- Inverting the sequence of the orders between positions p1 and p2**. While this may be beneficial**
with respect to the distances, it may pose a problem for the time windows as usually orders are served in the sequence of their time windows.

- Exchanging the positions p1 and p2 **of two orders.** - Moving an order from position p1 **and reinserting it at position** p2, p1 < p2 **(forward shift).** - Moving an order from position p1 **and reinserting it at position** p2, p1 > p2 **(backward shift).**
In each step of the local search procedure, a neighborhood is **randomly picked from the set of** neighborhoods and a move is computed and accepted given an improvement.

Bids for orders on the marketplace are generated by the vehicle agents, taking into consideration all possible insertion points in the current route. The sum of the weighted increase in distance DIST
and tardiness TARDY gives the prize for the order.

The decider assigns orders to vehicles such that the maximum regret when not **assigning the order**
to a particular vehicle, and therefore having to assign it to **some other vehicle, is minimized. It also**
analyzes the progress of the improvement procedures. Given **no improvement for a certain number**
of iterations, the decider forces the vehicle agents to place back orders on the market such that they may be reallocated.

The optimization framework has been tested on a benchmark instance taken from [1]. The instance comprises 48 customers that have to be served from 4 depots, each of which possesses two vehicles.

We simulated a decision maker changing the relative importance wDIST **during the optimization**
procedure. First, a decision maker starting with a wDIST **= 1 and successively decreasing it to 0,**
second a decision maker starting with a wDIST **= 0 and increasing it to 1, and third a decision maker** starting with a wDIST = 0.**5, increasing it to 1 and decreasing it again to 0. Between adjusting**
the values of wDIST **in steps of 0.1, enough time for computations has been given to the system to**
allow a convergence to (at least) a local optimum. Figure 2 plots the results obtained during the test runs.

The first decision maker starts with DIST **= 975,** T ARDY **= 6246 and moves to** DIST **= 1412,**
T ARDY **= 0 while the second starts with** DIST **= 2953,** T ARDY **= 0 and moves to** DIST **= 1326,** T ARDY **= 3654. Clearly, the first strategy outperforms the second. While an initial value of**
wDIST **= 0 allows the identification of a solution with zero tardiness, it tends to construct routes**
that, when decreasing the relative importance of the tardiness, turn out to be hard to adapt. In comparison to the strategy starting with a wDIST **= 1, the clustering of orders turns out the be**
prohibitive for a later improvement.

When comparing the third strategy of starting with a wDIST = 0.**5, it becomes obvious that**
this outperforms both other ways of interacting with the system. Here, the solutions start with DIST **= 1245,** T ARDY **= 63, go to** DIST **= 946,** T ARDY **= 4342, and finally to** DIST **= 1335,**
T ARDY **= 0. Apparently, starting with a compromise solution is beneficial even for both extreme**
values of DIST and T ARDY .