The therapist routing and scheduling problem

Shao, Yufen; Bard, Jonathan F.; Jarrah, Ahmad I.

doi:10.1080/0740817x.2012.665202

Cited by 53 publications

(22 citation statements)

References 25 publications

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“…The authors develop an auction heuristic enhanced by VNS and GRASP concepts that yields a reduction in traveled distance of more than 20% in comparison to the currently implemented solution. A similar problem is studied in Shao, Bard, and Jarrah [128], where the authors investigate the assignment and routing of therapists providing home health care.…”

Section: Case Studiesmentioning

confidence: 92%

Chapter 9: Four Variants of the Vehicle Routing Problem

Irnich¹,

Schneider²,

Vigo³

2014

Vehicle Routing

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In this chapter, we review four important variants of the Vehicle Routing Problem (VRP): 1. VRP with Backhauls (VRPB); 2. Heterogeneous or mixed Fleet VRP (HFVRP); 3. Periodic VRP (PVRP); 4. Split Delivery VRP (SDVRP).Each family plays an important role in the literature and considers a characteristic feature often encountered, both alone and combined, in real-world applications. The resulting VRP variants are non-trivial extensions of the basic VRP, and they all deserve specifically tailored algorithmic approaches for their effective solution.In the first problem class, the VRP with Backhauls (VRPB), the customers are partitioned into two sets, the linehaul and backhaul customers. The linehaul customers require the delivery of goods from the depot, and the backhaul customers have goods to be picked up and transported to the depot. Since a rearrangement of the load inside a vehicle is often impossible or undesirable, e.g., because the vehicle's loading space is only accessible from the rear, a precedence relation on the service of linehaul and backhaul customers is imposed: In a feasible VRPB solution the combined service of linehauls and backhauls in the same route is possible, but all linehaul customers receive their demand before load at backhaul customers is picked up. The VRPB belongs to the more general class of delivery and collection VRP, which clearly introduces considerable improvements over separate distribution and collection services in different routes.In the second variant, the family of Heterogeneous or mixed Fleet VRP (HFVRP), the fleet consists of different vehicles or vehicle types, each with a specific capacity and costs.

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Section: Case Studiesmentioning

confidence: 92%

Chapter 9: Four Variants of the Vehicle Routing Problem

Irnich¹,

Schneider²,

Vigo³

2014

Vehicle Routing

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“…The authors proposed a branch-price and cut approach, which uses a VNS solution as the upper bounds. Shao et al [19] studied the therapist routing and scheduling problem, proposing a MILP model which considers different patient categories, optional weekly treatment arrangements and a complex payment structure. The heuristic method developed by the authors, greedy randomized adaptive search procedure (GRASP), adopts a two-phase solution strategy.…”

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%

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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“…An et al [16] investigate the HCSRP when the intervals between visits may vary for different patients and develop a two-phase heuristic to solve the problem. 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.…”

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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The therapist routing and scheduling problem

Cited by 53 publications

References 25 publications

Chapter 9: Four Variants of the Vehicle Routing Problem

Chapter 9: Four Variants of the Vehicle Routing Problem

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

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

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