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1
Heasley, McKay N. "Dynamic Appointment Scheduling in Healthcare." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3176.
Повний текст джерелаАнотація:
In recent years, healthcare management has become fertile ground for the scheduling theory community. In addition to an extensive academic literature on this subject, there has also been a proliferation of healthcare scheduling software companies in the marketplace. Typical scheduling systems use rule-based analytics that give schedulers advisory information from programmable heuristics such as the Bailey-Welch rule cite{B,BW}, which recommends overbooking early in the day to fill-in potential no-shows later on. We propose a dynamic programming problem formulation to the scheduling problem that maximizes revenue. We formulate the problem and discuss the effectiveness of 3 different algorithms that solve the problem. We find that the 3rd algorithm, which has smallest amount of nodes in the decision tree, has an upper bound given by the Bell numbers. We then present an alternative problem formulation that includes stochastic appointment lengths and no shows.
2
Banerjea-Brodeur, Monica. "Selection hyper-heuristics for healthcare scheduling." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/14395/.
Повний текст джерелаАнотація:
A variety of approaches have been used to solve a variety of combinatorial optimisation problems. Many of those approaches are tailored to the particular problem being addressed. Recently, there has been a growing number of studies towards providing more general search methodologies than currently exist which are applicable to different problem domains without requiring any algorithmic modification. Hyper-heuristics represent a class of such general methodologies which are capable of automating the design of search process via generating new heuristics and/or mixing existing heuristics to solve hard computational problems. This study focuses on the design of selection hyper-heuristics which attempt to improve an initially created solution iteratively through heuristic selection and move acceptance processes and their application to the real-world healthcare scheduling problems, particularly, nurse rostering and surgery admission planning. One of the top previously proposed general hyper-heuristic methodology was an adaptive hyper-heuristic consisting of many parameters, although their values were either fixed or set during the search process, with a complicated design. This approach ranked the first at an international cross-domain heuristic search challenge among twenty other competitors for solving instances from six different problem domains, including maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, travelling salesman, vehicle routing problems. The hyper-heuristics submitted to the competition along with the problem domain implementations can now be considered as the benchmark for hyper-heuristics. This thesis describes two new easy-to-implement selection hyper-heuristics and their variants based on iterated and greedy search strategies. A crucial feature of the proposed hyper-heuristics is that they necessitate setting of less number of parameters when compared to many of the existing approaches. This entails an easier and more efficient implementation, since less time and effort is required for parameter tuning. The empirical results show that our most efficient and effective hyper-heuristic which contains only a single parameter outperforms the top ranking algorithm from the challenge when evaluated across all six problem domains. Moreover, experiments using additional nurse rostering problems which are different than the ones used in the challenge and surgery scheduling problems show that the results found by the proposed hyper-heuristics are very competitive, yielding with the best known solutions in some cases.
3
Villarreal, Monica Cecilia. "Capacity planning and scheduling with applications in healthcare." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54855.
Повний текст джерелаАнотація:
In this thesis we address capacity planning problems with different demand and service characteristics, motivated by healthcare applications. In the first application, we develop, implement, and assess the impact of analytical models, accompanied by a decision-support tool, for operating room (OR) staff planning decisions with different service lines. First, we propose a methodology to forecast the staff demand by service line. We use these results in a two-phase mathematical model that defines the staffing budget for each service line, and then decides how many staff to assign to each potential shift and day pair while considering staff overtime and pooling policies and other staff planning constraints. We also propose a heuristic to solve the model's second phase. We implement these models using historical data from a community hospital and analyze the effect of different model parameters and settings. Compared with the current practice, we reduce delays and staff pooling at no additional cost. We validate these conclusions through a simulation model.
In the second application, we consider the problem of staff planning and scheduling when there is an accepted time window between each order's arrival and fulfillment, with the goal of obtaining a balanced schedule that focuses on on-time demand fulfillment but also considers staff characteristics and operational practices. Hence, solving this problem requires simultaneously scheduling the staff and the forecasted demand. We propose, implement, and analyze the results of a model for staff and demand scheduling under this setting, accompanied by a decision-support tool. We implement this model in a company that offers document processing and other back-office services to healthcare providers. We provide details on the model validation, implementation, and results, including a 25\% increase in the company's staff productivity. Finally, we provide insights on the effects of some of the model's parameters and settings, and assess the performance of a proposed heuristic to solve this problem.
In the third application, we consider a non-consumable resource planning problem. Demand consists of a set of jobs, each job has a scheduled start time and duration, and belongs to a particular demand class that requires a subset of resources. Jobs can be `accepted' or `rejected,' and the service level is measured by the (weighted) percentage of accepted jobs. The goal is to find the capacity level that minimizes the total cost of the resources, subject to global and demand-class-based service level constraints. We first analyze the complexity of this problem and several of its special cases, and then we propose a model to find the optimal inventory for each type of resource. We show the convergence of the sample average approximation method to solve a stochastic extension of the model. This problem is motivated by the inventory planning decisions for surgical instruments for ORs. We study the effects of different model parameters and settings on the cost and service levels, based on surgical data from a community hospital.
4
Demirbilek, Mustafa. "Dynamically accepting and scheduling patients for home healthcare." Thesis, University of Warwick, 2018. http://wrap.warwick.ac.uk/110628/.
Повний текст джерелаАнотація:
Importance of home healthcare is growing rapidly since populations of developed and even developing countries are getting older quickly and the number of hospitals, retirement homes, and medical staff do not increase at the same rate. We present Scenario Based Approach (SBA) for the Home Healthcare Nurse Scheduling Problem. In this problem, arrivals of patients are dynamic and acceptance and appointment time decisions have to be made as soon as patients arrive. The primary objective is to maximise the average number of daily visits. For the sake of service continuity, patients have to be visited at the same days and times each week during their service horizon. SBA is basically a simulation procedure based on generating several scenarios and scheduling new customers with a simple but fast heuristic. Then results are analysed to decide whether to accept the new patient and at which appointment day/time. First, two different versions of SBA, Daily and Weekly SBA are developed and analysed for a single nurse. We compare Daily SBA to two greedy heuristics from the literature, distance and capacity based, and computational studies show that Daily SBA makes significant improvements compared to these other two methods for a single nurse. Next, we extend SBA for a multi-nurse case. SBA is compared to a greedy heuristic under different conditions such as same depot case where nurses start their visits from and return to same place, clustered service area, and nurses with different qualification level. SBA gives superior results under all experiment conditions compared to the greedy heuristic.
