An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: architecture and evaluation

Inter-cloud is an approach that facilitates scalable resource provisioning across multiple cloud infrastructures. In this paper, we focus on the performance optimization of Infrastructure as a Service (IaaS) using the meta-scheduling paradigm to achieve an improved job scheduling across multiple clouds. We propose a novel inter-cloud job scheduling framework and implement policies to optimize performance of participating clouds. The framework, named as Inter-Cloud Meta-Scheduling (ICMS), is based on a novel message exchange mechanism to allow optimization of job scheduling metrics. The resulting system offers improved flexibility, robustness and decentralization. We implemented a toolkit named “Simulating the Inter-Cloud” (SimIC) to perform the design and implementation of different inter-cloud entities and policies in the ICMS framework. An experimental analysis is produced for job executions in inter-cloud and a performance is presented for a number of parameters such as job execution, makespan, and turnaround times. The results highlight that the overall performance of individual clouds for selected parameters and configuration is improved when these are brought together under the proposed ICMS framework

Ready To Roll: Southeastern Pennsylvania's Regional Electric Vehicle Action Plan

On-road internal combustion engine (ICE) vehicles are responsible for nearly one-third of energy use and one-quarter of greenhouse gas (GHG) emissions in southeastern Pennsylvania.1 Electric vehicles (EVs), including plug-in hybrid electric vehicles (PHEVs) and all-electric vehicles (AEVs), present an opportunity to serve a significant portion of the region's mobility needs while simultaneously reducing energy use, petroleum dependence, fueling costs, and GHG emissions. As a national leader in EV readiness, the region can serve as an example for other efforts around the country."Ready to Roll! Southeastern Pennsylvania's Regional EV Action Plan (Ready to Roll!)" is a comprehensive, regionally coordinated approach to introducing EVs and electric vehicle supply equipment (EVSE) into the five counties of southeastern Pennsylvania (Bucks, Chester, Delaware, Montgomery, and Philadelphia). This plan is the product of a partnership between the Delaware Valley Regional Planning Commission (DVRPC), the City of Philadelphia, PECO Energy Company (PECO; the region's electricity provider), and Greater Philadelphia Clean Cities (GPCC). Additionally, ICF International provided assistance to DVRPC with the preparation of this plan. The plan incorporates feedback from key regional stakeholders, national best practices, and research to assess the southeastern Pennsylvania EV market, identify current market barriers, and develop strategies to facilitate vehicle and infrastructure deployment

Visibility based hospital inpatient unit design.

Patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Healthcare literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. This dissertation fills significant voids in this domain and adds much-needed realism to develop insights that hospital decision-makers can use to design their inpatient unit layout. Our first contribution (Chapter 2) adopts an interdisciplinary approach that combines the human field of regard with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient\u27s bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-, I-, and R-shaped; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visibility of a target in a room when viewed by the nurse from the nursing station. This algorithm considers nurses\u27 horizontal visual field and their depth of vision. We also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions to the bi-objective optimization model. Our findings suggest that the R-shaped layout outperforms the other two layouts on these visibility-based objectives. Further, the position of the patient\u27s bed plays a role in maximizing the visibility of the patient\u27s room. In our second contribution, we extend the model in the first contribution to now include position of the bed in patient rooms as a decision variable and consider various door positions. We consider four distinct layout types, L–shaped, U-shaped, R-shaped, and I-shaped, with eight patient rooms and a nurse-to-patient ratio of 1:4. We propose an ε-constrained approach to convert the corresponding bi-objective optimization model into a single objective optimization model, prioritizing equity as an objective function. We propose a progressive refinement algorithm to solve this optimization model within a reasonable time. Our findings suggest that a significant improvement in the equity score of a layout can be obtained through the joint determination of patient beds and nurse positions. We also perform a comparative analysis of equity offered by various layout types and observed that angular layout types are a promising output. We also observed that higher spatial distance between nurses is beneficial in achieving higher equity measures when obstruction is high in the case of angular layouts. There are several implications of our findings to practice. The insights from our study related to the impact of layout shapes, bed locations, and obstruction levels on patient visibility can help decision-makers in both greenfield and retrofitting of existing inpatient unit layout designs. Our models can quickly identify highly visible layouts, avoiding costly trial and error in layout changes. Improved decision-making in inpatient unit design will facilitate better patient experiences through equitable visibility distribution and enhanced quality of care

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms