Multi-Priority Online Scheduling with Cancellations

Templating strategies specify policies on capacity allocation and appointment booking, which are central to patient access management in outpatient specialty practices (SPs). In the widely implemented partitioned templating strategy, appointment capacity is allocated exclusively to each patient group defined by a combination of patient attributes and medical conditions, which often results in utilization challenges for SPs. Motivated by problems faced by an SP within a large academic medical center, we propose partially partitioned templating strategies that cluster patient groups into access classes and allocate appointment slots to these classes. We formulate a twostage stochastic optimization model to simultaneously optimize decisions on patient group clustering and capacity allocation in the templating strategy design. We develop an efficient solution algorithm for the problem, which otherwise poses a combinatorial challenge, based on Benders' cuts and an anticipatory approximation. Numerical experiments using real-world data show that the partially partitioned strategies outperform benchmark strategies used by SPs through maintaining a balance of high capacity utilization and providing timely access for priority patients. In addition, we study a wide range of SP setups for transferable insights on templating strategies based on practice specifics.

Section: Appointment Schedulingmentioning

confidence: 99%

Partially partitioned templating strategies for outpatient specialty practices

Bai

Berg

Sir

et al. 2023

“…In a setting where arriving customers can consume multiple units of a particular resource, and where the goal is simply to maximize the number of units consumed, Stein et al (2020) develop a 0.321-approximation scheme. Wang et al (2018) consider an online matching setting that is almost identical to ours, and provide an approach that is…”

Section: Online Matchingmentioning

confidence: 99%

Approximation schemes for the joint inventory selection and online resource allocation problem

Chen

Feldman

Kouvelis

2022

In this paper, we introduce and study the joint inventory selection and online resource allocation problem, which is characterized by two sequential sets of decisions that are irrevocably linked. First, a decision maker (DM) must select starting inventory levels for a set of available resources. Subsequently, the DM must match arriving customers to available resources in an online fashion so as to maximize expected reward. We first study the problem in its most general form, before focusing on a specific version that arises at Anheuser Busch InBev (ABI). This particular application of our general setting is referred to as the ABI Trailer Problem, and it considers how ABI ships its beer to vendors via third‐party delivery trucks. In this problem, ABI must select the weights of preloaded trailers of beer, which are then matched in an online fashion to the arriving third‐party delivery trucks. For the general setting, we develop simple and easy‐to‐implement approaches that come with robust worst‐case performance guarantees. For the ABI setting, we reveal a simplifying structural property related to the optimal matching policy, which gives rise to a natural adaptation of our original approach. We test the efficacy of these policies through extensive numerical experiments, where we find that our approaches are either near‐optimal or improve upon state‐of‐the‐art benchmarks. In particular, using a data set from ABI, we are able to generate instances of the ABI Trailer Problem, on which our algorithm has the potential to yield revenue improvements in the range of millions of dollars per year.

“…To evaluate the performance of an online algorithm L , we adapt CR as our performance criterion. CR is a widely used performance metric for online algorithms with various applications, including the scheduling problems (Stein et al., 2020; Wang & Truong, 2018), order fulfillment problems (Jasin & Sinha, 2015; Zhao et al., 2020), and revenue management problems (Ma et al., 2021a; Ma et al., 2021b).…”

Section: Modelmentioning

confidence: 99%

A nonasymptotic analysis for re‐solving heuristic in online matching

Wang

Yan

Bei

2022

We investigate an online edge‐weighted bipartite matching problem with general capacity constraints. In this problem, the resources are offline and nonreplenishable with different capacities. Demands arrive online and each requests a certain amount of resources. The goal is to maximize the reward generated by successful matches. We model the offline optimization problem as a deterministic linear program (LP) and present multiple randomized online algorithms based on the solution to the offline LP. We analyze the performance guarantee of each algorithm in terms of its competitive ratio (CR). Importantly, we introduce a re‐solving heuristic that periodically recomputes the offline LP and uses the updated offline solution to guide the online algorithm decisions. We find that the algorithm's CR can be significantly improved when re‐solving at carefully selected time steps. Finally, we investigate the value of the demand distribution in further improving the algorithm efficiency. We conduct extensive numerical studies to demonstrate the efficiency of the proposed algorithms. The effect of market conditions on the algorithm performance is also investigated.