5
Gurumurthy, Prakash. "Dynamic stochastic vehicle routing model in home healthcare scheduling /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426064.
Повний текст джерела
6
Elsaeiby, Aber. "Healthcare Operations Management: Models for Improving Productivity, Scheduling and Quality." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1449421673.
Повний текст джерела
7
Dehnoei, Sajjad. "A Stochastic Optimization Approach for Staff Scheduling Decisions at Inpatient Clinics." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40925.
Повний текст джерелаАнотація:
Staff scheduling is one of the most important challenges that every healthcare organization faces. Long wait times due to the lack of care providers, high salary costs, rigorous work regulations, decreasing workforce availability, and other similar difficulties make it necessary for healthcare decision-makers to pay special attention to this crucial part of their management activities. Staff scheduling decisions can be very difficult. At inpatient clinics, there is not always a good estimate of the demand for services and patients can be discharged at any given time, consequently affecting staff requirements. Moreover, there are many other unpredictable factors affecting the decision process. For example, various seasonal patterns or possible staff leaves due to sickness, vacations, etc.
This research describes a solution approach for staff scheduling problems at inpatient clinics where demand for services and patient discharges are considered to be stochastic. The approach is comprehensive enough to be generalizable to a wide range of different inpatient settings with different staff requirements, patient types, and workplace regulations.
We first classify patients into a number of patient groups with known care-provider requirements and then develop a predictive model that captures patients’ flow and arrivals for each patient category in the inpatient clinic. This model provides a prediction of the number of patients of each type on each specific day of the planning
horizon. Our predictive modelling methodology is based on a Discrete Time Markov model with the number of patients of different types as the state of the system. The predictive model generates a potentially large set of possible scenarios for the system utilization over the planning horizon. We use Monte Carlo Simulation to generate samples of these scenarios and a well known Stochastic Optimization algorithm, called the Sample Average Approximation (SAA) to find a robust solution for the
problem across all possible scenarios. The algorithm is linked with a Mixed-Integer Programming (MIP) model which seeks to find the optimal staff schedule over the planning horizon while ensuring maximum demand coverage and cost efficiency are achieved. To check the validity of the proposed approach, we simulated a number of scenarios for different inpatient clinics and evaluated the model’s performance
for each of them.
8
Curtois, Timothy. "Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/28309/.
Повний текст джерелаАнотація:
This thesis is concerned with automated personnel scheduling in healthcare organisations; in particular, nurse rostering. Over the past forty years the nurse rostering problem has received a large amount of research. This can be mostly attributed to its practical applications and the scientific challenges of solving such a complex problem. The benefits of automating the rostering process include reducing the planner’s workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. Basically stated, the nurse rostering problem requires the assignment of shifts to personnel to ensure that sufficient employees are present to perform the duties required. There are usually a number of constraints such as working regulations and legal requirements and a number of objectives such as maximising the nurses working preferences. When formulated mathematically this problem can be shown to belong to a class of problems which are considered intractable. The work presented in this thesis expands upon the research that has already been conducted to try and provide higher quality solutions to these challenging problems in shorter computation times. The thesis is broadly structured into three sections. 1) An investigation into a nurse rostering problem provided by an industrial collaborator. 2) A framework to aid research in nurse rostering. 3) The development of a number of advanced algorithms for solving highly complex, real world problems.
9
Iezzi, Jana. "Multi-criteria decision making in outpatient scheduling." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001817.
Повний текст джерела
10
White, Denise L. "Operational Planning and Scheduling in the Outpatient Clinic Environment." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276527552.
Повний текст джерела
11
Kapamara, T. "Novel approaches to radiotherapy planning and scheduling in the NHS." Thesis, Coventry University, 2010. http://curve.coventry.ac.uk/open/items/dbc287f2-c579-39c9-921b-3411ee1c9c7e/1.
Повний текст джерелаАнотація:
The main subject matter of this thesis concerns radiotherapy patient scheduling subproblems formulated as four separate shop scheduling problem models (i.e. hybrid flowshop, flowshop, mixed shop and multiple identical parallel machine scheduling problems) based on the characteristics of the intricate real-life treatment processes observed at the Arden Cancer Centre in Coventry, UK. Insight into these processes was gained by developing and using a novel discrete-event simulation (DES) model of the four units of the radiotherapy department. By typifying the subproblems as well-known scheduling problem models, it was intended that methods amenable to them such as heuristics be used in the study. Four novel constructive heuristics based on priority dispatching rules and strategies adapted from some established algorithms have been developed and implemented using the C++ programming language. Further, these heuristics were incorporated into the DES model to create schedules of appointments for the patients generated daily. The effectiveness and efficiency of the constructive heuristics have been tested using the following performance criteria: minimising i) average waiting time to the start of treatment, and ii) average percentage of patients late for their treatment, and iii) the amount of overtime slots used for the patients received in a given period of time. The coordinated constructive heuristics and the DES model have also been tested using possible alternative pathways patients can follow in the treatment unit. The aim of these tests was to compare the efficiency of the radiotherapy department’s current pathway to other possible pathways. Further, strategies for using maximum allowed breaches of targeted due dates, reserved slots for critical treatments and overtime slots was also included in the heuristics. The results of several tests showed that the heuristics created schedules of appointments whose average waiting times for emergency, palliative and radical treatments improved by about 50%, 34% and 41%, respectively, compared to the historical data. However, their major slack was evidenced by the fact that about 13% of the patients needing palliative treatment were expected to be late for treatment compared to about 1% of those requiring radical treatment.
12
Rinder, Maria M. "An Integrated Decision-Support Tool to Forecast and Schedule No-Show Appointments in Healthcare." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1339185682.
Повний текст джерела
13
Khambhammettu, Mahith. "Analyzing behavior and applicability of an optimization model : A simulation study for sequence dependent scheduling of surgeries." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11721.
