A Branch-Price-and-Cut approach for solving the medium-term home health care planning problem

Trautsamwieser, Andrea; Hirsch, Patrick

doi:10.1002/net.21566

Cited by 87 publications

(46 citation statements)

References 29 publications

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“…They introduced two phase heuristic algorithms and tested them using real data sets and randomly generated data instances of the problem. A medium-term home health care problem along with a metaheuristic solution method was devised by Trautsamwieser and Hirsch [18]. The authors proposed a branch-price and cut approach, which uses a VNS solution as the upper bounds.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (16) and (17) implement the break time windows restrictions for the hired nurses. Constraints (18) and (19) ensure that the existing nurses and patients must be assigned in daily planning for the duration of their respective contracts. These two constraints also avoid the inclusion of a patient or nurse in the planning model, if his/her contract is already expired.…”

Section: Mathematical Modelmentioning

confidence: 99%

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Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Nasir

Dang

2018

Sustainability

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Development of an efficient and effective home health care (HHC) service system is a quite recent and challenging task for the HHC firms. This paper aims to develop an HHC service system in the perspective of long-term economic sustainability as well as operational efficiency. A more flexible mixed-integer linear programming (MILP) model is formulated by incorporating the dynamic arrival and departure of patients along with the selection of new patients and nursing staff. An integrated model is proposed that jointly addresses: (i) patient selection; (ii) nurse hiring; (iii) nurse to patient assignment; and (iv) scheduling and routing decisions in a daily HHC planning problem. The proposed model extends the HHC problem from conventional scheduling and routing issues to demand and capacity management aspects. It enables an HHC firm to solve the daily scheduling and routing problem considering existing patients and nursing staff in combination with the simultaneous selection of new patients and nurses, and optimizing the existing routes by including new patients and nurses. The model considers planning issues related to compatibility, time restrictions, contract durations, idle time and workload balance. Two heuristic methods are proposed to solve the model by exploiting the variable neighborhood search (VNS) approach. Results obtained from the heuristic methods are compared with a CPLEX based solution. Numerical experiments performed on different data sets, show the efficiency and effectiveness of the solution methods to handle the considered problem.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Nasir

Dang

2018

Sustainability

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“…Only small instances (four nurses and 20 clients [15], nine nurses and 45 clients [18]) can be solved optimally; hence, to deal with larger instances, a heuristic approach is required in this framework.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (16) and (17) express a piecewise linear function of the deviation from the starting time of the activity. Constraint (18) determines the arriving times of nurses according to the starting time of the previous jobs, the travelling times based on the transportation mode, and the duration of the jobs. The idle times of 138 nurses are computed via constraints (19) and (20) (22) is a non-negativity constraint.…”

Section: Problem Formulationmentioning

confidence: 99%

A Two-Stage Solution Approach for the Large-Scale Home Healthcare Routeing and Scheduling Problem

Erdem¹,

Bulkan

2017

SAJIE

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The purpose of this study is to introduce a two-stage solution approach for a large-scale home healthcare routeing and scheduling problem (HHCRSP). In the first part of the two-stage solution approach, a cluster-assign algorithm is employed, based on the home location and the time to obtain feasible clusters. In the second stage, using these clusters, route construction heuristics start to create schedules and routes, taking the side constraints of the model into consideration. Using the novelty of this two-stage solution approach, higher diversification is achieved with a series of newly-developed cross movement strategies. The computational results show that our solution approach offers certain advantages, such as an increase in the efficient use of human resources, and a decrease in the working time of nurses. OPSOMMINGDie doel van hierdie studie is om ŉ tweeledige oplossingsbenadering vir grootskaalse tuissorg roetebeplanning en skedulering voor te stel vir gesondheidsorg. In die eerste deel van die benadering word ŉ groepering-algoritme, gebaseer op die ligging van die tuiste en die tyd om vatbare groepe te vorm, toegepas. Tydens die tweede stap van die benadering word die groepe gebruik saam met roetebeplanning-heuristiek om skedules en roetes te skep terwyl die randvoor-waardes van die model in ag geneem word. Die tweeledige benadering bewerkstellig hoër diversifikasie met ŉ reeks nuut ontwikkelde kruisbeweging strategieë. Die gesimuleerde resultate toon dat die voorgestelde oplossingsbenadering sekere voordele inhou, onder andere die ŉ verhoging in die effektiewe aanwending van menslikehulpbronne en ŉ afname in die werktyd van verpleegsters.

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“…Bard et al [17][18][19] and Shao et al [20] design a sequential greedy randomized adaptive search procedure (GRASP), a parallel GRASP, and a branch-and-price-and-cut algorithm to solve the weekly therapist routing and scheduling problem, which defines a set of therapists that can see a given patient. Trautsamwieser et al [21] consider additional working time regulations (e.g., breaks, maximum working time per day, and daily as well as weekly rest times) for nurses into the weekly HCSRP and propose a branch-price-and-cut solution approach to exactly solve the problem using the solutions of a variable neighborhood search (VNS) algorithm as upper bounds. Cappanera et al [22] use a pattern modeling device to jointly coordinate the assignment, scheduling, and routing decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Yuan

Jiang

2017

Sustainability

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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.

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A Branch-Price-and-Cut approach for solving the medium-term home health care planning problem

Cited by 87 publications

References 29 publications

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

A Two-Stage Solution Approach for the Large-Scale Home Healthcare Routeing and Scheduling Problem

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

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