Promoting continuity of care in nurse-patient assignment: A multiple objective heuristic algorithm

2023 | Decision Support Systems | Citations: 0

Authors: Jiang, Haoqiang; Gomes, Paulo; Meer, Debra Vander

Abstract: Continuity of care is a critical element for delivering quality of care in inpat ... Expand

Abstract: Continuity of care is a critical element for delivering quality of care in inpatient units, however it has rarely been considered in nurse-patient assignment (NPA) models. The nursing literature suggests that continuity of care helps reduce medical errors and readmissions and increases patient satisfaction. Balancing patient assignments to nursing staff is also critical to avoid overwork and burnout. This study investigates the relationship between continuity of care and workload balance in NPA decisions in hospital inpatient units. We develop an NPA heuristic to guide continuity-care assignment while addressing the trade-off with workload balance. The heuristic prioritizes higher-acuity patients to the objective of continuity of care, while assignment of lower acuity patients seeks to minimize workload imbalance. A discrete event simulation model of an inpatient hospital unit is used to evaluate how the heuristic addresses the tradeoff between the two objectives under distinct work design sce­ narios. The heuristic enables significant increases in continuity of care for specific patient segments while limiting the impact on workload balance. We discuss implications for theory and managerial practice. Collapse

Topics: accounting legal context decision support system personality decision support

Methods: simulation computational algorithm sensitivity analysis mixed-integer programming experiment

Maximizing student opportunities for in-person classes under pandemic capacity reductions

2022 | Decision Support Systems | Citations: 0

Authors: Cardonha, Carlos; Bergman, David; Day, Robert

Abstract: In this article, we describe the decision support system that was developed for ... Expand

Abstract: In this article, we describe the decision support system that was developed for the assignment of courses to teaching modalities and rooms for the Fall semester of 2020 at the University of Connecticut (UConn). With the adoption of safety/mitigation standards imposed by the COVID-19 pandemic, the seating capacities of rooms were reduced by more than 70%, thus making virtually every existing room assignment for Fall 2020 infeasible. The demand for in-person instruction required the reassignment of a large number of courses to rooms, where not all requests for physical space could be accommodated. In order to maximize opportunities for in-person in­ struction, UConn introduced a teaching modality in which class meetings are attended on campus by only 50% of the enrolled students. As decision makers were given partial flexibility to assign teaching modalities to classes, the complexity of the assignment problem increased considerably, especially because the real-world instances involved hundreds of rooms and thousands of classes and required a quick solution turnaround in practice. In this article, we introduce this flexible assignment problem and describe the two mixed-integer programming for­ mulations that were used to solve the real-world instances of the problem; in particular, one of the formulations leverages structural properties presented in this work in order to represent the problem in a more compact way. We explain how we tailored our algorithms to solve the real-world problem, describe the dynamics of the interactive decision support system created in this initiative, and present insights derived from our study. Collapse

Topics: decision support system digital dashboard decision support covid 19 pandemic

Methods: computational algorithm optimization model experiment mixed-integer programming integer linear programming

Managing Digital Platforms with Robust Multi-Sided Recommender Systems

2022 | Journal of Management Information Systems | Citations: 0

Authors: Malgonde, Onkar S.; Zhang, He; Padmanabhan, Balaji; Limayem, Moez

Abstract: Digital platforms have replaced traditional markets in most industries and orche ... Expand

Abstract: Digital platforms have replaced traditional markets in most industries and orchestrate socioeconomic aspects of our lives. We address the problem of negative direct side network effects that arise with an increased number of agents on one side of the platform. Negative effects, if unaddressed, lead to undesired long-term consequences for the platform by developing a positive vicious cycle. Addressing negative effects require dynamic solution mechanisms that adapt to the changing landscape of platforms. The recommender systems literature has proposed multi-sided recommender systems (MSR) as a dynamic solution to many problems on platforms. However, current state-of-the-art MSRs do not consider uncertainty in predicting agents' choices, resulting in limited efficacy. We present a robust multi-sided recommender system that considers estimation errors in agents' choice to address this concern. Extensive experiments with agent-based models—ride-pooling and education platform—provide support for the efficacy and generalizability of the robust MSR to address negative effects. Collapse

Topics: recommender system digital platform anonymity IS management Python

Methods: simulation agent based simulation experiment case study nonparametric test

Data analytics and decision-making systems: Implications of the global outbreaks

2022 | Decision Support Systems | Citations: 0

Authors: Wu, Desheng; Olson, David L.; Lambert, James H.

Abstract: ... Expand

Abstract: Collapse

Topics: shapley additive explanation decision quality IT alignment big data database system

Methods: computational algorithm machine learning path analysis survey artificial neural network

Group Decision Making with Transitive Preferences Under Ordinal and Cardinal Consistencies: An Optimization Approach

2021 | Group Decision and Negotiation | Citations: 0

Authors: Wu, Zhibin; Yuan, Rong; Tu, Jiancheng

Abstract: With the ubiquity of preference relations used in group decision making (GDM), c ... Expand

Abstract: With the ubiquity of preference relations used in group decision making (GDM), controlling the transitivity of preferences characterized by individual consistency has attracted much attention. However, few previous studies consider ordinal and cardinal consistencies simultaneously and the ordinal consistency is often ignored in consensus reaching process in existing studies. In this study, the conditions of individual ordinal consistency are proved to be equivalent to a series of inequalities, based on which, an optimization model is developed to deal with the ordinal inconsistency problem. In addition, a second optimization model is proposed to address the coexistence of both ordinal and cardinal inconsistencies. A framework is designed to provide a complete strategy for controlling consistency. Such a framework is also generalized to accommodate the consensus problem in GDM. Comparing to the existing consistency improvement approaches, the proposed approach explicitly solves the ordinal consistency problem by an optimization approach. Furthermore, the proposed group consensus model guarantees both ordinal consistency and acceptable cardinal consistency when consensus is achieved. Finally, classical examples with extensive comparisons are conducted to show the effectiveness of the proposed approaches. Collapse

Methods: optimization model mixed-integer programming cross sectional research computational algorithm design artifact

An optimization-based DSS for student-to-teacher assignment: Classroom heterogeneity and teacher performance measures

2019 | Decision Support Systems | Citations: 0

Authors: Bailey, Matthew D.; Michaels, David

Abstract: A significant amount of administrator's summer planning is spent attempting to a ... Expand

Abstract: A significant amount of administrator's summer planning is spent attempting to assign hundreds of students to teachers. These assignments must satisfy federal guidelines, parent preferences, and principal preferences while pursuing classroom parity and equity in regards to academic performance, behavioral support, and demographics. In addition, teachers are often the most invested in these decisions given the impact on their day-to-day life and their future performance evaluations. This problem is a variant of a standard problem in operations research; however, administrators often lack the technical expertise or budgets to build and/or implement such models. We present an open-source spreadsheet model-based DSS to this purpose. We formulate the problem as a mixed integer program, present the implemented spreadsheet model interface, and illustrate on a set of student data. This DSS was implemented in the fall of 2018 for the assignment of close to 600 students to 24 classrooms saving dozens of staff and administration hours. With minor adaptions and/or redefinitions, the DSS would provide similar value to many of the roughly 64,000 public primary and middle school principals across the country. Even more generally, the model as presented is a framework for developing and implementing an optimization-based DSS for assistance in creating project teams under a variety of potential preferences and constraints on team composition. Collapse

Topics: decision support system open source spreadsheet decision making

Methods: optimization model computational algorithm mixed-integer programming literature sample linear optimization

Modeling Local Ambulance Resource Scheduling

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Goldberg, David; Garvey, Patricia; Xia, Long; Hong, Sukhwa

Abstract: Effective management of ambulance staffing is a complex challenge that has prof ... Expand

