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

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

The SEC-system : reuse support for scheduling system development

Recently, in a joint cooperation of Stichting VNA, SAL Apotheken, the Faculty of Management and Organization, and the University Centre for Pharmacy, University of Groningen in the Netherlands, a Ph.D-study started regarding Apot(he)ek, Organization and Management (APOM). The APOM-project deals with the structuring and steering of pharmacy organization. The manageability of the internal pharmacy organization, and the manageability of the direct environment of pharmacy organization is the subject matter. The theoretical background of the APOM-project is described. A literature study was made to find mixes of objectives. Three mixes of objectives in pharmacy organization are postulated; the product mix, the process mix, and the customer mix. The typology will be used as a basic starting point for the empirical study in the next phase of the APOM-project.

The drug logistics process: an innovation experience

Purpose - The purpose of this paper is to present the latest innovations in the drug distribution processes of hospital companies, which are currently dealing with high inventory and storage costs and fragmented organizational responsibilities. Design/methodology/approach - The literature review and the in-depth analysis of a case study support the understanding of the unit dose drug distribution system and the subsequent definition of the practical implications for hospital companies. Findings - Starting from the insights offered by the case study, the analysis shows that the unit dose system allows hospitals to improve the patient care quality and reduce costs. Research limitations/implications - The limitations of the research are those related to the theoretical and exploratory nature of the study, but from a practical point of view, the work provides important indications to the management of healthcare companies, which have to innovate their drug distribution systems. Originality/value - This paper analyzes a new and highly topical issue and provides several insights for the competitive development of a fundamental sector

Incorporating capacitative constraint to the preference-based conference scheduling via domain transformation approach

No AbstractKeywords: conference scheduling; domain transformation approach; capacity optimizatio

Use of the CAMEO II Acuity Tool to Decrease Burnout for Nurses Working in a Pediatric Intensive Care Unit

The World Health Organization (WHO) reported registered nurse burnout is an occupational hazard resulting in serious consequences for patients, healthcare organizations, and individual registered nurses (Woo et al., 2020). The purpose of this project was to see if the Complexity Assessment and Monitoring to Ensure Optimal Outcomes II (CAMEO II) Acuity Tool, used as an intervention for staffing and scheduling, would have a positive effect against nurse burnout in a pediatric critical care setting in a pediatric medical center. Maslach’s Burnout Inventory- Human Services Survey for Medical Personnel (MBI-HSSMP) was used as a pre- and postsurvey to measure the emotional exhaustion, reduced personal accomplishment, and depersonalization of registered nurses before and after the use of the CAMEO II Acuity Tool. The initial results from the MBI-HSSMP presurvey were alarming, showing evidence of chronic nurse burnout. While the CAMEO II Acuity Tool was exhausting to use, the results of its implementation into the scheduling of registered nurses had a positive outcome following the results of the MBI-HSSMP postsurvey. Key recommendations for the organization and its leaders were the continued use of a modified acuity tool for its departments and continued research on other factors affecting registered nurse burnout

A domain transformation approach for addressing staff scheduling problems

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient