Metaheuristics for solving a multimodal home-healthcare scheduling problem

Abstract 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

Effective compilation of constraint models

Constraint Programming is a powerful technique for solving large-scale combinatorial (optimisation) problems. However, it is often inaccessible to users without expert knowledge in the area, precluding the wide-spread use of Constraint Programming techniques. This thesis addresses this issue in three main contributions. First, we propose a simple ‘model-and-solve’ approach, consisting of a framework where the user formulates a solver-independent problem model, which is then automatically tailored to the input format of a selected constraint solver (a process similar to compiling a high-level modelling language to machine code). The solver is then executed on the input, solver, and solutions (if they exist) are returned to the user. This allows the user to formulate constraint models without requiring any particular background knowledge of the respective solver and its solving technique. Furthermore, since the framework can target several solvers, the user can explore different types of solvers. Second, we extend the tailoring process with model optimisations that can compensate for a wide selection of poor modelling choices that novices (and experts) in Constraint Programming often make and hence result in redundancies. The elimination of these redundancies by the proposed optimisation techniques can result in solving time speedups of over an order of magnitude, in both naive and expert models. Furthermore, the optimisations are particularly light-weight, adding negligible overhead to the overall translation process. The third contribution is the implementation of this framework in the tool TAILOR, that currently translates 2 different solver-independent modelling languages to 3 different solver formats and is freely available online. It performs almost all optimisation techniques that are proposed in this thesis and demonstrates its significance in our empirical analysis. In summary, this thesis presents a framework that facilitates modelling for both experts and novices: problems can be formulated in a clear, high-level fashion, without requiring any particular background knowledge about constraint solvers and their solving techniques, while (sometimes naturally occurring) redundancies in the model are eliminated for practically no additional cost, improving the respective model in solving performance by up to an order of magnitude

Effective compilation of constraint models

Constraint Programming is a powerful technique for solving large-scale combinatorial (optimisation) problems. However, it is often inaccessible to users without expert knowledge in the area, precluding the wide-spread use of Constraint Programming techniques. This thesis addresses this issue in three main contributions. First, we propose a simple ‘model-and-solve’ approach, consisting of a framework where the user formulates a solver-independent problem model, which is then automatically tailored to the input format of a selected constraint solver (a process similar to compiling a high-level modelling language to machine code). The solver is then executed on the input, solver, and solutions (if they exist) are returned to the user. This allows the user to formulate constraint models without requiring any particular background knowledge of the respective solver and its solving technique. Furthermore, since the framework can target several solvers, the user can explore different types of solvers. Second, we extend the tailoring process with model optimisations that can compensate for a wide selection of poor modelling choices that novices (and experts) in Constraint Programming often make and hence result in redundancies. The elimination of these redundancies by the proposed optimisation techniques can result in solving time speedups of over an order of magnitude, in both naive and expert models. Furthermore, the optimisations are particularly light-weight, adding negligible overhead to the overall translation process. The third contribution is the implementation of this framework in the tool TAILOR, that currently translates 2 different solver-independent modelling languages to 3 different solver formats and is freely available online. It performs almost all optimisation techniques that are proposed in this thesis and demonstrates its significance in our empirical analysis. In summary, this thesis presents a framework that facilitates modelling for both experts and novices: problems can be formulated in a clear, high-level fashion, without requiring any particular background knowledge about constraint solvers and their solving techniques, while (sometimes naturally occurring) redundancies in the model are eliminated for practically no additional cost, improving the respective model in solving performance by up to an order of magnitude.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Constraint Model for the Settlers Planning Domain

The Settlers planning domain has proved a challenging problem for planning technology. We present a preliminary model of Settlers in the Essence ’ specification language. We generate a constraint model for the CSP solver Minion using the automated modelling tool Tailor. We show this model to be competitive with state-of-the-art planning technology

Large Neighborhood Search · Optimization

Submitted for publication Abstract 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 approaches