Abstract: Effective management of ambulance staffing is a complex challenge that has profound implications for public health and safety. This study analyzes the spatial and temporal distribution of 911 calls and proposes a model for staffing ambulance shifts. We demonstrate the variability in important factors across localities and the implications of these factors for response times. We also show the optimal resource scheduling proposed by our model and the variability in these schedules across localities. These results offer a useful framework from which practitioners can analyze their locality and ensure effective management of emergency medical service resources. Collapse

Topics: criminality resource allocation

Methods: simulation mixed-integer programming regression analysis method

Constraint programming for flexible Service Function Chaining deployment

2019 | HICSS | Citations: 0

Authors: Liu, Tong; Callegati, Franco; Cerroni, Walter; Contoli, Chiara; Gabbrielli, Maurizio; Giallorenzo, Saverio

Abstract: Network Function Virtualization (NFV) and Software Defined Networking (SDN) are t ... Expand

Abstract: Network Function Virtualization (NFV) and Software Defined Networking (SDN) are technologies that recently acquired a great momentum thanks to their promise of being a flexible and cost-effective solution for replacing hardware-based, vendor-dependent network middleboxes with software appliances running on general purpose hardware in the cloud. Delivering end-to-end networking services across multiple NFV/SDN network domains by implementing the so-called Service Function Chain (SFC) i.e., a sequence of Virtual Network Functions (VNF) that composes the service, is a challenging task. Collapse

Topics: Google+ user satisfaction artificial intelligence programming language lean manufacturing

Methods: experiment constraint programming modeling language minimum spanning tree problem mixed-integer programming

Optimal Dispatch of Volunteers to Out-of-hospital Cardiac Arrest Patients

2019 | HICSS | Citations: 0

Authors: Matinrad, Niki; Vogel, Nicklas Ennab; Granberg, Tobias Andersson; Angelakis, Vangelis

Abstract: Initiatives with mobile phone dispatched volunteers to out-of-hospital cardiac a ... Expand

Abstract: Initiatives with mobile phone dispatched volunteers to out-of-hospital cardiac arrest (OHCA) cases, can be found today in some countries, e.g. Sweden, the Netherlands, Switzerland and Italy. When an OHCA case is reported, an alarm is sent to the registered volunteers’ phones. However, the allocation of which volunteers to send to the automatic external defibrillator (AED) and who to send directly to the patient, is today based on simple rules of thumb. In this paper, we propose a model to optimally select how many and which volunteers to send directly to the patient, and who should pick up and deliver an AED. The results show that the model can help increase the survivability of the patients, compared to simple decision rules. Collapse

Topics: smartphone

Methods: optimization model case study survival function experiment mixed-integer programming

Service-oriented Application Composition with Evolutionary Heuristics and Multiple Criteria

2019 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Zo, Hangjung; Nazareth, Derek L.; Jain, Hemant K.

Abstract: The need to create and deploy business application systems rapidly has sparked i ... Expand

Abstract: The need to create and deploy business application systems rapidly has sparked interest in using web services to compose them. When creating mission-critical business applications through web service compositions, in addition to ensuring that functional requirements are met, designers need to consider the end-to-end reliability, security, performance, and overall cost of the application. As the number of available coarse-grain business services grows, the problem of selecting appropriate services quickly becomes combinatorially explosive for realistic-sized business applications. This article develops a business-process-driven approach for composing service-oriented applications. We use a combination of weights to explore the entire QoS criteria landscape through the use of a multi-criteria genetic algorithm (GA) to identify a Pareto-optimal multidimensional frontier that permits managers to trade off conflicting objectives when selecting a set of services. We illustrate the effectiveness of the approach by applying it to a real-world drop-ship business application and compare its performance to another GA-based approach for service composition. Collapse

Topics: business process management enterprise information system web service task characteristic workstation

Methods: computational algorithm case study optimization model integer programming trade-off analysis method