Повний текст джерелаАнотація:
Context. With the proportional increase in the population of elderly people, there is an increase in the need for providing quality health-care. Operating room planning is one aspect that is considered to meet the requirement of providing quality health care. Operatingroom planning concerns about the efficient management of the available resources to perform surgeries. It deals with allocation and assignment of surgeries to operating rooms in a sequential manner using resource optimization strategies to manage with the available operating rooms. Objectives. In this thesis, we investigate the behavior and applicabilityof an optimization model and measure the degree to which the model can efficiently utilize the available hospital resources. Methods. Simulations are conducted to test the impact of implemented model on turnover time. The experiment is conducted on three different scenarios using the real world data collected from Blekinge hospital. Results. The impact on the turnover time measured for the three different scenarios is evaluated using simulation experiment. The relationship between the scenarios is identified by comparing the results with a baseline scenario (real world schedule). Conclusions. Based on the analysis, we conclude that the new optimization model is capable of scheduling better than the existing scheduling system used by the hospitals. The observations show that optimization model significantly reduces the turnover time compared to the real schedule. Besides, the scenario using an additional resourceis found to have better performance compared to other scenarios. The thesis concludes by showcasing the performance and applicability of the optimization model.
14
Myers, Robert A. "Engineering Healthcare Delivery: A Systems Engineering Approach to Improving Trauma Center Nursing Efficacy." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1482419145222356.
Повний текст джерела
15
Liu, Yazhuo. "Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System." Thesis, University of South Florida, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3714134.
Повний текст джерелаАнотація:
The efforts to improve health care delivery usually involve studies and analysis of patient populations and healthcare systems. In this dissertation, I present the research conducted in the following areas: identifying patient groups, improving treatments for specific conditions by using statistical as well as data mining techniques, and developing new operation research models to increase system efficiency from the health institutes’ perspective. The results provide better understanding of high risk patient groups, more accuracy in detecting disease’ correlations and practical scheduling tools that consider uncertain operation durations and real-life constraints.
16
Bashir, Bushra. "Contribution to modeling and optimization of home healthcare." Thesis, Clermont-Ferrand 2, 2013. http://www.theses.fr/2013CLF22391/document.
Повний текст джерелаАнотація:
Résumé indisponibleA healthcare network or health system consists of all organizations, actions and people who participate to promote, restore or maintain people’s health. The health care systems in many developed countries are facing increasing costs. The major reason is the changing age distribution of the population with more elderly people in need of support. Increasing healthcare costs has created new alternatives to traditional hospitalization in which one is Home Health Care (HHC). Home health care or domiciliary care is the provision of health care and assistance to people in their own homes, according to a formal assessment of their needs. HHC has attained a specific place in healthcare network. HHC programs have now been successfully implemented in many countries. The purpose of HHC is to provide the care and support needed to assist patients to live independently in their own homes. HHC is primarily performed by means of personal visitations of healthcare workers to patients in their homes, where they provide care assistance according to patients’ needs. In this thesis we have considered different aspects of planning problems for home health care services. The efficient use of resources is necessary in continuous healthcare services. To meet the increased demand of HHC, operation research specialist can play an important role by solving the various combinatorial optimization problems arising in HHC. These problems can be tactical, strategic or operational with respect to planning horizon. Strategic problems are those which help in attaining long term goals or objectives, e.g. higher level of quality for HHC patients and efficient use of resources. These strategic objectives can be achieved through tactical i.e. medium term panning and operational planning i.e. short term planning. The main purpose of our thesis is to identify these potential optimization problems and solve them via recent metaheuristics. HHC is an alternative to traditional hospitalization and has got a significant share in the organization of healthcare in developed countries. The change in aging demographics, recent development in technology and the increase in the demand of healthcare services are major reasons for this rapid growth. Some studies show HHC as a tool to reduce costs of care, which is a major preoccupation in developed countries. Some others reveal that it leads to the improvement of patients’ satisfaction without increasing the resources. Home health care, i.e. visiting and nursing patients in their homes, is a flourishing realm in the medical industry. The number of companies has grown largely both in public and private sectors. The staffing needs for HHC companies have been expanded as well. Also they face the problem of assigning geographically dispersed patients to home healthcare workers and preparing daily schedules for these workers. The challenge of this problem is to combine aspects of vehicle routing and staff rostering. Both of them are well known NP- hard combinatorial optimization problems, it means the amount of computational time required to find solution increases exponentially with problem size. Home healthcare workers scheduling problem is difficult to solve optimally due to presence of large number of constraints. These are two types of constraints: hard constraints and soft constraints. The hard constraints are the restrictions to be fulfilled for the schedules to be applicable and soft constraints are preferences to improve the quality of these schedules. (...)
17
Lourenço, Maria Luísa Oliveira. "Agendamento de atos médicos no Instituto Português de Reumatologia." Master's thesis, Instituto Superior de Economia e Gestão, 2013. http://hdl.handle.net/10400.5/11405.
Повний текст джерелаАнотація:
Mestrado em Decisão Económica e EmpresarialEste trabalho foca-se no agendamento dos atos médicos que o IPR oferece para as várias especialidades, dando particular atenção à especialidade de reumatologia. O objetivo deste trabalho é ajustar o número de atos médicos, no sentido de chegar ao objetivo de atos médicos contratualizados e rentabilizar o tempo disponível dos médicos, tendo em conta restrições contextuais. Após uma recolha inicial de dados, foi delineado um modelo para se efetuar a distribuição dos atos médicos, de forma rápida, organizada e eficiente. O modelo vai ser estudado utilizando metodologias de otimização, nomeadamente programação linear, de modo a poderem ser testados dois cenários. O software utilizado foi o Premium Solver Pro. O modelo construído está focado na especialidade de reumatologia mas poderá ser adaptado a qualquer das outras especialidades oferecidas pelo IPR.
This report will focus on the scheduling of medical appointments offered by the Portuguese Institute of Rheumatology. The Institute offers treatments on various specialties, but it gives special attention to the rheumatology specialty. The goal of this study is to adjust the number of appointments, in order to reach the objective number of medical acts that was contracted, and to profit from the available time of each physician, taking into account the contextual restrictions. After a primary data collection, a problem was identified to figure out how to distribute the appointments, in a fast, organized and efficient way. The problem will be studied using optimization methodologies, namely linear programing, in lieu of testing two scenarios. The software used is Premium Solver Pro. The model is focused on the rheumatology specialty but it can be adapted for any of the other specialties offered by IPR.
