Simple Scheduling Policy Research Articles

Timing of mastectomy and the effect on the likelihood of outpatient surgery and cost savings in breast cancer patients

BackgroundSame-day discharge after mastectomy has potential patient- and hospital-level benefits; however, few data are available regarding factors affecting the likelihood of same-day discharge in order to address barriers. We sought to evaluate factors contributing to same-day discharge, focusing on the timing of mastectomy during the operative day. MethodsWe conducted a single-institution retrospective review of patients who underwent mastectomies for malignancy over a 3-y time frame. Clinicopathologic variables were collected along with a binary variable for mastectomy start time (morning versus afternoon). Our primary endpoint was rate of same-day discharge. A multivariable logistic regression model was constructed from significant univariate variables to determine independent predictors of same-day discharge. A secondary endpoint was a cost-utility analysis for morning versus afternoon start time, using hospital cost data. ResultsThere were 451 patients included in the analysis. Factors associated with same-day discharge rate included the American Society of Anesthesiologists score, use of a preoperative regional anesthesia block, type of mastectomy performed, individual surgeon variation, and a morning start for the mastectomy. On multivariable analysis, morning start was a strong independent predictor of same-day discharge (odd ratio = 2.83; 95% CI, 1.75–4.60). The cost-utility analysis favored a morning start, with average cost savings of $550 per patient. ConclusionDespite patient- and surgeon-specific variations, simple scheduling policies can improve same-day discharge rates after mastectomy, leading to improved hospital bed use and cost reduction.

Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections

More and more vehicles are connected today via emerging connected and automated vehicle (CAV) technologies. An intriguing application of CAVs is to cross intersections without stops through cooperative scheduling by traffic control infrastructure. Nonetheless, with the increase of CAVs’ requests for green, two problems will surface: (I) accommodating too many CAVs’ green requests will generate severe interruptions to general traffic; (II) simple scheduling policies like first-come-first serve is inappropriate due to heterogeneous importance of CAVs. To overcome these challenges, we present a mixed-integer linear programming (MILP) formulation for congestion-aware heterogeneous CAV scheduling problems at intersections in this paper. The objective is to ensure that intensive and heterogeneous green requests by CAVs can be scheduled at intersections while the mobility of background traffic is still maintained. The MILP formulation is developed in the context of discrete space-time and phase-time networks whose variables are space-time arc choice variables with respect to individual vehicles and phase-time arc choice variables. We also build an efficient search algorithm based on the “A-D curves” for real-time applications. Three experiments are conducted to validate the proposed MILP formulation and search algorithm. The simulation-based performance evaluation for the congestion-aware CAV scheduling reveal promising results for real-world applications in the future.

Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis

We consider the classical appointment scheduling problem that does not have a session length constraint. The decision is to determine job allowances to minimize total expected weighted costs of waiting times of jobs and idle times of the service provider. In particular, we study a simple yet effective scheduling policy—the so‐called “constant" policy, which allocates a constant job allowance for each appointment. Prior studies on appointment scheduling suggest a “dome”‐shaped structure for the optimal job allowance over the planning horizon. This implies that job allowance does not vary significantly in the middle of the schedule sequence, but varies at the beginning as well as the end of the optimal schedule. We show that an even simple scheduling policy—the constant policy, is asymptotically optimal thus provide theoretical justification for such a widely used policy. Using a dynamic programming formulation, we express each job's waiting time as the maximum of a random walk, which allows us to bound the performance gap between the constant policy and the optimal schedule based on classical results on D/ G/1 queues. We derive an explicit upper bound for the performance gap when either of the following conditions holds: (i) the server idling cost is relatively small compared to the job waiting cost, and (ii) the number of appointments is sufficiently large. We also extend this result to a more general setting with multiple service types. Numerical experiments show that the constant policy is near optimal even when the number of appointments is small or when the server idling cost is moderately large, which complements our theoretical results. Our result provides a justification and strong support for the constant policy under certain mild conditions. Moreover, with minor modifications, the constant policy can be adapted to more general scenarios with patient no‐shows, or with heterogeneous appointment types.

Solution of the Neutron Transport Equation on Unstructured Grids Using the Parallel Block Jacobi-Integral Transport Matrix Method via the Novel Green’s Function ITMM Construction Algorithm on Massively Parallel Computer Systems

The Parallel Block Jacobi (PBJ) spatial domain decomposition is well suited for implementation on massively parallel computers to solve the neutron transport equation on unstructured grids due to the simple scheduling policy that arises from the PBJ’s iterative asynchronicity. The Parallel Block Jacobi-Integral Transport Matrix Method (PBJ-ITMM) is an iterative method that utilizes the PBJ decomposition and resolves local within-group scattering in a single iteration, but requires a matrix-vector iterative solution. This work details the development, implementation, and testing of the novel Green’s Function ITMM Construction (GFIC) algorithm. The GFIC constructs the matrices required for the PBJ-ITMM’s iterative solution on unstructured grids, utilizing the physical interpretation of these matrices as discretized response functions to create a local problem with a Green’s Function–like source. Conducting a set of mesh sweeps over all angles on this local problem yields the ITMM matrix elements. On unstructured grids, this approach utilizes the kernel calculation and fundamental solution algorithm present in an existing transport code, thus avoiding reimplementation of code functionality. Using the GFIC, the PBJ-ITMM is implemented in THOR, a tetrahedral mesh transport code, along with the Inexact Parallel Block Jacobi (IPBJ) method for performance comparison. This comparison involves strong and weak scaling studies of the Godiva and C5G7 benchmark problems using up to 32 768 processors. These studies establish that the PBJ-ITMM executes faster than the IPBJ when the number of cells per subdomain falls below a problem-dependent threshold, ~128 cells for Godiva, >256 cells for C5G7. The largest problem tested, comprising more than 6.8 billion unknowns, solves in <30 min with the IPBJ and <20 min with the PBJ-ITMM, using 32 768 processors. These results demonstrate the PBJ-ITMM as a viable approach for solving neutron transport problems on unstructured grids using massively parallel computers. Additionally, this study illustrates the range of number of cells per subdomain over which this method is favorable.

