A stochastic programming model for outpatient appointment scheduling considering unpunctuality

Jiang, Bowen; Tang, Jiafu; Yan, Chongjun

doi:10.1016/j.omega.2017.12.004

Cited by 42 publications

(20 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have shown that both early and late arrivals disrupt clinic service operations (Alexopoulos et al, 2008; Fetter & Thompson, 1966; Glowacka et al, 2017), increase inefficiencies and delays (Deceuninck et al, 2018; Okotie, Patel, & Gonzalez, 2008), decrease the service quality for punctual patients (Glowacka et al, 2017), and increase clinic operational costs (eg, the provider overtime cost) and implicit costs related to the perceived quality of service (eg, decreased patient satisfaction) and hence the reputability of the clinic (Cayirli & Veral, 2003; Deceuninck et al, 2018; Glowacka et al, 2017; Kocas, 2015; Osuna, 1985). Stochastic arrival times can also lead to adverse operational outcomes and challenging queueing issues (Cayirli & Veral, 2003; Deceuninck et al, 2018; Jiang et al, 2019; Klassen & Yoogalingam, 2014). For example, not serving an early arrival (or waiting for a chronically late patient) can result in service delay of the next on‐time patient (Deceuninck et al, 2018; Glowacka et al, 2017; Samorani & Ganguly, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…First‐come‐first‐serve is another policy which designates that the provider serves the patients according to their actual arriving order, regardless of their initial scheduled order of arrival. This policy often raises the concern that it (partially) conflicts with the goal of appointment scheduling and can encourage the patients to arrive early and beat the appointment system (Cayirli & Veral, 2003; Deceuninck et al, 2018; Jiang et al, 2019). In this paper, we adopt the AO policy to obtain an upper bound on the optimal value of MSMIP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The third approach employs stochastic mixed‐integer programming (SMIP) but, to the best of our knowledge, only involves very few papers (Ahmadi‐Javid et al, 2017). Jiang et al (2019) proposed a stochastic linear program that considers stochastic arrival and adopts the AO policy. Assuming patient homogeneity, they demonstrated how the optimal appointment intervals change under different stochastic arrivals scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another common phenomenon among OPC patients (as well as the customers in many other service industries with an appointment scheduling system) is the lack of punctuality, which introduces additional complexity into clinical operations and increases operational costs (Alexopoulos, Goldsman, Fontanesi, Kopald, & Wilson, 2008; Cayirli, Veral, & Rosen, 2006; Deceuninck, Fiems, & De Vuyst, 2018; Fetter & Thompson, 1966; Glowacka et al, 2017; Jiang, Tang, & Yan, 2019; Klassen & Yoogalingam, 2014; Williams, Chambers, Dada, McLeod, & Ulatowski, 2014). Late arrivals often create provider idling (and thus poor utilization of the clinic resources) and, along with the random service durations, may increase the waiting time of the subsequent appointments and provider overtime through the propagation of the delay, if there is no buffer in the schedule to absorb it.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Shehadeh

Cohn

Jiang

2020

Naval Research Logistics

View full text Add to dashboard Cite

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Shehadeh

Cohn

Jiang

2020

Naval Research Logistics

View full text Add to dashboard Cite

show abstract

“…Burdett et al [1] robustly schedule appointments by strategically inserting buffer times, and combine simulated annealing and evolutionary search to improve scheduleability. Jiang et al [2] found that most studies on appointment systems assume that patients arrive at appointment time, but the actual situation is not the case. Therefore, they proposed a random planning appointment scheduling system considering late patients, using the BD-SAA method to find the best appointment time that reaches the least patient waiting time and doctor idle time.…”

Section: Reservation Schedulingmentioning

confidence: 99%

A Real-time Approach for Dynamic Scheduling Pet Activities in a Hospital

Chiou

Yaxin

Lin

2020

Preprint

View full text Add to dashboard Cite

Background: At present, medical personnel use manual scheduling methods to calculate usage times of PET devices, hospital beds, operating rooms, and other treatment spaces. However, as patient treatment and recovery times are unequal and uncertain, arriving times of the scheduled/temporary patients are unexpected, examination items, beds requirements, and drugs waiting times are different, and scan-again requirements are unexpected. Manual scheduling arrangements are easy to make mistakes and put pressure on employees. With changing scheduling arrangements, it is difficult to announce the estimated time to patients and their families in real time. As a result, family members keep asking medical staff about various situations and waiting time. This makes the process unstable and exhausts the medical staff. Although previous researches proposed algorithms for specific inspections to solve the equipment resource allocation problems and calculate outpatient visit time for outpatient clinics, there is no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with Bluetooth Beacon positioning. Results: This system can automatically schedule, estimate, and instantly update the start and end time of various tasks of PET patients during examinations, and automatically allocate beds and real-time announce schedule information, allowing the schedule being automated, instant and almost optimized. Moreover, this system promptly reminds medical staffs in each area. This can greatly reduce the work of medical staff, avoid human error, improve the safety of drugs, and provide medical staff, patients and family members to view the inspection progress and estimated waiting time in real time, reducing the number of patients and family members to query medical staff. We implemented this system in an application for the Android system to prove the feasibility of the system. We also collected time data of 200 actual patients for 2 weeks in the Department of Nuclear Medicine of Chang Gung Memorial Hospital, Linkou, and put these data into the implementation program for simulation and comparison. It was found that the average time difference between manual and automatic scheduling was 7.32 minutes, and it could reduce the average examination time of 82% of patients by 6.14 minutes, proving the system's correctness and efficiency. To our knowledge, this paper is the first to propose a real-time automatic scheduling and control system for PET patients.

show abstract

A Simulation Model for Outpatient Department Scheduling Window with Patient No-Show

Zhou

Jiang

Wang

et al. 2018

Proceedings of the Twelfth International Conference on Management Science and Engineering Management

View full text Add to dashboard Cite

A stochastic programming model for outpatient appointment scheduling considering unpunctuality

Cited by 42 publications

References 43 publications

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

A Real-time Approach for Dynamic Scheduling Pet Activities in a Hospital

A Simulation Model for Outpatient Department Scheduling Window with Patient No-Show

Contact Info

Product

Resources

About