New Variations of the Online <em>k</em>-Canadian Traveler Problem: Uncertain Costs at Known Locations

In this chapter, we study new variations of the online k-Canadian Traveler Problem (k-CTP) in which there is an input graph with a given source node O and a destination node D. For a specified set consisting of k edges, the edge costs are unknown (we call these uncertain edges). Costs of the remaining edges are known and given. The objective is to find an online strategy such that the traveling agent finds a route from O to D with minimum total travel cost. The agent learns the cost of an uncertain edge, when she arrives at one of its end-nodes and decides on her travel path based on the discovered cost. We call this problem the online k-Canadian Traveler Problem with uncertain edges. We analyze both the single-agent and the multi-agent versions of the problem. We propose a tight lower bound on the competitive ratio of deterministic online strategies together with an optimal online strategy for the single-agent version. We consider the multi-agent version with two different objectives. We suggest lower bounds on the competitive ratio of deterministic online strategies to these two problems

Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents

Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents

Online routing and scheduling of search-and-rescue teams

We study how to allocate and route search-and-rescue (SAR) teams to areas with trapped victims in a coordinated manner after a disaster. We propose two online strategies for these time-critical decisions considering the uncertainty about the operation times required to rescue the victims and the condition of the roads that may delay the operations. First, we follow the theoretical competitive analysis approach that takes a worst-case perspective and prove lower bounds on the competitive ratio of the two variants of the defined online problem with makespan and weighted latency objectives. Then, we test the proposed online strategies and observe their good performance against the offline optimal solutions on randomly generated instances

Nurse Practitioner-Led Care Pods: A Team Communication Enhancement Model

Background: Communication has become a key performance measure in the shift to value-based healthcare. Given the impact of communication failures on patient harm, length of stay, and dissatisfaction with care, new models of care with better communication through structured teamwork and interdisciplinary collaboration are needed. Problem: In a 16-bed geriatric medical/surgical unit of a New York City multispecialty community hospital, the workflow structure unintentionally created inconsistent handoff communication, gaps in continuity of care, missed care events, and inattention to the patient’s priorities in the care plan. A gap analysis identified communication deficiencies that impacted team effectiveness and patient care outcomes. Methods: Patient perceptions of care and staff perceptions of teamwork were assessed pre-and post-intervention for the effects of implementing structured team communication in a nurse practitioner (NP) medical management model. Responses were collected with the NRC Health Patient Experience Survey and the AHRQ TeamSTEPPS® Teamwork Perceptions Questionnaire (T-TPQ). Patient experience scores for the NP-led unit, a resident-led unit, and a physician-assistant led unit were compared. Interventions: Implementation of an NP-Led Care Pod model was evaluated over three months. An education session on structured communication tools prepared NP-Led Care Pod teams in role-based purposeful rounds, bedside shift reports, structured bedside interdisciplinary team rounds, and TeamSTEPPScommunication strategies. Results: Teamwork perception scores post-education and post-implementation fell short of the aim for a 10% increase from baseline. Patient experience survey scores increased 71.6% from baseline at two months for care team explanations, 128% for listening carefully, and 71.6% for perceived staff communication. Although not sustained, all scores were 14% higher than baseline. Team members reported increased team support, inclusion, and role satisfaction. Patients asked fewer questions about their care plans. Conclusion: The NP-Led Care Pods contributed to evidence on the effectiveness of NP-led, team-based care, with implications for nursing leadership and team communication. The NP-Led Care Pod environment improved workflow, team dynamics, and staff communication. Further studies may benefit from using measures to capture improvement in patient safety and patient experience domains that were not addressed in this project. Keywords: acute care nursing, bedside handoff, collaboration, communication, interprofessional, patient-centered care, purposeful rounding, team perceptions, TeamSTEPPS