18
Moussavi, Seyed Esmaeil. "Workforce scheduling and job rotation by considering ergonomic factors (Presentation of the Sequencing Generalized Assignment Problem) : application to production and home healthcare systems." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCA017/document.
Повний текст джерелаАнотація:
Cette thèse porte sur la planification du personnel en accordant une attention particulière à l'aspect humain et aux facteurs ergonomiques dans le domaine de la production. Un certain nombre de modèles mathématiques sont présentés pour formuler les problèmes d'ordonnancement et de planification du personnel étudié. Concernant les modèles de planification, la productivité du système de fabrication et le bien-être des travailleurs sont ciblés. De cette manière, une méthode d'affectation des travailleurs est présentée pour réduire le temps de production et une méthode d'ordonnancement pour la rotation des tâches est présentée afin d’équilibrer la charge de travail des opérateurs. À cet effet, une analyse ergonomique est effectuée sur les postes de travail du système de production étudié. Cette analyse aboutit à l'évaluation des postes du travail suivant la convention dite des feux de circulation, c'est-à-dire que les postes sont classés dans les niveaux de charge faible, moyen et élevé qui sont représentés respectivement par les couleurs verte, jaune et rouge. Une approche mathématique est développée pour convertir ces résultats en valeurs numériques, car les paramètres quantitatifs sont plus applicables pour l'optimisation de la planification. Une programmation multi-objectifs est proposée pour optimiser les deux objectifs mentionnés du problème d'ordonnancement de tournée du personnel étudié. Les méthodes d'agrégation linéaire et de ε-contrainte sont appliquées pour résoudre ce modèle d'optimisation. En outre, cette thèse présente une nouvelle variante du problème d'affectation appelé problème d'affectation généralisée par séquence qui est défini pour la planification du personnel dans un système combiné constitué des postes de travail en série et en parallèle. Il est prouvé que ce problème d'optimisation combinatoire est NP-difficile et les méthodes exactes ne sont pas capables de résoudre les instances de grande taille. Ainsi, trois méthodes approchées composées de deux approches matheuristiques et une heuristique hybride sont développées pour résoudre ce problème. Les méthodes matheuristiques sont basées sur la décomposition de la formulation pour simplifier le modèle principal en deux ou plusieurs modèles plus petits. La troisième méthode est une heuristique gloutonne combinée à une recherche locale. En outre, dans la dernière étape de cette thèse, la planification des ressources humaines pour un système de soins à domicile est formulée mathématiquement. Selon la structure du système, une intégration des problèmes d'affectation et de tournées de véhicules est présentée. Enfin, une approche matheuristique en trois étapes est proposée pour résoudre ce problème d'optimisation combinatoireThis thesis concerns the human resource planning by paying a special attention to the human aspect and ergonomic factors in the manufacturing domain. A number of mathematical models are presented to formulate the studied workforce scheduling and planning problems. In the planning models, the productivity of the manufacturing system and the well-being of the workers are targeted. In this way, a worker assignment approach is presented to reduce the production time and a job rotation scheduling approach is presented to balance the workloads on the operators. For this purpose, an ergonomic analysis is carried out on the jobs of the studied production system. This analysis results in the traffic light evaluation for the jobs, i.e., the jobs are categorized into the low, medium and high workload levels which are presented respectively by the green, yellow and red colors. A mathematical approach is developed to convert these outputs to the numerical values, because the quantitative parameters are more applicable for the optimization of the planning. A multi-objective programming is proposed to optimize two mentioned objectives of the studied workforce scheduling problem. Both linear aggregation and epsilon-constraint methods are applied to solve this optimization model. Furthermore, this thesis presents a novel variant of the assignment problem called sequencing generalized assignment problem which is defined for workforce scheduling in a combined system consisting of the jobs in series and in parallel. It is proved that this combinatorial optimization problem is NP-hard and the exact methods are not able to solve the large-scale instances. Hence, three approximate methods consisting of two matheuristic and a hybrid heuristic approaches are developed to solve it. The matheuristic methods are based on the decomposition of the formulation to break down and simplify the main model into two or more smaller models. The third method is a greedy heuristic combined with a local search. The efficiency of the three mentioned methods is evaluated by various instances of different sizes. Moreover, in the last step of this thesis, the human resource planning for a home healthcare system is formulated mathematically. According to the structure of the system, an integration of the worker assignment and vehicle routing problems is presented. Finally, a three-steps matheuristic approach is proposed to solve this combinatorial optimization problem
19
Bennett, Ashlea R. "Home health care logistics planning." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33989.
Повний текст джерелаАнотація:
This thesis develops quantitative methods which incorporate transportation modeling for tactical and operational home health logistics planning problems. We define home health nurse routing and scheduling (HHNRS) problems, which are dynamic periodic routing and scheduling problems with fixed appointment times, where a set of patients must be visited by a home health nurse according to a prescribed weekly frequency for a prescribed number of consecutive weeks during a planning horizon, and each patient visit must be
assigned an appointment time belonging to an allowable menu of equally-spaced times. Patient requests are revealed incrementally, and appointment time selections must be made without knowledge of future requests. First, a static problem variant is studied to understand the impact of fixed appointment times on routing and
scheduling decisions, independent of other complicating factors in the HHNRS problem. The costs of offering fixed appointment times are
quantified, and purely distance-based heuristics are shown to have potential limitations for appointment time problems unless proposed arc cost transformations are used. Building on this result, a new rolling horizon capacity-based heuristic is developed for HHNRS problems. The heuristic considers interactions between travel times, service times, and the fixed appointment time menu when inserting appointments for currently revealed patient requests into partial
nurse schedules. The heuristic is shown to outperform a distance-based heuristic on metrics which emphasize meeting as much patient demand as possible.
The home health nurse districting (HHND) problem is a tactical planning problem which influences HHNRS problem solution quality. A set of geographic zones must be partitioned into districts to be served by home health nurses, such that workload is balanced across districts and nurse travel is minimized. A set partitioning model for HHND is formulated and a column generation heuristic is developed which integrates ideas from optimization and local search. Methods for estimating district travel and workload are developed and implemented within the heuristic, which outperforms local search
on test instances.
