Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

Abstract

Abstract:The aggravating trend of the aging population, the miniaturization of the family structure, and the increase of families with empty nesters greatly affect the sustainable development of the national economy and social old-age security system of China. The emergence of home health care or home care (HHC/HC) service mode provides an alternative for elderly care. How to develop and apply this new mobile service mode is crucial for the government. Therefore, the pertinent optimization problems regarding HHC/HC have constantly attracted the attention of researchers. Unexpected events, such as new requests of customers, cancellations of customers' services, and changes of customers' time windows, may occur during the process of executing an a priori visiting plan. These events may sometimes make the original plan non-optimal or even infeasible. To cope with this situation, we introduce disruption management to the real-time home caregiver scheduling and routing problem. The deviation measurements on customers, caregivers, and companies are first defined. A mathematical model that minimizes the weighted sum of deviation measurements is then constructed. Next, a tabu search (TS) heuristic is developed to efficiently solve the problem, and a cost recorded mechanism is used to strengthen the performance. Finally, by performing computational experiments on three real-life instances, the effectiveness of the TS heuristic is tested, and the advantages of disruption management are analyzed.

Impact of digital twin technology on the real-time optimisation of material handling processes

“…There are different types of solutions for this optimization problem including assignment, scheduling and routing. It is possible use different types of metaheuristic solution, like black hole algorithm, flower pollination heuristics [11], savings-based algorithm [12], tabu search [13], simulated annealing [14], decision diagrams combined with branch-and-bound [15,16] or simulation [17]. The main findings of the above-mentioned review can be summarized as follows:…”

Routing and scheduling in Home Health Care: A literature survey and bibliometric analysis

“…First, they can stem from the patients who may require a change in the frequency of their visits (Mosquera et al, 2018) or in their assigned time slot (Lin et al, 2018). They may also have new demands or cancel their planned visits (Yuan and Jiang, 2017). Sometimes one-time cancellations occur (Gunawan et al, 2017), but at other times, the patient is simply getting out of the system because his/her health declined and he/she had to be hospitalized (Gomes and Ramos, 2019).…”

“…Dynamic problems can be handled with a stability objective, which consists in staying as close as possible to the initial schedule: it offers a continuity highly appreciated by both staff members and patients. In (Yuan and Jiang, 2017) for example, the stability is maximized for all stakeholders: the patients, the staff, but also the company. Starting and ending times or assignment of staff members are the main criteria of stability.…”

“…Nickel et al (2012) consider the changes in the starting times of the cares, whileShi et al (2017b) compute extra-working times and delayed visits. In(Yuan and Jiang, 2017), three criteria are jointly minimized: (1) changes in starting times of care, (2) changes in the length of the tour, and the succession of patients, (3) additional costs due to late penalties, overtime costs, etc. Costs induced by the changes in the initial planning are taken into account in 19% of papers: it includes delayed visit penalties or reassignment costs Du et al (2017b).…”

BLPAD.Core: A Multi-Functions Optimizer Towards Daily Planning Generation in Home Health Care

“…The algorithm Tabu Search (TS) is largely used for solving the HHCSRP, Hertz and Lahrichi [10] consider the deterministic patients to caregivers assignment for HHC service by using TS. A daily planning generation using public transport [11], a periodic home health care logistics [12] [13], a sustainable delivery scheduling [14], a robust planning model considering the temporal uncertainty [15], a real-time scheduling problem with the disruption management [16] are solved by TS with the good performance. Moreover, Simulated Annealing (SA) is widely used for approximate solution generation in HHCSRP, such as an interdependent services based scheduling problem [17], a bi-objective green routing problem [18], a simultaneous delivery and pick-up problem [19], a workforce scheduling problem [20], a multimodal scheduling problem [21].…”