • Implement a restart strategy fixing randomly 10% of the variables to their value in the current best solution.
  • Each restart has a failure limit of 100 backtracks.

An example of LNS is given in You can simply copy/paste/modify this implementation for the TSP:

  • Does it converge faster to good solutions than the standard DFSearch? Use the larger instance with 26 facilities.

  • What is the impact of the percentage of variables relaxed (experiment with 5%, 10%, and 20%)?

  • What is the impact of the failure limit (experiment with 50, 100, and 1000)?

  • Which parameter setting works best? How did you choose it?

  • Imagine a different relaxation specific for this problem. Try to relax the decision variables that have the strongest impact on the objective with a greater probability (the choice of relaxed variables should still be somehow randomized). You can for instance compute for each facility i the quantity:

    s_i = \sum\limits_{j}{d[x[i]][x[j]] \cdot w[i][j]}

    and base your decision to relax facilities based on those values.

From TSP to VRP

Create a new file called working with the same distance matrix as the TSP but assuming that there are now k vehicles (make it a parameter and experiment with k=3). The depot is the city at index 0, and every other city must be visited exactly once by exactly one of the k vehicles:

  • Variant 1: Minimize the total distance traveled by the three vehicles.
  • Variant 2 (more advanced): Minimize the longest distance traveled by the three vehicles (in order to be fair among the vehicle drivers).

You can also use LNS to speed up the search.