20
Näsman, Felicia, and Andrea Horsma. "En prototyp av ett webbaserat placeringssystem till Södersjukhusets akutmottagning." Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297689.
Повний текст джерелаАнотація:
The emergency department at Södersjukhuset is one of the largest in the Nordic region. There is a large workforce with several skills at different levels, which means that the placement of the personnel becomes complex. The problem with the current placement method is that it is a manual and time consuming process. This report aims to solve this problem by creating a prototype of an automated and web-based placement system. During the development of the prototype different programming languages were used in unison to create a website with different functions. The most important functions to create were a search tool for names, save the placements in the form of a PDF-file and to be able to clear the placements. The goals of the project were satisfied. However, several improvements can be made to the prototype to make it more valuable for the workplace. The advantage of the prototype was that it saves time for the scheduling staff. Additionally, it facilitates the work for the on-duty personnel as the placement is easily visualised for every module at the emergency department.Södersjukhusets akutmottagning är en av Nordens största. Där finns en stor personalstyrka med olika yrkesgrupper och inom dem finns flera olika kompetensnivåer, vilket medför att placeringen av personalen blir komplex. Problemet med nuvarande placeringssystem är att det är en manuell och tidskrävande process. Målet för projektet var att lösa detta problem genom att skapa en prototyp av ett automatiserat och webbaserat placeringssystem. Under utvecklingen av prototypen användes olika programmeringsspråk i samklang för att skapa en hemsida med olika funktioner. De viktigaste funktionerna som skapades var att kunna söka efter namn, spara placeringen i form av en PDF-fil samt att kunna rensa placeringen på information. Målen för arbetet uppfylldes. Samtidigt finns det flera utvecklingsområden för prototypen som kan göra den mer användbar och värdefull för arbetsplatsens placering av personal. Fördelen med prototypen var att den sparar tid för bemanningspersonalen samt underlättar arbetet för personalen i tjänst då hela placeringen enklare kan visualiseras på varje modul.
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Oliveira, Margarida Maria de Frias Rodrigues de. "Escalonamento de pacientes de radioterapia." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/11164.
Повний текст джерелаАнотація:
Mestrado em Decisão Económica e EmpresarialO tema deste Trabalho Final de Mestrado (TFM) centra-se na otimização de um escalonamento de pacientes de radioterapia, no contexto do Hospital da Luz, em Lisboa. Este TFM foi desenvolvido sob a forma de um projeto, tendo como principal objetivo maximizar a oferta de tratamentos por dia, minimizando o tempo de inatividade do serviço no seu horário de funcionamento diário. Assim, a fase inicial do projeto consistiu em compreender como funcionava o serviço de radioterapia e aprender algumas noções acerca dos tratamentos que podem ser feitos. Posteriormente, procedeu-se à recolha de dados, que correspondem aos tempos específicos de tratamento de cada paciente, tendo em consideração o tumor a ser tratado e a técnica utilizada. Este estudo foi elaborado tendo como base um escalonamento inicial correspondente aos pacientes em tratamento. Esta afetação é revista semanalmente por forma a planear a semana seguinte. Assim, conhecidos os pacientes que finalizam e os que iniciam tratamento na semana seguinte, pretende-se afetar os vários pacientes às vagas disponíveis. O problema identificado e formulado foi resolvido com recurso ao Solver do Excel. A solução obtida é posteriormente escrita numa folha de Excel com recurso a um programa desenvolvido em VBA.
This Masters' Final Work (MFW) is focused on optimizing a schedule for radiotherapy patients in the context of Hospital da Luz, in Lisbon. My MFW is considered as a project, with the main objective of maximizing the number of treatments per day, while minimizing service idle time within its daily workload. Thus, the initial phase of the project was to understand how the radiotherapy service works and learn about treatments that can be done. Then, it was proceeded to the collection of data, which correspond to specific time of treatment of each patient, taking into account the tumor to be treated and the technique used. This study was based on an initial schedule according to the patients under treatment. The allocation is reviewed on a weekly basis in order to plan the next week. Thus, knowing the patients who complete and those who start treatment in the following week, the aim is to affect the new patients to available places, as much as possible. The identified and formulated problem was solved via Excel Solver. The solution obtained is subsequently written in an Excel spreadsheet using a program developed in VBA.
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Jacquemin, Yoan. "Optimisation de la trajectoire du patient dans les centres de radiothérapie ou d'hadronthérapie." Phd thesis, Université Jean Monnet - Saint-Etienne, 2011. http://tel.archives-ouvertes.fr/tel-00718789.
Повний текст джерелаАнотація:
L'optimisation de la planification des traitements par rayons ionisants est bénéfique tant aux patients qu'aux structures de soins bien que particulièrement difficile du fait de la rareté des ressources et de l'importante répétition des séances. Face à cette problématique, un modèle d'optimisation linéaire à nombres entiers a été créé permettant de planifier des protocoles de traitement complexes tout en prenant en compte la disponibilité des patients ainsi que des radiothérapeutes qui les suivent avec pour résultat une amélioration significative des performances sur des indicateurs couvrant les ressources humaines et matérielles ainsi que les délais de prise en charge. De plus nous avons développé des solutions adaptées à des contextes concrets : i) une planification heuristique de la trajectoire des patients au sein du Centre de Protonthérapie d'Orsay (CPO) assortie d'indicateurs de performances, et ii) une adaptation au monde hospitalier de la solution industrielle de planification PREACTOR permettant de conserver la finesse obtenue dans les modélisations linéaires tout en tirant parti des capacités de résolution des heuristiques complexes intégrées à PREACTOR
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Ajmi, Faiza. "Optimisation collaborative par des agents auto-adaptatifs pour résoudre les problèmes d'ordonnancement des patients en inter-intra urgences hospitalières." Thesis, Centrale Lille Institut, 2021. http://www.theses.fr/2021CLIL0019.
Повний текст джерелаАнотація:
Cette thèse s’attaque à des problèmes d’ordonnancement des patients aux urgences, avec prise en compte des contraintes d’aval, en utilisant des approches d’optimisation collaboratives optimisant le temps d’attente global moyen des patients. Ces approches sont utilisées en intégrant, dans le comportement de chaque agent,une métaheuristique qui évolue efficacement, grâce à deux protocoles d’interaction "amis" et "ennemis". En outre, chaque agent s’auto-adapte à l’aide d’un algorithme d’apprentissage par renforcement adapté a unproblème étudié. Cette auto-adaptation tient compte d’expériences des agents et de leurs connaissances de l’environnement des urgences. Afin d’assurer la continuité d’une prise en charge de qualité des patients,nous proposons également dans cette thèse, une approche conjointe d’ordonnancement et d’affectation des lits d’aval aux patients. Nous illustrons les approches collaboratives proposées et démontrons leur sefficacités sur des données réelles provenant des services des urgences du CHU de Lille obtenues dans le cadre du projet ANR OIILH. Les résultats de simulations donnent des meilleurs ordonnancements par rapport aux scénarios dans lesquels les agents travaillent individuellement ou sans apprentissage.L’application des algorithmes qui gèrent la prise en charge des patients dans les services d’aval, fournit des résultats sous la forme d’un tableau de bord, contenant des informations statiques et dynamiques. Ces informations sont mises à jour en temps réel et permettent aux urgentistes d’orienter plus rapidement les patients vers les structures qui peuvent les accueillir. Ainsi, les résultats des expérimentations montrent que les algorithmes d’IA proposés peuvent améliorer de manière significative l’efficacité de la chaîne des urgences en réduisant le temps d’attente global moyen des patients en inter-intra-urgencesThis thesis addresses the scheduling patients in emergency department (ED) considering downstreamconstraints, by using collaborative optimization approaches to optimize the total waiting time of patients.These approaches are used by integrating, in the behavior of each agent, a metaheuristic that evolvesefficiently, thanks to two interaction protocols "friends" and "enemies". In addition, each agent self-adaptsusing a reinforcement learning algorithm adapted to the studied problem. This self-adaptation considersthe agents’ experiences and their knowledge of the ED environment. The learning of the agents allowsto accelerate the convergence by guiding the search for good solutions towards more promising areas inthe search space. In order to ensure the continuity of quality patient care, we also propose in this thesis,a joint approach for scheduling and assigning downstream beds to patients. We illustrate the proposedcollaborative approaches and demonstrate their effectiveness on real data provided from the ED of the LilleUniversity Hospital Center obtained in the framework of the ANR OIILH project. The results obtainedshow that the collaborative Learning approach leads to better results compared to the scenario in whichagents work individually or without learning. The application of the algorithms that manage the patientscare in downstream services, provides results in the form of a dashboard, containing static and dynamicinformation. This information is updated in real time and allows emergency staff to assign patients morequickly to the adequate structures. The results of the simulation show that the proposed AI algorithms cansignificantly improve the efficiency of the emergency chain by reducing the total waiting time of patientsin inter-intra-emergency
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"Scheduling in Wireless and Healthcare Networks." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.62654.
Повний текст джерелаАнотація:
abstract: This dissertation studies the scheduling in two stochastic networks, a co-located wireless network and an outpatient healthcare network, both of which have a cyclic planning horizon and a deadline-related performance metric.
For the co-located wireless network, a time-slotted system is considered. A cycle of planning horizon is called a frame, which consists of a fixed number of time slots. The size of the frame is determined by the upper-layer applications. Packets with deadlines arrive at the beginning of each frame and will be discarded if missing their deadlines, which are in the same frame. Each link of the network is associated with a quality of service constraint and an average transmit power constraint. For this system, a MaxWeight-type problem for which the solutions achieve the throughput optimality is formulated. Since the computational complexity of solving the MaxWeight-type problem with exhaustive search is exponential even for a single-link system, a greedy algorithm with complexity O(nlog(n)) is proposed, which is also throughput optimal.
The outpatient healthcare network is modeled as a discrete-time queueing network, in which patients receive diagnosis and treatment planning that involves collaboration between multiple service stations. For each patient, only the root (first) appointment can be scheduled as the following appointments evolve stochastically. The cyclic planing horizon is a week. The root appointment is optimized to maximize the proportion of patients that can complete their care by a class-dependent deadline. In the optimization algorithm, the sojourn time of patients in the healthcare network is approximated with a doubly-stochastic phase-type distribution. To address the computational intractability, a mean-field model with convergence guarantees is proposed. A linear programming-based policy improvement framework is developed, which can approximately solve the original large-scale stochastic optimization in queueing networks of realistic sizes.Dissertation/Thesis
Doctoral Dissertation Electrical Engineering 2020
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Granja, Maria da Conceição Ferreira Osório Campos. "Process Optimization in Healthcare Providers. A Batch Scheduling Approach." Tese, 2012. https://repositorio-aberto.up.pt/handle/10216/102039.
Повний текст джерела
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Granja, Maria da Conceição Ferreira Osório Campos. "Process Optimization in Healthcare Providers. A Batch Scheduling Approach." Doctoral thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/102039.
Повний текст джерела
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Lee, Chi-Hun, and 李建宏. "Factors Affecting Intentions to Use Mobile Healthcare APP:Hospital Appointment Scheduling Service." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/vh73pg.
Повний текст джерелаАнотація:
碩士輔仁大學
資訊管理學系碩士在職專班
102
During the information technology has increased in the smart handheld mobile devices, its APP had changed consumer’s lifestyles and habits. Various hospitals begun to provide hers medical APP, and it can improve the efficiency of hospital service. This study applies perceived usefulness, user attitudes, interpersonal influence, networking reputation, goodwill hospital, and subjective norms, combined with system quality, information quality dimensions to discuss the use-intention of medical App. The results are listed as below: 1. Schedule visits, mobile registration, registration inquiries, information query in medication and drug are the important function. But most medical App is not support drug information inquiries. 2. Perceived usefulness has a notably positive effect on user attitude, and user attitude have notably positive impact on use-intention. From the general public, the main concerns of medical Apps are capable of solving medical problem. 3. Interpersonal influence has a notably positive impact on subjective norms, and subjective norms have a notably positive impact on use-intention. 4. What impact user intention in networking reputation? People tend to trust comment by a credible unit (such as medical journals or medical recommendation site).
