Promise Neighborhoods Research Articles

Promise Neighborhoods (ED)

Promise Neighborhoods (ED)

A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.

Research and Evaluation in a Child-Focused Place-Based Initiative: West Philly Promise Neighborhood.

Place-based initiatives attempt to reduce persistent health inequities through multisectoral, cross-system collaborations incorporating multiple interventions targeted at varying levels from individuals to systems. Evaluations of these initiatives may be thought of as part of the community change process itself with a focus on real-time learning and accountability. We described the design, implementation, challenges, and initial results of an evaluation of the West Philly Promise Neighborhood, which is a comprehensive, child-focused place-based initiative in Philadelphia, Pennsylvania. Priorities for the evaluation were to build processes for and a culture of ongoing data collection, monitoring, and communication, with a focus on transparency, accountability, and data democratization; establish systems to collect data at multiple levels, with a focus on multiple uses of the data and future sustainability; and adhere to grant requirements on data collection and reporting. Data collection activities included the compilation of neighborhood-level indicators; the implementation of a program-tracking system; administrative data linkage; and neighborhood, school, and organizational surveys. Baseline results pointed to existing strengths in the neighborhood, such as the overwhelming majority of caregivers reporting that they read to their young children (86.9%), while other indicators showed areas of need for additional supports and were programmatic focuses for the initiative (e.g., about one-quarter of young children were not engaged in an early childhood education setting). Results were communicated in multiple formats. Challenges included aligning timelines, the measurement of relationship-building and other process-focused outcomes, data and technology limitations, and administrative and legal barriers. Evaluation approaches and funding models that acknowledge the importance of capacity-building processes and allow the development and measurement of population-level outcomes in a realistic timeframe are critical for measuring the success of place-based approaches.

Improving Sampling Probability Definitions with Predictive Algorithms

Place-based initiatives often use resident surveys to inform and evaluate interventions. Sampling based on well-defined sampling frames is important but challenging for initiatives that target subpopulations. Databases that enumerate total population counts can produce overinclusive sampling frames, resulting in costly outreach to ineligible participants. Quantifying eligibility before sampling using machine learning algorithms can improve efficiency and reduce costs. We developed a model to improve sampling for the West Philly Promise Neighborhood’s biennial population-representative survey of households with children within a geographic footprint. This study proposes a method to estimate probability of study eligibility by building a well-calibrated predictive model using existing administrative data sources. Six machine-learning models were evaluated; logistic regression provided the best balance of accuracy and understandable probabilities. This approach can be a blueprint for other population-based studies whose sampling frames cannot be well defined using traditional sources.

A metaheuristic for the double traveling salesman problem with partial last‐in‐first‐out loading constraints

AbstractThis paper addresses the double traveling salesman problem with partial last‐in‐first‐out (LIFO) loading constraints. A vehicle picks up items in the pickup area and loads them into its container, a horizontal stack. Once all the pickup operations are done, the vehicle can deliver the items to the delivery area because pickup‐and‐delivery areas are geographically separated. Additionally, the loading and unloading operations also follow a partial LIFO policy. The aim is to find a Hamiltonian cycle that satisfies the loading and unloading policy described above in order to minimize the distance traveled by the vehicle in both areas and the cost of the rearrangements that are performed across the routes. The state‐of‐the‐art algorithm finds good solutions to the existing instances. However, finding good‐quality solutions in large instances requires longer computing times. Therefore, we propose an iterated local search that considers two additional components to the classical components, the intensification and restart procedure. The first helps in a more thorough examination of promising neighborhoods, while the second helps in the complete diversity of the solutions in the hopes of escaping the local optima. Moreover, a new unique decoding approach for the solution representation is proposed to effectively generate feasible solutions. The proposed algorithm's performance is evaluated on an existing set of instances and in two new additional sets of larger instances that are proposed. The computational results obtained on the three test sets show that the proposed algorithm outperforms the state‐of‐the‐art algorithm in terms of computing time required to find good‐quality solutions.

An Iterated Local Search Methodology for the Qubit Mapping Problem

The qubit mapping approach serves to transform a quantum logical circuit (LC) into a physical one that satisfies the connectivity constraints imposed by the noisy intermediate-scale quantum (NISQ) devices. The quality of the physical circuit generated by a mapping approach depends largely on the initial mapping, which specifies the correspondence between the qubits in the LC and the qubits on the NISQ device. There are a total of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n!$ </tex-math></inline-formula> different initial mappings for a qubit mapping problem with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> qubits, and among them, there is at least one initial mapping corresponding to the smallest physical circuit that this mapping approach can output. Finding such an initial mapping is very important for reliable computations on the NISQ device. To this end, we propose an iterated local search framework as well as a heuristic circuit mapper. In this framework, we perform multiple local searches on the space of initial mappings, and during each local search, several promising neighborhoods of the current initial mapping are generated and evaluated by invoking the circuit mapper in a forward or a backward manner. This framework provides a way for the qubit mapping approach to find the best physical circuit that it can produce, allowing it to trade time for circuit quality, which is necessary in the NISQ era. The experimental results demonstrate the stability, scalability, and effectiveness of this approach in reducing the number of additional gates. Moreover, although this approach is a multipass circuit mapping process, it can generate a good-quality physical circuit within half an hour, even for the circuit with more than 10 000 gates.

