A Hyper-heuristic Approach to the Home Care Scheduling Problem
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».