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KO, HSING-CHENG, and 柯星丞. "Scheduling Home Healthcare Visits Using Analytic Hierarchy Process with Branch and Bound Methods." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/6ukq5m.
Повний текст джерелаАнотація:
碩士國立雲林科技大學
電機工程系
105
Nowadays, the way to secure medical services has evolved. In the past, patients went to hospitals for treatment. At the present, because the population structure in Taiwan is aging and the elderly suffering from chronic diseases cannot afford to go back and forth to the hospital frequently, a new paradigm of home healthcare is emerging. Meanwhile, the uneven distribution of medical resources leads to significant shortage in medical and nursing workforces in the countryside, leaving medical personnel overloaded. As a consequence, care-givers not only have tight schedules, but also spend overwhelming time visiting patients. Random or inefficient plans for home visits will exhaust medical personnel. Such issues can be dealt with by our scheme that designs and implements a scheduling mechanism of home healthcare visits using AHP (Analytic Hierarchy Process) with B&B (Branch and Bound) methods. This thesis consists of two parts. First, we apply AHP to calculate the weights of 9 different life-threatening factors. The selection of these factors are based on official statistics of nearly eight years published by the Ministry of Health and Welfare. With these weights in place, the designed system shall single out top five patients from the database who rank highest according to illness severity. Then, we apply the B&B method to find a better route for visiting the five patients. As a handy user interface, web pages are developed to interact with our system that embodies all the necessary processes and functions. Additionally, we develop a LoRa-based wireless sensor node that is intended for deployment in each patient’s house to report the sensed physiological data to the backend database for health status monitoring. Our developed user interface is easy to access. When the medical staff selects one or multiple conditions of concern on our web page, our designated server will calculate and filter top five patient candidates in an automated manner, followed by route computation for calling on these patients. Although our suggested route is suboptimal, experiment results show that our route is kept less than 3 minutes away from the optimal one. In our architecture, route selection is simplified to an extent by trading assessment optimality for reduced computational complexity. Our system operates by consulting Google Maps online for estimating the travel time between each pair of sites. As Google Maps is reflective of real-world situations allowing for local speed limits and traffic dynamics, the resulting route from our system is arguably realistic enough. We believe that our design and implementation are of value to ease the workload on medical personnel, bringing certain usefulness and effectiveness to home healthcare circles.
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Wang, Huan master of science in engineering. "The therapist scheduling problem for patients with fixed appointment times." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4512.
Повний текст джерелаАнотація:
This report presents a series of models that can be used to find weekly schedules for therapists who provide ongoing treatment to patients scattered around a geographical region. In all cases, the patients’ appointment times and visit days are known prior to the beginning of the planning horizon. Variations in the model include single vs. multiple home bases, homogeneous vs. heterogeneous therapists, lunch break requirements, and a nonlinear cost structure for mileage reimbursement and overtime. The single home base and homogeneous therapist cases proved to be easy to solve and so were not investigated. This left two cases of interest: the first includes only lunch breaks while the second adds overtime and mileage reimbursement. In all, 40 randomly generated data sets were solved that consisted of either 15 or 20 therapists and between roughly 300 and 540 visits over five days. For each instance, we were able to obtain the minimum cost of providing home healthcare services for both models using CPLEX 12.2. The results showed that CPU time increases more rapidly than total cost as the total number of visits grows. In general, data sets with therapists who have different starting and ending locations are more difficult to solve than those whose therapists have the same home base.text
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Sharif, Behnam. "A novel non-gradient optimization methodology with application to mechanical design and scheduling in production and healthcare." 2007. http://hdl.handle.net/1993/29474.
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yu, lee-ching, and 游麗卿. "A Study of Dispatching and Scheduling Model of Home Healthcare Nurse an Example of Some Organization of Nurse Dispatching in Taiwan." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/97549890527818151360.
Повний текст джерелаАнотація:
碩士輔仁大學
資訊管理學系
95
The facts of population aging and birthrate decreasing make family caring resource lacking. Therefore, home healthcare nurse will be the main human resource of long-term caring field in future. This study first uses analytic hierarchy process (AHP) to analyze and calculate the required preference and its quantity among patient, family members, and home healthcare nurse. Under the constraints of time available, budget, each specific requirement, etc., a multi-objective dispatching and scheduling model of home healthcare nurse is formed and solved by using a famous software, Lingo 10.0. Then, a web-based prototype is developed by evolutionary prototyping method and each user can interact with the system and set up her or his preference value and constraints for finding some good-enough solution of the whole system with more flexible and adaptable way. Based on experimental results, the system can significantly enhance the satisfaction of all its participants and improve the efficiency and effectiveness of the dispatching organization. An empirical case study is from some dispatching organization of home healthcare nurse in the Northern Taiwan. Various simulation experiments based on actual data and uniform distribution are tested on the system and their managerial analysis and guidelines can be obtained as the foundation and references of subsequent research and decision-making in the organization.
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Norouzi, Esfahani Nasim. "Application of Lean Methods and Multi-Objective Optimization to Improve Surgical Patients Flow at Winnipeg Children’s Hospital." 2011. http://hdl.handle.net/1993/4771.
Повний текст джерелаАнотація:
This research has been defined in response to the Winnipeg children's hospital (WCH) challenges such as long waiting times, delays and cancellations in surgical flow. Preliminary studies on the surgical flow revealed that definition and implementation of successful process improvement projects (PIPs) along with application of an efficient master surgical schedule (MSS) are efficient solutions to the critical problems in WCH.
In the first phase of this work, a process improvement program including three major PIPs, is defined and implemented in WCH in order to improve the efficiency of the processes providing surgical service for patients. In the second phase, two new multi-objective mathematical models are presented to develop efficient MSSs for operating room department (OR) in WCH.
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CHANG, KUO-CHENG, and 章國正. "A Study of Design and Application of Rehabilitation Scheduling System for Improving Healthcare Quality Management：The Recent Experience of a Regional Teaching Hospital in Taiwan." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/zsmc2p.
