Priorities of the Nurse Schedule by using MODM Approach: A case Study

El efecto bienestar de una migración internacional es habitualmente calculado como la variación del ingreso per capita de quienes quedan atrás luego de la migración. En este trabajo se presenta una crítica de dicho criterio que toma en cuenta que el efecto bienestar es opuesto para asalariados y capitalistas en el caso en que la migración modifique la relación K/L de la economía. Se propone un criterio alternativo que descubra de manera adecuada los efectos que la migración tiene para cada uno de los grupos mencionados.The welfare effect of an international migration is usually calculated as the per capita income variation of those left behind after the migration. A critique of this criterion and a proposal of an alternative one is presented in this paper, considering the fact that in the case in which the overall K/L ratio changes, the welfare effect of wage earners is the opposite of the welfare effect of capital owners.Instituto de Investigaciones Económica

A time predefined variable depth search for nurse rostering

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive

Revisión de literatura sobre los modelos de optimización en programación de turnos de enfermería

Siendo la programación de turnos de enfermería (NSP) un componente esencial en la calidad del servicio de salud y debido al gran número de investigaciones desarrolladas sobre NSP en la literatura, se desarrolla una revisión de literatura sobre los artículos sobre NSP realizados desde 2003 hasta la fecha. A partir de este trabajo se logran identificar la tendencia y las necesidades propias de este problema, las cuales se caracterizan por (1) la necesidad de cerrar la brecha entre academia y práctica mediante el desarrollo de modelos objetivos de representación del problema y (2), desarrollar investigación sobre técnicas de solución capaces de tratar modelos de gran complejidad, sin sacrificar el recurso computacional. Este artículo presenta una revisión de literatura sobre los modelos de optimización en la programación de turnos de enfermería, publicados desde 2003 a la fecha.B Being the nurse shift scheduling an essential component of the quality of the health service and due to the big amount of research conducted regarding the Nurse Scheduling Problem (NSP), a literature review is carried out concerning articles on NSP published from 2003 up to now. As a result of this work, we were able to highlight the tendencies and own needs of this problem, which are characterized by: (1) the need to close the gap between academy and practice through the development of objective models that represent the problem and (2) research about solution techniques capable of processing models of great complexity, without sacrificing the computational resource. This article presents a literature review on optimization models in the NSP published since 2003

Strategic Nurse Allocation Policies Under Dynamic Patient Demand

ABSTRACT STRATEGIC NURSE ALLOCATION POLICIES UNDER DYNAMIC PATIENT DEMAND by Osman T. Aydas The University of Wisconsin-Milwaukee, 2017 Under the Supervision of Professor Anthony D. Ross Several studies have shown a strong association between nurse staffing and patient outcomes. When a nursing unit is chronically short-staffed, nurses must maintain an intense pace to ensure that patients receive timely care. Over time this can result in nurse burnout, as well as dissatisfied patients and even medical errors. Improved accuracy in the allocation of nursing staff can mitigate these operational risks and improve patient outcomes. Nursing care is identified as the single biggest factor in both the cost of hospital care and patient satisfaction. Yet, there is widespread dissatisfaction with the current methods of determining nurse staffing levels, including the most common one of using minimum nurse-to-patient ratios. Nurse shortage implications go beyond healthcare quality, extending to health economics as well. In addition, implementation of mandatory nurse-to-patient ratios in some states creates a risk of under- or over-estimating required nurse resources. With this motivation, this dissertation aims to develop methodologies that generate feasible six-week nurse schedules and efficiently assign nurses from various profiles to these schedules while controlling staffing costs and understaffing ratios in the medical unit. First, we develop and test various medium-term staff allocation approaches using mixed-integer optimization and compare their performance with respect to a hypothetical full information scenario. Second, using stochastic integer programming approach, we develop a short-term staffing level adjustment model under a sizable list of patient admission scenarios. We begin by providing an overview of the organization of the dissertation. Chapter 1 presents the problem context and we provide research questions for this dissertation. Chapter 2 provides a review of the literature on nurse staffing and scheduling specifically from the Operations Management journals. We introduce the challenges of nursing care and nurse scheduling practices. We identify major research areas and solution approaches. This is followed by a discussion of the complexities associated with computing nursing requirements and creating rosters. Staffing requirements are the result of a complex interaction between care-unit sizes, nurse-to-patient ratios, bed census distributions, and quality-of-care requirements. Therefore, we review the literature on nursing workload measurement approaches because workloads depend highly on patient arrivals and lengths of stay, both of which can vary greatly. Thus, predicting these workloads and staffing nurses accordingly are essential to guaranteeing quality of care in a cost-effective manner. For completeness, a brief review of the literature on workforce planning and scheduling that is linked to the nurse staffing and scheduling problem is also provided. Chapter 3 develops a framework for estimating the daily number of nurses required in Intensive Care Units (ICUs). Many patient care units, including ICUs, find it difficult to accurately estimate the number of nurses needed. One factor contributing to this difficulty is not having a decision support tool to understand the distribution of admissions to healthcare facilities. We statistically evaluate the existing staff allocation system of an ICU using clinical operational data, then develop a predictive model for estimating the number of admissions to the unit. We analyze clinical operational data covering 44 months for three wards of a pediatric ICU. The existing staff allocation model does not accurately estimate the required number of nurses required. This is due in part to not understanding the pattern and frequency of admissions, particularly those which are not known 12 hours in advance. We show that these “unknown” admissions actually follow a Poisson distribution. Thus, we can more accurately estimate the number of admissions overall. Analytical predictive methods that complement intuition and experience can help to decrease unplanned requirements for nurses and recommend more efficient nurse allocations. The model developed here can be inferred to estimate admissions for other intensive care units, such as pediatric facilities. Chapter 4 examines an integrated nurse staffing and scheduling model for a Pediatric Intensive Care Unit (PICU). This model is targeted to recommend initial staffing plans and schedules for a six-week horizon given a variety of nurse groups and nursing shift assignment types in the PICU. Nurse rostering is an NP-hard combinatorial problem, which makes it extremely difficult to efficiently solve life-sized problems due to their complexity. Usually, real problem instances have complicated work rules related to safety and quality of service issues, as well as preferences of the personnel. To avoid the size and complexity limitations, we generate feasible nurse schedules for the full-time equivalent (FTE) nurses, using algorithms that will be employed in the mixed-integer programming models we develop. Pre-generated schedules eliminate the increased number of constraints, and reduce the number of decision variables of the integrated nurse staffing and scheduling model. We also include a novel methodology for estimating nurse workloads by considering the patient, and individual patient’s acuity, and activity in the unit. When the nursing administration prepares the medium-term nurse schedules for the next staffing cycle (six weeks in our study), one to two months before the actual patient demand realizations, it typically uses a general average staffing level for the nursing care needs in the medical units. Using our mixed-integer optimization model, we examine fixed vs. dynamic medium-term nurse staffing and scheduling policy options for the medical units. In the fixed staffing option, the medical unit is staffed by a fixed number of nurses throughout the staffing horizon. In the dynamic staffing policy, we propose, historical patient demand data enables us to suggest a non-stationary staffing scheme. We compare the performance of both nurse allocation policy options, in terms of cost savings and understaffing ratios, with the optimal staffing scheme reached by the actual patient data. As a part of our experimental design, we evaluate our optimization model for the three medical units of the PICU in the “as-is” state. In Chapter 5, we conduct two-stage short-term staffing adjustments for the upcoming nursing shift. Our proposed adjustments are first used at the beginning of each nursing shift for the upcoming 4-hour shift. Then, after observing actual patient demand for nursing at the start of the next shift, we make our final staffing adjustments to meet the patient demand for nursing. We model six different adjustment options for the two-stage stochastic programming model (five options available as first-stage decisions and one option available as the second-stage decision). Because the adjustment horizon is less than 12 hours, the current patient census, patient acuity, and the number of scheduled admissions/discharges in the current and upcoming shift are known to the unit nurse manager. We develop a two-stage stochastic integer programming model which will minimize total nurse staffing costs (and the cost of adjustments to the original schedules developed in the medium-term planning phase) while ensuring adequate coverage of nursing demand. Chapter 6 provides conclusions from the study and identify both limitations and future research directions

Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions

Decentralized and Dynamic Home Health Care Resource Scheduling Using an Agent-Based Model

The purpose of this thesis is to design an agent-based scheduling system, simulated in a dynamic environment that will reduce home healthcare service costs. The study focuses on situations where a health care agency needs to assign home visits among a group of independent healthcare practitioners. Each practitioner has different skill sets, time constraints, and cost structures, given the nature, time and location of each home visit. Each expects reasonable payment commensurate with their skill levels as well as the costs incurred. The healthcare agency in turn needs all planned visits performed by qualified practitioners while minimizing overall service costs. Decisions about scheduling are made both before and during the scheduling period, requiring the health care agency to respond to unexpected situations based on the latest scheduling information. This problem is examined in a multi-agent system environment where practitioners are modeled as self-interested agents. The study first analyzes the problem for insights into the combinatorial nature of such a problem occurring in a centralized environment, then discusses the decentralized and dynamic challenges. An iterated bidding mechanism is designed as the negotiation protocol for the system. The effectiveness of this system is evaluated through a computational study, with results showing the proposed multi-agent scheduling system is able to compute high quality schedules in the decentralized home healthcare environment. Following this, the system is also implemented in a simulation model that can accommodate unexpected situations. We presents different simulation scenarios which illustrate the process of how the system dynamically schedules incoming visits, and cost reduction can be observed from the results

An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation

Binary tournament (BT) selection is known as an established selection operator that has been employed in various problems. However, in the development of evolutionary algorithms (EA), this selection operator has a drawback in providing an efficient implementation of the union procedure, which cannot guarantee a parsimonious knowledge base with reduced number of rules. Therefore, this paper introduces binary-standard deviation (SD) tournament selection into EA as an enhancement of BT that can lead to focus on more exploration in terms of searching for the best solutions. *e proposed selection operator has been experimented within fish feed formulation in grouper fish farming as a case study on finding the minimum cost and fulfilling constraints. *is approach is better than experimental design in terms of costs and time. *e motivation for doing so is to search for alternative ingredients for the grouper fish, as the price of trash fish is too luxurious. It is because grouper fish are carnivorous and need many trash fish for better growth. *e novelty of the proposed SD tournament selection is compared with BT selection in terms of searching for an efficient but not myopic algorithm. Hence, based on the comparative study, the findings of the enhanced selection operator towards the EA have been convinced and accepted in terms of better cost and fulfiiling constraints requirement