## Report in Wirtschaftsmathematik (WIMA Report)

- 161
- Zone-based, Robust Flood Evacuation Planning (2016)
- 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.

- 160
- Ranking Robustness and its Application to Evacuation Planning (2016)
- 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.

- 159
- Minimizing the Number of Apertures in Multileaf Collimator Sequencing with Field Splitting (2015)
- In this paper we consider the problem of decomposing a given integer matrix A into a positive integer linear combination of consecutive-ones matrices with a bound on the number of columns per matrix. This problem is of relevance in the realization stage of intensity modulated radiation therapy (IMRT) using linear accelerators and multileaf collimators with limited width. Constrained and unconstrained versions of the problem with the objectives of minimizing beam-on time and decomposition cardinality are considered. We introduce a new approach which can be used to find the minimum beam-on time for both constrained and unconstrained versions of the problem. The decomposition cardinality problem is shown to be NP-hard and an approach is proposed to solve the lexicographic decomposition problem of minimizing the decomposition cardinality subject to optimal beam-on time.

- 158
- Robust storage loading problems with stacking and payload constraints (2015)
- We consider storage loading problems where items with uncertain weights have to be loaded into a storage area, taking into account stacking and payload constraints. Following the robust optimization paradigm, we propose strict and adjustable optimization models for finite and interval-based uncertainties. To solve these problems, exact decomposition and heuristic solution algorithms are developed. For strict robustness, we also present a compact formulation based on a characterization of worst-case scenarios. Computational results show that computation times and algorithm gaps are reasonable for practical applications. Furthermore, we find that the robustness concepts show different potential depending on the type of data being used.

- 157
- Discrete Parallel Machine Makespan ScheLoc Problem (2015)
- Scheduling-Location (ScheLoc) Problems integrate the separate fields of scheduling and location problems. In ScheLoc Problems the objective is to find locations for the machines and a schedule for each machine subject to some production and location constraints such that some scheduling object- ive is minimized. In this paper we consider the Discrete Parallel Machine Makespan (DPMM) ScheLoc Problem where the set of possible machine loc- ations is discrete and a set of n jobs has to be taken to the machines and processed such that the makespan is minimized. Since the separate location and scheduling problem are both NP-hard, so is the corresponding ScheLoc Problem. Therefore, we propose an integer programming formulation and different versions of clustering heuristics, where jobs are split into clusters and each cluster is assigned to one of the possible machine locations. Since the IP formulation can only be solved for small scale instances we propose several lower bounds to measure the quality of the clustering heuristics. Ex- tensive computational tests show the efficiency of the heuristics.

- 156
- A new solution approach for solving the 2-facility location problem in the plane with block norms (2015)
- Motivated by the time-dependent location problem over T time-periods introduced in Maier and Hamacher (2015) we consider the special case of two time-steps, which was shown to be equivalent to the static 2-facility location problem in the plane. Geometric optimality conditions are stated for the median objective. When using block norms, these conditions are used to derive a polygon grid inducing a subdivision of the plane based on normal cones, yielding a new approach to solve the 2-facility location problem in polynomial time. Combinatorial algorithms for the 2-facility location problem based on geometric properties are deduced and their complexities are analyzed. These methods differ from others as they are completely working on geometric objects to derive the optimal solution set.

- 155
- A Finite Dominating Set Algorithm for a Dynamic Location Problem in the Plane (2014)
- A single facility problem in the plane is considered, where an optimal location has to be identified for each of finitely many time-steps with respect to time-dependent weights and demand points. It is shown that the median objective can be reduced to a special case of the static multifacility median problem such that results from the latter can be used to tackle the dynamic location problem. When using block norms as distance measure between facilities, a Finite Dominating Set (FDS) is derived. For the special case with only two time-steps, the resulting algorithm is analyzed with respect to its worst-case complexity. Due to the relation between dynamic location problems for T time periods and T-facility problems, this algorithm can also be applied to the static 2-facility location problem.

- 154
- Bicriteria approach to the optimal location of surveillance cameras (2014)
- We consider the problem of finding efficient locations of surveillance cameras, where we distinguish between two different problems. In the first, the whole area must be monitored and the number of cameras should be as small as possible. In the second, the goal is to maximize the monitored area for a fixed number of cameras. In both of these problems, restrictions on the ability of the cameras, like limited depth of view or range of vision are taken into account. We present solution approaches for these problems and report on results of their implementations applied to an authentic problem. We also consider a bicriteria problem with two objectives: maximizing the monitored area and minimizing the number of cameras, and solve it for our study case.

- 152
- A coverage-based Box-Algorithm to compute a representation for optimization problems with three objective functions (2014)
- A new algorithm for optimization problems with three objective functions is presented which computes a representation for the set of nondominated points. This representation is guaranteed to have a desired coverage error and a bound on the number of iterations needed by the algorithm to meet this coverage error is derived. Since the representation does not necessarily contain nondominated points only, ideas to calculate bounds for the representation error are given. Moreover, the incorporation of domination during the algorithm and other quality measures are discussed.

- 151
- Optimization Models to Enhance Resilience in Evacuation Planning (2014)
- 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.