Hospital resident scheduling problem

This paper addresses the resident scheduling problem (RSP) at hospitals concerned with prescribing work-nights for residents while considering departmental staffing and skill requirements as well as residents' preferences. Three scenarios that represent most situations and account for various departmental requirements and needs are described. Although similar scheduling problems are considered in the literature, no analysis exists that adequately deals with the speciffic nature of this problem. The problem is modeled as a mixed-integer program and heuristic solution procedures are developed for the different identified scheduling scenarios. These procedures exploit the inherent network structure of the problem which is an important feature that enhances problem solvability. For the sake of comparison, the problem is also solved exactly via the CPLEX-MIP (version 6.0) package. The contribution of this work is important since many hospitals are still utilizing manual techniques in preparing their own schedules, expending considerable effort and time and yet contending with limited scheduling flexibility.     

A closed loop automatic scheduling system (CLASS)

Abstract

CLASS is a production scheduling system, that is designed to function in either a stand-alone manner, or in conjunction with an MRP system. MRP systems innately do not have 'closed loop’ capability in the sense of being able to produce master schedules and order releases that are consistent and that respect capacity constraints. True closed loop performance requires detailed scheduling. In addition to interfacing with MRP systems, CLASS is designed to produce schedules that can be used in conventional shops or can be downloaded to automated facilities. The design goals for the system, its internal architecture, and its role in manufacturing control systems are described. The modelling and decision capabilities     

TAPAS: A modular framework to support reuse in scheduling software development

Industrial schedulers try to take into account more and more complex aspects of manufacturing and become, as a consequence, more difficult to use efficiently. We suggest a modular framework for scheduling in order to support the development of schedulers better adapted to the workshop they are used in. An extensive reuse of past developments is made possible by the definition of a powerful, yet generic, data model, together with a fully modular architecture and the use of standards, e.g. CORBA, MMS and STEP. Examples of the first schedulers developed within this framework in an industrial context are shown.     

Decision analysis for job shop scheduling

Following a brief account of its principal components, the framework of statistical decision theory is shown to be applicable to selecting schedules by a heuristic procedure for the general J × M job shop problem. Sequential Bayesian strategies and explicit forms of stopping rules are obtained for the search procedure, together with bounds on required sample size.     

Appointment Scheduling with No‐Shows and Overbooking

We study an overbooking model for scheduling arrivals at a medical facility under no‐show behavior, with patients having different no‐show probabilities and different weights. The scheduler has to assign the patients to time slots in such a way that she minimizes the expected weighted sum of the patients' waiting times and the doctor's idle time and overtime. We first consider the static problem, where the set of patients to be scheduled and their characteristics are known in advance. We partially characterize the optimal schedule and introduce a new sequencing rule that schedules patients according to a single index that is a function of their characteristics. Then we apply our theoretical results and conclusions from numerical experiments to sequential scheduling procedures. We propose a heuristic solution to the sequential scheduling problem, where requests for appointments come in gradually over time and the scheduler has to assign each patient to one of the remaining slots that are available in the schedule for a given day. We find that the no‐show rate and patients' heterogeneity have a significant impact on the optimal schedule and should be taken under consideration.     

Selling Finite Capacity in Bulks*

We consider a firm that owns a limited capacity for the delivery of services or for the production of customized products. Potential buyers specify the amount of capacity they will require for the execution of their intended services, goods or projects. Based on the size of the requirement, the firm makes a bid while being challenged in various ways: (1) it only knows the underlying probability function from which its customers’ reservation prices are drawn, (2) arrival of additional future requests is stochastic, and, (3) the firm knows in advance neither the magnitude of these potential requests nor the buyer's reservation price. The firm aims to maximize its expected profit by choosing its pricing mechanism. The fact that capacity is demanded in varying amounts distinguishes this problem from most available literature in which standard sizes are sold or partial fulfillment and displacement are permitted. Lacking such allowances presents a new challenge to the firm as in conjunction with pricing it should also address the issue of various sizes requests’ compatibility to achieve optimal utilization of its capacity in order to maximize expected profit. In this article, we consider two approaches of handling this problem: myopic and foresighted. We formulate and analyze the problem to obtain the firm's optimal bidding decisions as well as managerial insight about the optimal bid level and its important role in coordinating buyers’ requests. Furthermore, due to this role, pricing patterns in this environment are different than those in standard unit sales.     

A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority

In this paper, a multi-objective vehicle routing and scheduling problem with uncertainty in priority and request of customers is presented. In the proposed model, a set of dynamic requests is received over time, and the planner does not have any information regarding their location and size until they arrive. Moreover, the routing model aims to satisfy different customers according to their specific time windows which were predefined by an expert as (being very important, important, casual or unimportant). This paper uses the proposed model as a multi-objective problem where the total required number of vehicles, the total distance travelled and the waiting time imposed on vehicles are minimized, and the total customers’ satisfaction for service is maximized. An efficient framework for solving this model is designed and its performance is evaluated in different steps for various test problems generalized from Solomon’s VRPTW benchmark problems. The various heuristics and improvement concepts incorporate local exploitation in the evolutionary search, and the concept of Pareto optimality for the multi-objective optimization is used in the proposed procedure. The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.     

