A Hyper-heuristic Approach to the Home Care Scheduling Problem
Bibliographic record
Abstract
1 AbstractThe Home Care Scheduling Problem (HCSP) involves as-signing nurses to certain people or patients who are in needof support within quite strict time windows at their homes.The assignment is based on particular support requirementsand qualifications of the nurses. Each nurse should visit allthe assigned patients within the determined time windows toperform the tasks concerning them. During these visits, to-tal traveling time should be minimized and a number of con-straints concerning the nurses’ rosters should be handled.HCSP is a combination of two NP-hard problems, namelythe Nurse Rostering Problem (NRP) and the Vehicle Rout-ing Problem (VRP). In the literature, we can find plenty ofstudies related to both problems. However, very limitedstudies are available about the combined HCSP. A relatedcombined problem that shows similar characteristics undersimpler constraints is the Vehicle Routing and SchedulingProblem with Time Window Constraints (VRSPTW) [9].It is the VRP with time windows that cover the time hori-zon for delivering goods or services to customers. In theHCSP, different nurses with different skills are required, theservices delivered are more complicated and more types oftasks are present than in the VRSPTW.There is a limited number of papers about the HCSP in theliterature [1, 2, 3, 4, 6, 7, 8]. Fast heuristic approaches are analternative to solve the HCSP. However, the problem depen-dency of such optimisation strategies is an important issue tobe tackled. It is possible to design an algorithm that can findhigh performance solutions for a specific problem or just aninstance of a problem. However, the algorithm may not besuccessful to solve related problems or different instances orit may require lots of changes or tuning to be adapted to thenew problem. Hyper-heuristics are generic search strategiesthat perform search over a heuristic search space instead ofthe solution space to clear up this case [5]. In this study, weapply a new improvement hyper-heuristic which chooses thebest possible low-level heuristic at each optimization stepfor the HCSP to show the potential of hyper-heuristics fordifficult combined problems.References[1] C. Akjiratikarl, P. Yenradee, and P.R. Drake. Pso-based algorithm for home care worker scheduling in theuk. Computers and Industrial Engineering, 53(4):559–583,2007.[2] S.V. Begur, D.M. Miller, and J.R. Weaver. An inte-grated spatial dss for scheduling and routing home-health-care nurses. Interfaces, 27:35–48, 1997.[3] S. Bertels and T. Fahle. A hybrid setup for a hy-brid scenario: combining heuristics for the home healthcare problem. Computers and Operations Research,33(10):2866–2890, 2006.[4] V. Borsani, A. Matta, G. Beschi, and F. Sommaruga.A home care scheduling model for human resources. In Pro-ceedingsoftheInternationalConferenceonServiceSystemsand Service Management (ICSSSM), Troyes, France, 2006.[5] E.K. Burke, E. Hart, G. Kendall, J. Newall, P. Ross,and S. Schulenburg. Handbook of Meta-Heuristics, chap-ter Hyper-Heuristics: An Emerging Direction in ModernSearch Technology, pages 457–474. Kluwer Academic Pub-lishers, 2003.[6] E. Cheng and J.L. Rich. A home health care rout-ing and scheduling problem. Technical report caam tr98-04,Rice University, 1998.[7] P. Eveborn, P. Flisberg, and M. Ronnqvist. Laps care–an operational system for staff planning of home care. Eu-ropean Journal of Operational Research, 171(3):962–976,June 2006.[8] K. Martin and M. Wright. Using particle swarm op-timization to determine the visit times in community nursetimetabling. In Proceedings ofthe 7thInternational Confer-ence on the Practice and Theory of Automated Timetabling(PATAT’08), Montreal, Canada, August 19–22 2008.[9] M.M. Solomon. Algorithms for the vehicle routingand scheduling problems with time window constraints. Op-erations Research, 35(2):254–265, 1987.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".