Metaheuristics for solving a multimodal home-healthcare scheduling problem

Abstract : We 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.
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Central European Journal of Operations Research, Springer Verlag, 2015, 23 (&), pp.89-113. 〈10.1007/s10100-013-0305-8〉
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Gerhard Hiermann, Matthias Prandtstetter, Andrea Rendl, Jakob Puchinger, Günther Raidl. Metaheuristics for solving a multimodal home-healthcare scheduling problem. Central European Journal of Operations Research, Springer Verlag, 2015, 23 (&), pp.89-113. 〈10.1007/s10100-013-0305-8〉. 〈hal-01224625〉

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