Matthias Prandtstetter

Metaheuristics for solving a multimodal home-healthcare scheduling problem

We present a general framework for solving a real-world multimodal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds.

Hybrid heuristics for multimodal homecare scheduling

We focus on hybrid solution methods for a large-scale real-world multimodal homecare scheduling (MHS) problem, where the objective is to find an optimal roster for nurses who travel in tours from patient to patient, using different modes of transport. In a first step, we generate a valid initial solution using Constraint Programming (CP). In a second step, we improve the solution using one of the following metaheuristic approaches: (1) variable neighborhood descent, (2) variable neighborhood search, (3) an evolutionary algorithm, (4) scatter search and (5) a simulated annealing hyper heuristic. Our evaluation, based on computational experiments, demonstrates how hybrid approaches are particularly strong in finding promising solutions for large real-world MHS problem instances.

Meta-heuristics for reconstructing cross cut shredded text documents

In this work, we present two new approaches based on variable neighborhood search (VNS) and ant colony optimization (ACO) for the reconstruction of cross cut shredded text documents. For quickly obtaining initial solutions, we consider four different construction heuristics. While one of them is based on the well known algorithm of Prim, another one tries to match shreds according to the similarity of their borders. Two further construction heuristics rely on the fact that in most cases the left and right edges of paper documents are blank, i.e. no text is written on them. Randomized variants of these construction heuristics are applied within the ACO. Experimental tests reveal that regarding the solution quality the proposed ACO variants perform better than the VNS approaches in most cases, while the running times needed are shorter for VNS. The high potential of these approaches for reconstructing cross cut shredded text documents is underlined by the obtained results.

Combining Forces to Reconstruct Strip Shredded Text Documents

In this work, we focus on the reconstruction of strip shredded text documents(RSSTD) which is of great interest in investigative sciences and forensics. After presenting a formal model for RSSTD, we suggest two solution approaches: On the one hand, RSSTD can be reformulated as a (standard) traveling salesman problem and solved by well-known algorithms such as the chained Lin Kernighan heuristic. On the other hand, we present a specific variable neighborhood search approach. Both methods are able to outperform a previous algorithm from literature, but nevertheless have practical limits due to the necessarily imperfect objective function. We therefore turn to a semi-automatic system which also integrates user interactions in the optimization process. Practical results of this hybrid approach are excellent; difficult instances can be quickly resolved with only few user interactions.

A memetic algorithm for reconstructing cross-cut shredded text documents

The reconstruction of destroyed paper documents became of more interest during the last years. On the one hand it (often) occurs that documents are destroyed by mistake while on the other hand this type of application is relevant in the fields of forensics and archeology, e.g., for evidence or restoring ancient documents.Within this paper, we present a new approach for restoring cross-cut shredded text documents, i.e., documents which were mechanically cut into rectangular shreds of (almost) identical shape. For this purpose we present a genetic algorithm that is extended to a memetic algorithm by embedding a (restricted) variable neighborhood search (VNS). Additionally, the memetic algorithms final solution is further improved by an enhanced version of the VNS. Computational experiments suggest that the newly developed algorithms are not only competitive with the so far best known algorithms for the reconstruction of cross-cut shredded documents but clearly outperform them.

Semantically Enriched Multi-Modal Routing

We present an innovative extension to routing: intention-oriented routing which is a direct result of combining classical routing-services with Semantic Web technologies. Thereby, the intention of a user can be easily incorporated into route planning. We highlight two use cases where this hybridization is of great significance: neighborhood routing, where a neighborhood can be explored (e.g. searching for events around your place) and via routing, where errands should be run along a route (e.g. buying the ingredients for dinner on your way home). We outline the combination of different methods to achieve these services, and demonstrate the emerging framework on two case studies, with a prototype extending in-use routing services.

On the way to a multi-modal energy-efficient route

Within this paper, we present a flexible and expandable routing framework capable of finding multi-modal and inter-modal energy-efficient routes incorporating, among others, transportation modes such as public transport, electric vehicles, car-sharing, bike-sharing and walking. In contrast to conventional trip planning services, the proposed framework can evaluate routes not only with respect to travel distance or travel time but also with respect to energy used. In addition, range limitation by electric vehicles is incorporated into the routing request such that range-safety can be provided.

A Hybrid Algorithm for Computing Tours in a Spare Parts Warehouse

We consider a real-world problem arising in a warehouse for spare parts. Items ordered by customers shall be collected and for this purpose our task is to determine efficient pickup tours within the warehouse. The algorithm we propose embeds a dynamic programming algorithm for computing individual optimal walks through the warehouse in a general variable neighborhood search (VNS) scheme. To enhance the performance of our approach we introduce a new self-adaptive variable neighborhood descent used as local improvement procedure within VNS. Experimental results indicate that our method provides valuable pickup plans, whereas the computation times are kept low and several constraints typically stated by spare parts suppliers are fulfilled.

A Variable Neighborhood Search Approach for the Interdependent Lock Scheduling Problem

We investigate a so far not examined problem called the Interdependent Lock Scheduling Problem. A Variable Neighborhood Search approach is proposed for finding lock schedules along the Austrian part of the Danube River in order to minimize the overall ship travel times. In computational experiments the performance of our approach is assessed and compared to real-world ship trajectories. Notable improvements can be achieved. In addition, the number of (empty) lockages can be significantly reduced when taking them into account during optimization without loosing too much of quality in travel time optimization.

Optimizing Charging Station Locations for Electric Car-Sharing Systems

This paper is about strategic decisions required for running an urban station-based electric car-sharing system. In such a system, users can rent and return publicly available electric cars from charging stations. We approach the problem of deciding on the location and size of these stations and on the total number of cars in such a system using a bi-level model. The first level of the model identifies the number of rental stations, the number of slots at each station, and the total number of cars to be acquired. Then, such a generated solution is evaluated by computing which trips can be accepted by the system using a path-based heuristic on a time-expanded location network. This path-based heuristic iteratively finds paths for the cars through this network. We compare three different pathfinder methods, which are all based on the concept of tree search using a greedy criterion. The algorithm is evaluated on a set of benchmark instances which are based on real-world data from Vienna, Austria using a demand model derived from taxi data of about 3500 taxis operating in Vienna. Computational tests show that for smaller instances the algorithm is able to find near optimal solutions and that it scales well for larger instances.