Online Failure Diagnosis in Interdependent Networks

Motivation. In interdependent networks, nodes are connected to each other with respect to their failure dependency relations. As a result of this dependency, a failure in one of the nodes of one of the networks within a system of several interdependent networks can cause the failure of the entire system. Diagnosing the initial source of the failure in a collapsed system of interdependent networks is an important problem to be addressed. We study an online failure diagnosis problem defined on a collapsed system of interdependent networks where the source of the failure is at an unknown node (v). In this problem, each node of the system has a positive inspection cost and the source of the failure is diagnosed when v is inspected. The objective is to provide an online algorithm which considers dependency relations between nodes and diagnoses v with minimum total inspection cost. Methodology. We address this problem from worst-case competitive analysis perspective for the first time. In this approach, solutions which are provided under incomplete information are compared with the best solution that is provided in presence of complete information using the competitive ratio (CR) notion. Results. We give a lower bound of the CR for deterministic online algorithms and prove its tightness by providing an optimal deterministic online algorithm. Furthermore, we provide a lower bound on the expected CR of randomized online algorithms and prove its tightness by presenting an optimal randomized online algorithm. We prove that randomized algorithms are able to obtain better CR compared to deterministic algorithms in the expected sense for this online problem

Engineering Responses to Pandemics

Focusing on pandemic influenza, this chapter approaches the planning for and response to such a major worldwide health event as a complex engineering systems problem. Action-oriented analysis of pandemics requires a broad inclusion of academic disciplines since no one domain can cover a significant fraction of the problem. Numerous research papers and action plans have treated pandemics as purely medical happenings, focusing on hospitals, health care professionals, creation and distribution of vaccines and anti-virals, etc. But human behavior with regard to hygiene and social distancing constitutes a first-order partial brake or control of the spread and intensity of infection. Such behavioral options are “non-pharmaceutical interventions.” (NPIs) The chapter employs simple mathematical models to study alternative controls of infection, addressing a well-known parameter in epidemiology, R0, the “reproductive number,” defined as the mean number of new infections generated by an index case. Values of R0 greater than 1.0 usually indicate that the infection begins with exponential growth, the generation-to-generation growth rate being R0. R0 is broken down into constituent parts related to the frequency and intensity of human contacts, both partially under our control. It is suggested that any numerical value for R0 has little meaning outside the social context to which it pertains. Difference equation models are then employed to study the effects of heterogeneity of population social contact rates, the analysis showing that the disease tends to be driven by high frequency individuals. Related analyses show the futility of trying geographically to isolate the disease. Finally, the models are operated under a variety of assumptions related to social distancing and changes in hygienic behavior. The results are promising in terms of potentially reducing the total impact of the pandemic

Using Education to Promote Waste Segregation and Waste Reduction in the Operating Room: A Quality Improvement Initiative

Background: The operating room (OR) is known to be one of the largest producers of medical waste and of the surgical specialties, orthopedic surgery tends to the largest portion of this. Waste from the OR can be mitigated by recycling non-contaminated materials. This reduces the number of raw materials needed to create new products, decreases landfill use, and lowers hospital costs. Local Problem: It was observed that a gap in knowledge existed related to recyclables in the OR of a large medical center in New England. Also, it was observed that there was potential for waste and recycling to be more accurately segregated and for blue wrap to be separated from general recycling. Specific Aims: The aim was to increase circulating nurses’ and surgical technologists’ confidence in knowledge of recyclables in the operating room. Additionally, the aim was to increase the proportion of recycling by 15%, increase the accuracy of recycling to 95%, and increase the accuracy of blue sterilization wrap segregation to 100%. Methods: The Plan-Do-Study-Act (PDSA) model was used for implementation. Pre- and post-education waste audits of primary total knee arthroplasties were performed to assess recycling, blue wrap, and waste. Pre- and post-education surveys were utilized to assess staff confidence. Interventions: An educational presentation was given to orthopedic surgical service circulating nurses and surgical technologists. Additionally, a visual poster board was displayed in the staff breakroom. Results: Survey data indicated an increase between pre- and post-education confidence in staff, which met the specific aim. Waste audit data showed a 26% increase in pre-operative proportions of recycling and an approximate 4% decrease in blue wrap by weight. There was a decrease in recycling accuracy by 6.6% but an increase of about 25% in the accuracy of blue wrap segregation post-intervention. Conclusion: Education for a subset of perioperative staff aided in increased confidence in recycling in the OR. Further improvements in staff confidence, recycling, and blue sterilization wrap segregation throughout surgical specialties may be possible through additional education and physical resources