2012
DOI: 10.1016/j.ejor.2011.10.048
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The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

Abstract: In the Home Care Crew Scheduling Problem a staff of caretakers has to be assigned a number of visits to patients' homes, such that the overall service level is maximised. The problem is a generalisation of the vehicle routing problem with time windows. Required travel time between visits and time windows of the visits must be respected. The challenge when assigning visits to caretakers lies in the existence of soft preference constraints and in temporal dependencies between the start times of visits.We model t… Show more

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Cited by 339 publications
(241 citation statements)
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“…The objective of the model is to minimise the sum of travel times and waiting times and the dissatisfaction levels of patients and healthcare operators, which are subject to assignment constraints, working time restrictions, time windows and mandatory breaks. Rasmussen et al (2012) provided a schedule that considers time windows for travel and visits. Nickel et al combine meta-heuristics with methods from constraint programming to solve the scheduling problem at the week level; it is later adapted to the day level.…”
Section: Manage Therapeutic Projectsmentioning
confidence: 99%
“…The objective of the model is to minimise the sum of travel times and waiting times and the dissatisfaction levels of patients and healthcare operators, which are subject to assignment constraints, working time restrictions, time windows and mandatory breaks. Rasmussen et al (2012) provided a schedule that considers time windows for travel and visits. Nickel et al combine meta-heuristics with methods from constraint programming to solve the scheduling problem at the week level; it is later adapted to the day level.…”
Section: Manage Therapeutic Projectsmentioning
confidence: 99%
“…Mathematical programming has been used in the literature to tackle some WSRP. Examples include linear programming (Angelis, 1998), integer programming (Kergosien et al, 2009) and mixed integer programming (Bredstrom and Ronnqvist, 2007;Dohn et al, 2009;Borsani et al, 2006;Rasmussen et al, 2012). Solving real-world size problems often resources to heuristic or hybrid algorithms (Bertels and Torsten, 2006;Akjiratikarl et al, 2007;Eveborn et al, 2009).…”
Section: Literature Reviewmentioning
confidence: 99%
“…Solving real-world size problems often resources to heuristic or hybrid algorithms (Bertels and Torsten, 2006;Akjiratikarl et al, 2007;Eveborn et al, 2009). There are some improved exact methods, like branch and price (Bredstrom and Ronnqvist, 2007;Dohn et al, 2009;Rasmussen et al, 2012), that can deal with large instances. Branch and price requires problem reformulation which usually involves Dantzig-Wolfe decomposition to compute a tighter relaxation bound (Vanderbeck, 2000;Feillet, 2010).…”
Section: Literature Reviewmentioning
confidence: 99%
“…Compared to the traditional healthcare service, home healthcare is special considering that the medical staff needs to offer visiting services, and patient satisfaction drops with longer waiting time [33][34][35][36]. However, if the waiting time exceeds the acceptable and tolerable limit, there would be an exponential plummet in patient satisfaction, and patient satisfaction could even drop to zero when patients terminate a service request for home healthcare and choose other kinds of medical services [12].…”
Section: Literature Reviewmentioning
confidence: 99%