Повний текст джерелаАнотація:
碩士亞洲大學
經營管理學系碩士在職專班
105
The purpose of this study was (1) to develop a medical scheduling system for scheduling and transferring the patients of the rehabilitation department, and (2) to use the Quality Control Circle (QCC) to compare and analyze the data from the scheduling system, and (3) using the TAM2 to design a questionnaire to discuss the factors of staffs’ accepting levels of the medical scheduling system. METHOD: 3 steps1. First step (system building): Using Microsoft office Access 2010 to develop the medical scheduling system, and the scheduling system is divided into 2 parts: (1) scheduling system for IN-patient and (2) scheduling system for OPD.2. Second step (QCC application): Exporting the data from the system to form a excel films and analysis the data to analyze the degree of infecting the healthcare quality management.3. Third step (Questionnaire): Using the technology acceptance model 2 (TAM2) to design a questionnaire for the staffs who works in the teaching hospital of the central area of Taiwan to discuss the factors of their accepting levels of the medical scheduling system. Result: Improving the QCC’s value from 55.20% to 85.95%,and the result of Questionnaire reveals that no obvious differences in demographic characters and in SN and PEOU. Conclusion: The system actually helps to improve the healthcare quality management.
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Shao, Yufen. "Home therapist network modeling." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4500.
Повний текст джерелаАнотація:
Home healthcare has been a growing sector of the economy over the last three decades with roughly 23,000 companies now doing business in the U.S. producing over $56 billion in combined annual revenue. As a highly fragmented market, profitability of individual companies depends on effective management and efficient operations. This dissertation aims at reducing costs and improving productivity for home healthcare companies.
The first part of the research involves the development of a new formulation for the therapist routing and scheduling problem as a mixed integer program. Given the time horizon, a set of therapists and a group of geographically dispersed patients, the objective of the model is to minimize the total cost of providing service by assigning patients to therapists while satisfying a host of constraints concerning time windows, labor regulations and contractual agreements. This problem is NP-hard and proved to be beyond the capability of commercial solvers like CPLEX. To obtain good solutions quickly, three approaches have been developed that include two heuristics and a decomposition algorithm.
The first approach is a parallel GRASP that assigns patients to multiple routes in a series of rounds. During the first round, the procedure optimizes the patient distribution among the available therapists, thus trying to reach a local optimum with respect to the combined cost of the routes. Computational results show that the parallel GRASP can reduce costs by 14.54% on average for real datasets, and works efficiently on randomly generated datasets.
The second approach is a sequential GRASP that constructs one route at a time. When building a route, the procedure tracks the amount of time used by the therapists each day, giving it tight control over the treatment time distribution within a route. Computational results show that the sequential GRASP provides a cost savings of 18.09% on average for the same real datasets, but gets much better solutions with significantly less CPU for the same randomly generated datasets.
The third approach is a branch and price algorithm, which is designed to find exact optima within an acceptable amount of time. By decomposing the full problem by therapist, we obtain a series of constrained shortest path problems, which, by comparison are relatively easy to solve. Computational results show that, this approach is not efficient here because: 1) convergence of Dantzig-Wolfe decomposition is not fast enough; and 2) subproblem is strongly NP-hard and cannot be solved efficiently.
The last part of this research studies a simpler case in which all patients have fixed appointment times. The model takes the form of a large-scale mixed-integer program, and has different computational complexity when different features are considered. With the piece-wise linear cost structure, the problem is strongly NP-hard and not solvable with CPLEX for instances of realistic size. Subsequently, a rolling horizon algorithm, two relaxed mixed-integer models and a branch-and-price algorithm were developed. Computational results show that, both the rolling horizon algorithm and two relaxed mixed-integer models can solve the problem efficiently; the branch-and-price algorithm, however, is not practical again because the convergence of Dantzig-Wolfe decomposition is slow even when stabilization techniques are applied.text
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Figueiredo, Helder Capitão. "Otimização da gestão de stocks de dispositivos médicocirúrgicos." Master's thesis, 2015. http://hdl.handle.net/1822/37868.
Повний текст джерелаАнотація:
Dissertação de mestrado integrado em Engenharia e Gestão IndustrialEsta dissertação foi desenvolvida no âmbito do projeto de conclusão do Mestrado Integrado em Engenharia e Gestão Industrial, com o tema “Otimização da Gestão de Stocks de Dispositivos Médicocirúrgicos”, feito, a par do estágio curricular, no Hospital de Braga. O objetivo primário era aproveitar os dados providenciados pelas recentes melhorias na logística do hospital, nomeadamente a implementação de um sistema de armazéns avançados, para tornar a gestão de stocks mais eficaz. O sistema de armazéns avançados implementado permite a obtenção de dados relativamente aos procedimentos cirúrgicos efetuados no Bloco Operatório Central (BOC), nomeadamente os materiais gastos, o número de procedimentos efetuados e a sua disposição no tempo. Com estes dados à disposição foi pedido que se fizesse uma mudança gradual da gestão de stocks, de alguns dispositivos médico-cirúrgicos a escolher, de push para pull, com base no agendamento e na previsão da produção cirúrgica. Feita a análise dos dados disponibilizados, bem como de toda a logística de abastecimento do Hospital de Braga, foi sugerido um modelo de compra e gestão de stocks que, reduzindo a incerteza relativamente à procura, permite baixar as existências dos materiais sem que isso afete o nível de serviço.
The present dissertation, entitled “Optimization of the Inventory Management of Surgical Materials”, was developed under the scope of the completion of the Integrated Master in Industrial Management and Engineering and was made, alongside the curricular internship, at Hospital de Braga. The primary objective was to avail the data provided by recent improvements in the hospital logistics, mainly the implementation of a system of advanced warehouses, and to enhance the efficiency of the inventory management. The advanced warehouse system allows the collection of data regarding surgical procedures performed in the operating room, namely material expenses, the number of procedures undertaken, and its temporal arrangement. Having all these data, it was requested to perform a gradual change in the inventory management of some materials, from a push to a pull-based management, depending on the scheduling and surgical production forecast. After performing the data analysis, as well as the analysis of the hospital’s supply chain, it was suggested a purchase and stock management model that, achieving to reduce the demand uncertainty, allows the reduction of the materials stock without affecting the service level provided.