Data-driven analytics to support scheduling of multi-priority multi-class patients with wait time targets

Magnetic Resonance Image (MRI) uses powerful magnetic forces and radio frequencies to create detailed images of the organs and tissues within the body. In this paper, we first conduct descriptive analytics on MRI data of over 3.7 million patient records and determine the main factors affecting the waiting time and conduct predictive analytics to forecast the daily arrivals and the number of procedures performed at each hospital. It is the hospital’s goal to serve 90% of patients within their wait time targets. Therefore, we prescribe two simple scheduling policies based on a balance between the FIFO (First-In First-Out) and strict priority policies; namely, weight accumulation and priority promotion to improve the wait time management. Under the weight accumulation policy, patients from different priority levels start with varying initial weights, which then accumulates as a linear function of their waiting time. Under the priority promotion policy, a strict priority policy is applied to priority levels where patients are promoted to a higher priority level after waiting for a predetermined threshold of time. We evaluate the proposed policies against two performance measures: total exceeding time (the number of days by which patients exceed their wait target), and overflow proportion (the percentage of patients that exceed the wait target). To investigate the value of information, we schedule patients at different points of time from their day of arrival. The results show that hospitals can enhance their wait time management by delaying patient scheduling. We demonstrate that effective scheduling policies may result in significant reduction in patient waiting time without any costly capacity expansion.

On the Stability of Isolated and Interconnected Input-Queueing Switches Under Multiclass Traffic

In this correspondence, we discuss the stability of scheduling algorithms for input-queueing (IQ) and combined input/output queueing (CIOQ) packet switches. First, we show that a wide class of IQ schedulers operating on multiple traffic classes can achieve 100% throughput. Then, we address the problem of the maximum throughput achievable in a network of interconnected IQ switches and CIOQ switches loaded by multiclass traffic, and we devise some simple scheduling policies that guarantee 100% throughput. Both the Lyapunov function methodology and the fluid modeling approach are used to obtain our results.

Optimal parallel processing of random task graphs

We consider scheduling of tasks of parallel programs on multiprocessor systems where tasks have precedence relations and synchronization points. The task graph structures are random variables in the sense that successors to a task do not become known until the task is executed. Thus, as is often the case with parallel processing, scheduling must occur at run time rather than compile time. We prove that a simple scheduling policy stochastically minimizes the processes of task completions and job (i.e. set of parallel tasks) completions for several classes of task graph structures. Copyright © 2001 John Wiley & Sons, Ltd.

Resource management in Legion

The recent development of gigabit networking technology, combined with the proliferation of low-cost, high-performance microprocessors, has given rise to metacomputing environments. These environments can combine many thousands of hosts, from hundreds of administrative domains, connected by transnational and world-wide networks. Managing the resources in such a system is a complex task, but is necessary to efficiently and economically execute user programs. In this paper, we describe the resource management portions of the Legion metacomputing system, including the basic model and its implementation. These mechanisms are flexible both in their support for system-level resource management but also in their adaptability for user-level scheduling policies. We show this by implementing a simple scheduling policy and demonstrating how it can be adapted to more complex algorithms.

Heavy traffic resource pooling in parallel-server systems

We consider a queueing system with r non-identical servers working in parallel, exogenous arrivals into m different job classes, and linear holding costs for each class. Each arrival requires a single service, which may be provided by any of several different servers in our general formulations the service time distribution depends on both the job class being processed and the server selected. The system manager seeks to minimize holding costs by dynamically scheduling waiting jobs onto available servers. A linear program involving only first-moment data (average arrival rates and mean service times) is used to define heavy traffic for a system of this form, and also to articulate a condition of overlapping server capabilities which leads to resource pooling in the heavy traffic limit. Assuming that the latter condition holds, we rescale time and state space in standard fashion, then identify a Brownian control problem that is the formal heavy traffic limit of our rescaled scheduling problem. Because of the assumed overlap in server capabilities, the limiting Brownian control problem is effectively one-dimensional, and it admits a pathwise optimal solution. That is, in the limiting Brownian control problem the multiple servers of our original model merge to form a single pool of service capacity, and there exists a dynamic control policy which minimizes cumulative cost incurred up to any time t with probability one. Interpreted in our original problem context, the Brownian solution suggests the following: virtually all backlogged work should be held in one particular job class, and all servers can and should be productively employed except when the total backlog is small. It is conjectured that such ideal system behavior can be approached using a family of relatively simple scheduling policies related to the c\mu rule.