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We discuss the problem of evaluating a robust solution.
To this end, we first give a short primer on how to apply robustification approaches to uncertain optimization problems using the assignment problem and the knapsack problem as illustrative examples.
As it is not immediately clear in practice which such robustness approach is suitable for the problem at hand,
we present current approaches for evaluating and comparing robustness from the literature, and introduce the new concept of a scenario curve. Using the methods presented in this paper, an easy guide is given to the decision maker to find, solve and compare the best robust optimization method for his purposes.

By natural or man-made disasters, the evacuation of a whole region or city may become necessary. Apart from private traffic, the evacuation from collection points to secure shelters outside the endangered region will be realized by a bus fleet made available by emergency relief. The arising Bus Evacuation Problem (BEP) is a vehicle scheduling problem, in which a given number of evacuees needs to be transported from a set of collection points to a set of capacitated shelters, minimizing the total evacuation time, i.e., the time needed until the last person is brought to safety.
In this paper we consider an extended version of the BEP, the Robust Bus Evacuation Problem (RBEP), in which the exact numbers of evacuees are not known, but may stem from a set of probable scenarios. However, after a given reckoning time, this uncertainty is eliminated and planners are given exact figures. The problem is to decide for each bus, if it is better to send it right away -- using uncertain numbers of evacuees -- or to wait until the numbers become known.
We present a mixed-integer linear programming formulation for the RBEP and discuss solution approaches; in particular, we present a tabu search framework for finding heuristic solutions of acceptable quality within short computation time. In computational experiments using both randomly generated instances and the real-world scenario of evacuating the city of Kaiserslautern, we compare our solution approaches.

The Bus Evacuation Problem (BEP) is a vehicle routing problem that arises in emergency planning. It models the evacuation of a region from a set of collection points to a set of capacitated shelters with the help of buses, minimizing the time needed to bring the last person out of the endangered region.
In this work, we describe multiple approaches for finding both lower and upper bounds for the BEP, and apply them in a branch and bound framework. Several node pruning techniques and branching rules are discussed. In computational experiments, we show that solution times of our approach are significantly improved compared to a commercial integer programming solver.

We consider the problem of scheduling a bus fleet to evacuate persons from an endangered region. As most of the planning data is subject to uncertainty, we develop a two-stage bicriteria robust formulation, which considers both the evacuation time, and the vulnerability of the schedule to changing evacuation circumstances.
As the resulting integer program is too large to solve it directly using an off-the-shelf solver, we develop an iterative algorithm that successively adds new scenarios to the currently considered subproblem. In computational experiments, we show that this approach is fast enough to deal with an instance modeling an evacuation case within the city of Kaiserslautern, Germany.

We consider the problem of evacuating a region with the help of buses. For a given set of possible collection points where evacuees gather, and possible shelter locations where evacuees are brought to, we need to determine both collection points and shelters we would like to use, and bus routes that evacuate the region in minimum time.
We model this integrated problem using an integer linear program, and present a branch-cut-and-price algorithm that generates bus tours in its pricing step. In computational experiments we show that our approach is able to solve instances of realistic size in sufficient time for practical application, and considerably outperforms the usage of a generic ILP solver.

We present a new approach to handle uncertain combinatorial optimization problems that uses solution ranking procedures to determine the degree of robustness of a solution. Unlike classic concepts for robust optimization, our approach is not purely based on absolute quantitative performance, but also includes qualitative aspects that are of major importance for the decision maker.
We discuss the two variants, solution ranking and objective ranking robustness, in more detail, presenting problem complexities and solution approaches. Using an uncertain shortest path problem as a computational example, the potential of our approach is demonstrated in the context of evacuation planning due to river flooding.

We consider the problem to evacuate several regions due to river flooding, where sufficient time is given to plan ahead. To ensure a smooth evacuation procedure, our model includes the decision which regions to assign to which shelter, and when evacuation orders should be issued, such that roads do not become congested.
Due to uncertainty in weather forecast, several possible scenarios are simultaneously considered in a robust optimization framework. To solve the resulting integer program, we apply a Tabu search algorithm based on decomposing the problem into better tractable subproblems. Computational experiments on random instances and an instance based on Kulmbach, Germany, data show considerable improvement compared to an MIP solver provided with a strong starting solution.

We argue that the concepts of resilience in engineering science and robustness in mathematical optimization are strongly related. Using evacuation planning as an example application, we demonstrate optimization techniques to improve solution resilience. These include a direct modelling of the uncertainty for stochastic or robust optimization, as well as taking multiple objective functions into account.

We consider an uncertain traveling salesman problem, where distances between nodes are not known exactly, but may stem from an uncertainty set of possible scenarios. This uncertainty set is given as intervals with an additional bound on the number of distances that may deviate from their expected, nominal value.
A recoverable robust model is proposed, that allows a tour to change a bounded number of edges once a scenario becomes known. As the model contains an exponential number of constraints and variables, an iterative algorithm is proposed, in which tours and scenarios are computed alternately.
While this approach is able to find a provably optimal solution to the robust model, it also needs to solve increasingly complex subproblems. Therefore, we also consider heuristic solution procedures based on local search moves using a heuristic estimate of the actual objective function. In computational experiments, these approaches are compared.
Finally, an alternative recovery model is discussed, where a second-stage recovery tour is not required to visit all nodes of the graph. We show that the previously NP-hard evaluation of a fixed solution now becomes solvable in polynomial time.

The ordered weighted averaging objective (OWA) is an aggregate function over multiple optimization criteria which received increasing attention by the research community over the last decade. Different to the ordered weighted sum, weights are attached to ordered objective functions (i.e., a weight for the largest value, a weight for the second-largest value and so on). As this contains max-min or worst-case optimization as a special case, OWA can also be considered as an alternative approach to robust optimization.
For linear programs with OWA objective, compact reformulations exist, which result in extended linear programs. We present new such reformulation models with reduced size. A computational comparison indicates that these formulations improve solution times.