Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Welcome to OR&S! Where students, academics and professionals come together

In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities

HealthPartners: Consumer-Focused Mission and Collaborative Approach Support Ambitious Performance Improvement Agenda

Presents a case study of a nonprofit healthcare organization that exhibits the six attributes of an ideal healthcare delivery system as defined by the Fund, including information continuity, care coordination and transitions, and system accountability

PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work

How Labor-Management Partnerships Improve Patient Care, Cost Control, and Labor Relations: Case Studies of Fletcher Allen Health Care, Kaiser Permanente, and Montefiore Medical Center’s Care Management Corporation

[Excerpt] This paper explores the ways in which healthcare unions and their members are strategically engaging with management through partnership to control costs and improve the patient experience, clinical outcomes, workplace environment, and labor relations. These initiatives depend on making use of the knowledge of front-line healthcare workers, improving communication between all staff members, and increasing transparency. In turn, these initiatives can also lead to more robust and dynamic local unions. Through participating in joint work activities, many union members note feeling more respected in their workplace and more connected to their union. Unions can benefit from these activities by offering their members the ability to inform decisions about how work gets done

Gestión logística de sistemas de hospitalización domiciliaria: una revisión crítica de modelos y métodos

RESUMEN: Los servicios de Hospitalización Domiciliaria (HD) se basan en una red de distribución, en la cual los pacientes son hospitalizados en sus casas y los prestadores de servicios de salud deben entregar cuidados médicos coordinados a los pacientes. La demanda de estos servicios está creciendo rápidamente y los gobiernos y proveedores de servicios de salud enfrentan el reto de tomar un conjunto de decisiones complejas en un sector con un componente logístico importante. En este artículo se presenta una revisión crítica de los modelos y métodos utilizados para darle soporte a las decisiones logísticas en HD. Para esto se presenta primero un marco de referencia, con el objetivo de identificar las oportunidades de investigación en el campo. Con base en dicho marco, se presenta la revisión de la literatura y la identificación de brechas en la investigación. En particular, se hace énfasis en la necesidad de desarrollar e implementar metodologías más integradas para dar soporte a las decisiones estratégicas y tácticas y de considerar puntos clave de los sistemas reales.ABSTRACT: Home Health Care (HHC) services are based on a delivery network in which patients are hospitalized at their homes and health care providers must deliver coordinated medical care to patients. Demand for HHC services is rapidly growing and governments and health care providers face the challenge to make a set of complex decisions in a medical service business that has an important component of logistics problems. The objective of this paper is to provide a critical review of models and methods used to support logistics decisions in HHC. For this purpose, a reference framework is proposed first in order to identify research perspectives in the field. Based on this framework, a literature review is presented and research gaps are identified. In particular, the literature review reveals that more emphasizes is needed to develop and implement more integrated methodologies to support decisions at tactical and strategic planning levels and to consider key features from real systems

Building Medical Homes in State Medicaid and CHIP Programs

Presents strategies, best practices, and lessons learned from ten states' efforts to advance the medical home model of comprehensive and coordinated care in Medicaid and Children's Health Insurance Programs in order to improve quality and contain costs

The Case for Developing and Deploying an Open Source Electronic Logistics Management Information System

Summarizes efforts to strengthen health information systems in low- and lower-middle-income countries, including development of common requirements. Outlines models for collaboration among stakeholders, national leaders, and health information users

Design of Equipment Rack with TRIZ Method to Reduce Searching Time in Change Over Activity (Case Study : PT. Jans2en Indonesia)

Janssen is a manufacturing plant that works in furniture assembly. Component shortages often occurs, it will cause the increase of work in process (WIP) in assembly section. In previous studies, we analyze the root causes with FMEA and then it is resulted that router section is the constraint of the system. There are many non value added activities such as searching and transportation caused by a messy condition of work places and the devices that aren’t put in the right place. The impact is that the time allocated for every change over is higher than before. There are many components that are worked by the router section, so improvements are needed to minimize changes in over time. 5S method and the use of a new design of rack by TRIZ method are suggested for fixing the conditions of work environment. It is expected to eliminate non value added activities and changes in over time. Result shows that we can reduce non value activities in change over of regular components up to 41% and the elimination of this time is 41,6%. The non value activities in changeover of new items is 36,6% and this elimination of time is 53,3%. Key word : change over, kaizen, design, TRIZ metho

Implementation plan of advanced access for the ear nose and throat department at a public portuguese hospital

Today's healthcare landscape grapples with the challenge of timely access and operational efficiency. The Ear, Nose, and Throat (ENT) department of the hospital in study faces these and other obstacles, such as high no-show rates and operational inefficiencies. High no-show rates disrupt patient care and leave resources underused, while low operational efficiency extends lead time and compromised continuity of care. To address these issues, a literature review was conducted, as well as interviews with some professionals of the department in study. A problem-solving methodology was used, in which the situation was diagnosed and then solutions were proposed to solve the problem. This thesis proposes an adapted Advanced Access implementation in the ENT department. It aims to enhance patient care and operational efficiency. By exploring this subject and implementing Advanced Access, this study contributes insights to elevate patient care and operational effectiveness in healthcare in the department in study.O atual senário da saúde enfrenta o desafio de fornecer acesso atempado e eficiente a nível operacional. O departamento de Otorrinolaringologia enfrenta esses e outros problemas, como altas taxas de não comparecimento e ineficiência operacional. As altas taxas de não comparecimento prejudicam o atendimento ao paciente e levam a subutilização de recursos, enquanto a baixa eficiência operacional aumenta o tempo de espera e compromete a continuidade de cuidados. Para abordar essas questões, foi realizada uma revisão da literatura, bem como entrevistas com alguns profissionais do departamento em estudo. Foi utilizada uma metodologia de resolução de problemas, na qual a situação foi diagnosticada e, em seguida, propostas soluções para resolver o problema. Esta tese propõe uma implementação adaptada de Advance Access no departamento de Otorrinolaringologia. Tem como objetivo melhorar o atendimento ao paciente e a eficiência operacional O objetivo é melhorar o atendimento ao paciente e a eficiência operacional. Ao explorar este assunto e implementar o Acesso Avançado, este estudo contribui com insights para elevar o atendimento ao paciente e a eficácia operacional em cuidados de saúde no departamento em estudo