Simulated annealing algorithm for scheduling problem in daily nursing cares

Abstract

In most Japanese hospitals, different nurses handle the pre-assigned nursing cares in different ways, which directly affect the quality of nursing cares. To our knowledge, there has been less attention on ensuring that nurses provide nursing cares in a timely and accurate fashion. Consequently, in this paper, considering the similarities to the traditional job shop scheduling problems, we will model the daily nursing care scheduling problems and propose an efficient scheduling method based on simulated annealing algorithm. By iteratively local searching based on simulated annealing: (1) permutating the tasks from one nurse to another and (2) permutating the sub tasks handled by a nurse from its original position to another new one, the proposed method is evaluated to be applicable to the nursing care scheduling problems (providing comprehensive, coordinated and cost effective nursing cares to patients).

Dynamic scheduling-based inpatient nursing support: applicability evaluation by laboratory experiments

“…In our previous study, we proposed a dynamic scheduling method based on a simulated annealing algorithm (Cheng et al, 2008(Cheng et al, , 2009. Through the generation of an initial schedule in a static status and regenerating schedules when interruptions occur, the proposed method was proven effective for high-quality care provision throughout several simulated NSPs.…”

Organizing integrated processes in cancer care

“…Instead of a patient that has to visit multiple types of providers, a provider has to visit multiple types of patients. For example in patient-to-nurse scheduling at the wards, which can be represented by an open-shop system, time constraints are restricting the possible schedules [66].…”

“…Series of examinations for vascular checkups are scheduled in [76], either on one day or on multiple days, for inpatients as well as outpatients. [66] schedule various (partially ordered) nurse tasks that have to be performed in a day horizon.…”

“…Hierarchical level 181,197,236,254,265,273,313], heuristics [67,76,143,148,181,198,241,277,313], simulation [15], genetic algorithms [16,68,69,148,197,241], local search methods [66], and data mining [15,68]. For online planning, we found 21 papers.…”

“…A focus on direct waiting times ensures quick access for patients on the day of the appointments. An interesting approach is adopted by [66], who assess the timeliness of care by taking both the tardiness and earliness into account. Notably, no authors have evaluated the accessibility in mixed-shop systems from an access time perspective.…”