A quantitative field study of the decision behaviour of four shopfloor schedulers

In this article the decision behaviour of four production schedulers in a truck manufacturing company is investigated by means of a quantitative model. The model consists of there parts: performance variables, action variables and disturbance variables. The outcomes show that there is a large difference between schedulers that apparently have the same type of decision problem. Another interesting finding is that some scheduling actions work positively in the short term, but negatively over a longer term. Other results, along with methodological issues of quantitative research, are discussed.     

On Optimal Rules of Persuasion

A speaker wishes to persuade a listener to accept a certain request. The conditions under which the request is justified, from the listener's point of view, depend on the values of two aspects. The values of the aspects are known only to the speaker and the listener can check the value of at most one. A mechanism specifies a set of messages that the speaker can send and a rule that determines the listener's response, namely, which aspect he checks and whether he accepts or rejects the speaker's request. We study mechanisms that maximize the probability that the listener accepts the request when it is justified and rejects the request when it is unjustified, given that the speaker maximizes the probability that his request is accepted. We show that a simple optimal mechanism exists and can be found by solving a linear programming problem in which the set of constraints is derived from what we call the L‐principle.     

Optimal Choice for Appointment Scheduling Window under Patient No‐Show Behavior

Observing that patients with longer appointment delays tend to have higher no‐show rates, many providers place a limit on how far into the future that an appointment can be scheduled. This article studies how the choice of appointment scheduling window affects a provider's operational efficiency. We use a single server queue to model the registered appointments in a provider's work schedule, and the capacity of the queue serves as a proxy of the size of the appointment window. The provider chooses a common appointment window for all patients to maximize her long‐run average net reward, which depends on the rewards collected from patients served and the “penalty” paid for those who cannot be scheduled. Using a stylized M/M/1/K queueing model, we provide an analytical characterization for the optimal appointment queue capacity K, and study how it should be adjusted in response to changes in other model parameters. In particular, we find that simply increasing appointment window could be counterproductive when patients become more likely to show up. Patient sensitivity to incremental delays, rather than the magnitudes of no‐show probabilities, plays a more important role in determining the optimal appointment window. Via extensive numerical experiments, we confirm that our analytical results obtained under the M/M/1/K model continue to hold in more realistic settings. Our numerical study also reveals substantial efficiency gains resulted from adopting an optimal appointment scheduling window when the provider has no other operational levers available to deal with patient no‐shows. However, when the provider can adjust panel size and overbooking level, limiting the appointment window serves more as a substitute strategy, rather than a complement.     

Approximations to Optimal k‐Unit Cycles for Single‐Gripper and Dual‐Gripper Robotic Cells

We consider the problem of scheduling operations in bufferless robotic cells that produce identical parts using either single‐gripper or dual‐gripper robots. The objective is to find a cyclic sequence of robot moves that minimizes the long‐run average time to produce a part or, equivalently, maximizes the throughput. Obtaining an efficient algorithm for an optimum k‐unit cyclic solution (k ≥ 1) has been a longstanding open problem. For both single‐gripper and dual‐gripper cells, the approximation algorithms in this paper provide the best‐known performance guarantees (obtainable in polynomial time) for an optimal cyclic solution. We provide two algorithms that have a running time linear in the number of machines: for single‐gripper cells (respectively, dual‐gripper cells), the performance guarantee is 9/7 (respectively, 3/2). The domain considered is free‐pickup cells with constant intermachine travel time. Our structural analysis is an important step toward resolving the complexity status of finding an optimal cyclic solution in either a single‐gripper or a dual‐gripper cell. We also identify optimal cyclic solutions for a variety of special cases. Our analysis provides production managers valuable insights into the schedules that maximize productivity for both single‐gripper and dual‐gripper cells for any combination of processing requirements and physical parameters.     

Simulation-based production schedule generation

Abstract

While simulation has been used in manufacturing for many years, predominantly for facility design, it is within the last few years, that simulation languages have been developed to a point where they can be used on a day-to-day basis to generate schedules and predict their performance. This paper describes our use of different modelling techniques to develop a production schedule generation system. An example describing a system for a small- to medium-sized order producing company using SIMAN/CINEMA is included. While addressing the shortcomings of existing scheduling systems it is shown how the approach taken is a feasible way of creating a dynamic goal-driven simulation-based production scheduler. The paper does not aim to describe an ‘off the shelf’ scheduling system product, but rather to give an overview to methods, techniques and experiences which enable us rapidly to tailor a simulation-based scheduling system to the specific needs of a company.     

The use of demand information in selecting production plans and schedules

The strategic problem of selecting a production plan for a given planning horizon is usually treated as independent of the tactical problem of scheduling the production plan. This paper approaches both selecting the production plan and scheduling it as one problem. The problem is formulated as a zero-one integer program. The formulation accommodates many real-life considerations. The integer program is solved using a branch and bound procedure which provides the optimal production plan and schedule as well as the importance indices of the orders, a concept which is introduced and used in this study to rank the available orders within the planning horizon according to their importance to the firm. The integer program and the search procedure can be used as a decision supporting tool to respond to any changes in the demand information, capacity of the firm, or its operating strategy, and it guarantees the selection of feasible production plan(s) and optimal schedules.     

Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance

The problem of no‐shows (patients who do not arrive for scheduled appointments) is particularly significant for health care clinics, with reported no‐show rates varying widely from 3% to 80%. No‐shows reduce revenues and provider productivity, increase costs, and limit patient access by reducing effective clinic capacity. In this article, we construct a flexible appointment scheduling model to mitigate the detrimental effects of patient no‐shows, and develop a fast and effective solution procedure that constructs near‐optimal overbooked appointment schedules that balance the benefits of serving additional patients with the potential costs of patient waiting and clinic overtime. Computational results demonstrate the efficacy of our model and solution procedure, and connect our work to prior research in health care appointment scheduling.     

非对称信息条件下成本控制模型研究

运用委托代理理论方法探讨了信息不对称条件下,生产管理者如何激励生产者努力降低成本的问题。信息不对称情况下,管理者不能观测到生产者为降低成本而付出的努力,因此管理者需要对生产者进行激励。管理者根据自己的要求和生产者的自报数确定了一个合约基数,并建立了满足生产者个体理性约束和激励相容约束条件下的管理者期望效用最大化模型,分别以奖励系数和惩罚系数为决策主量。运用进化规划算法作了仿真计算。     

Demanding work schedules and mental health in nursing assistants working in nursing homes

Nursing home assistants have physically and emotionally challenging jobs, and they often work demanding schedules in order to provide 24-h care. While the physical effects of demanding work schedules have been studied, little is known about the impact on mental health. This study explored the relationship between demanding scheduling variables and mental health indicators of depression, anxiety and somatization. A cross-section of 473 US female nursing assistants working in nursing homes was surveyed. Work schedule characteristics included shiftwork, hours per day and week, days per week, number of weekends per month, number of double shifts per month, breaks, and number of jobs worked. Working two or more double-shifts per month was associated with increased risk for all mental health indicators, and working 6-7 days per week was associated with depression and somatization. There was a trend for increasing odds of adverse mental health with increased numbers of demanding work schedule factors. The odds of depression was increased four-fold when working 50+ h/week, more than two weekends/month and more than two double shifts/month. Providing work schedules that are less unhealthy may have implications for both worker retention and the quality of care delivered to nursing home residents.     

Demanding work schedules and mental health in nursing assistants working in nursing homes

Nursing home assistants have physically and emotionally challenging jobs, and they often work demanding schedules in order to provide 24-h care. While the physical effects of demanding work schedules have been studied, little is known about the impact on mental health. This study explored the relationship between demanding scheduling variables and mental health indicators of depression, anxiety and somatization. A cross-section of 473 US female nursing assistants working in nursing homes was surveyed. Work schedule characteristics included shiftwork, hours per day and week, days per week, number of weekends per month, number of double shifts per month, breaks, and number of jobs worked. Working two or more double-shifts per month was associated with increased risk for all mental health indicators, and working 6–7 days per week was associated with depression and somatization. There was a trend for increasing odds of adverse mental health with increased numbers of demanding work schedule factors. The odds of depression was increased four-fold when working 50+ h/week, more than two weekends/month and more than two double shifts/month. Providing work schedules that are less unhealthy may have implications for both worker retention and the quality of care delivered to nursing home residents.     

DYNAMIC COURSE SCHEDULING FOR COLLEGE FACULTY VIA ZERO-ONE PROGRAMMING*

This paper formulates a multi-period course scheduling problem as a zero-one programming model. Under various constraints, and for a planning horizon of several terms, the model seeks to maximize: (1) the faculty course preferences in assigning faculty members to courses, and (2) the faculty time preferences in allocating courses to time blocks, via a two-stage optimization procedure. The multi-period structure of the model, strengthened by the explicit inclusion of a wide-range of constraints designed to represent various special requirements has enabled the model to capture the many dynamic features of the course scheduling problem at the college level. As such, it can be used not only for long-range or short-range departmental planning, but also as a suitable framework toward the development of a larger, and all-inclusive course scheduling decision system. The paper begins with a brief review of several related studies and then presents a multi-period scheduling model and its extensions. Some numerical examples are used to test the model, and the authors' experience, resulting from such tests with several computer codes, is reported. The possible directions for future research are also suggested.