International audienceWe present a general framework for solving a real-world multi-modal 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

Balancing bike sharing systems is an increasingly important problem, because of the rising popularity of this mean of transportation. Bike sharing systems need to be balanced so that bikes (and empty slots for returning bikes) are available to the customers, thus ensuring an adequate level of service. In this paper, we tackle the problem of balancing a real-world bike sharing system (BBSP) by means of a hybrid metaheuristic method. Our main contributions are: (i) a new Constraint Programming (CP) formulation for the problem, and (ii) a novel hybrid approach which combines CP techniques with Ant Colony Optimization (ACO). We validate our approach against real world instances from the Vienna Citybike system

In order to meet the users' demand, bike sharing systems must be regularly rebalanced. The problem of balancing bike sharing systems (BBSS) is concerned with designing optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS by means of Constraint Programming: first, we introduce two novel constraint models for the BBSS including a smart branching strategy that focusses on the most promising routes. Second, in order to speed-up the search process, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform a computational evaluation on instances based on real-world data, where we see that the LNS approach outperforms the Branch & Bound approach and is competitive with other existing approaches

Bike sharing systems need to be properly rebalanced to meet the demand of users and to operate successfully. However, the problem of Balancing Bike Sharing Systems (BBSS) is a demanding task: it requires the design of optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS problem by means of Constraint Programming (CP). First, we introduce two different CP models for the BBSS problem including two custom branching strategies that focus on the most promising routes. Second, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform an experimental evaluation of our approaches on three different benchmark sets of instances derived from real-world bike sharing systems. We show that our CP models can be easily adapted to the different benchmark problem setups, demonstrating the benefit of using Constraint Programming to address the BBSS problem. Furthermore, in our experimental evaluation, we see that the pure CP (branch & bound) approach outperforms the state-of-the-art MILP on large instances and that the LNS approach is competitive with other existing approache