Addressing Engagement Suppression in Black and Brown Racialized Communities

Across the United States, the politics of power, voice, and funding continues to disparately impact the health and life outcomes of most of the Black and Brown residents. At the root of the disparities can often be found a conscious and unconscious cross-sector collaboration of historically White institutions to silence, exclude, and suppress the voices and engagement of communities of color in leadership, decision making, and community and economic development. This conceptual article is based on the work of Promise Neighborhoods of the Lehigh Valley (PNLV), introduces the Radical Welcome and Engagement Restoration Model (RWERM), a community practice framework that challenges structural and/or oppressive conditions and forces that suppress minority engagement. This article provides a complete description of the RWERM, highlights its theoretical assumptions, and explains how it can be implemented across the country. The potential impact of the program in Allentown, Pennsylvania, is discussed.

A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm’s convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (POF) shape, the proposed algorithm utilizes the complementary characteristics in the search area of the adversarial vector, and the two populations are alternately updated and co-evolved by adversarial search directions. Additionally, a novel particle update strategy is proposed to select promising neighborhood information to guide evolution and enhance diversity. To improve the ability to cope with environmental changes, an effective dynamic response mechanism is proposed, including three parts: archive set prediction, exploration of global optimal information, and retention of excellent particles to accelerate convergence to the Pareto optimal set (POS) in the new environment. The proposed algorithm is tested on a series of benchmark problems and compared to several state-of-the-art algorithms. The results show that ADNEPSO performed excellently in both convergence and diversity, and is highly competitive in dealing with dynamic problems.

A Comparison of IHEs and Non-IHEs as Anchor Institutions and Lead Agents of Promise Neighborhoods Projects

Place-based initiatives, such as the federal Promise Neighborhoods grant, attempt to coordinate interventions, supports, and services with a myriad of organizations to targeted communities. Although Institutions of Higher Education (IHEs), inclusive of academic medical institutions, are among the most overall researched anchor institution, Non-Institutions of Higher Education (NIHEs) have led more Promise Neighborhood grants since the inception of the program in 2010. Therefore, this study compared the revitalization efforts proposed by IHEs ( n = 5) and NIHE ( n = 5) in their applications for Promise Neighborhoods grants awarded between 2016 and 2018. Although similarities existed within and across the applications from NIHEs and IHEs, namely focused on improving academics and health/wellness, the specific interventions, supports, and services proposed by each lead institution largely reflected the individual needs of the targeted communities. The findings from this study illustrate how IHEs and NIHEs are similarly positioned to effectuate change within their communities. Implementing place-based initiatives requires anchor institutions to allocate considerable time and resources in order to adapt to the current needs of the community in real time. Therefore, future lead agents of Promise Neighborhoods should seek to promote an environment that fosters on-going collaboration and mutual trust across and within multiple stakeholders, while also exploring sustainability efforts to extend gains made beyond the duration of the grant.

Self-regulated differential evolution for real parameter optimization

This paper presents a novel differential evolution (DE) framework with a self-regulated neighborhood, termed self-regulated differential evolution (SrDE), for real parameter optimization. The novelty and advantage of SrDE are to present a self-regulated neighborhood (SrN) for learning, regulating, and using the neighborhood information of the population in guiding the search process of DE. Specifically, SrDE is characterized by the following three aspects. First, a one-dimensional self-organizing map (SOM) method is employed to dynamically construct the neighborhood relationships between individuals. Second, a self-sizing technique is applied to adaptively regulate the neighborhood size of each individual based on its search state. Third, a neighborhood path-assisted strategy is proposed to utilize promising neighborhood information for guiding the mutation process. Extensive experiments on a suite of real-parameter functions and real-world problems have demonstrated the superior and competitive performance of SrDE when compared with the state-of-the-art DE variants.

Disrupting Intergenerational Poverty Through a Place-Based Education Initiative : A Review of Promise Neighborhoods in their First Decade

This study explains the complexities of attainment, implementation, and evaluation of the U.S. Department of Education's Promise Neighborhood (PN) initiative. PNs seek to disrupt intergenerational poverty through a place-based education initiative that creates a pipeline of health, social, and education resources within an economically distressed neighborhood in an urban, rural, or tribal setting. The PN implementation grantees manage a network of school-community partnerships aiming to support students from cradle to career. This study includes an analysis of the complexities of PN attainment with a focus on the PN grant awardees; the complexities of PN implementation with a focus on PN management and services; and lastly, the complexities of evaluation with a focus on the PN evaluation factors. Recommendations for the future of the PN initiative are discussed.

THE POWER OF REVIEWS: HARNESSING RESEARCH ON COGNITIVE HEALTH

Networks harness powerful resources to tackle complex challenges. This symposium leverages the power of one such network to advance research in cognitive health. Using literature reviews, we seek to aid in the translation of knowledge on cognitive aging and health into practice. By documenting research successes and identifying gaps, we highlight areas in which important lines of research can be propelled forward. The papers together focus on three dynamics of research practice: communication among researchers, commonly-used research tools, and the need to develop valid and reliable measures. The first paper examines nomenclature that scientists use to describe cognitive health in their research reporting, and makes recommendations for alignment of important concepts. The second and third papers explore commonly-used tools in intervention research: one on the use of psychosocial assessments typically employed with individuals with ADRD, such as for depression and quality of life, and the other on tools used by educational and intervention programs to evaluate caregiver knowledge of dementia. Each paper suggests that consistent measurement practices would enhance interpretation of findings across studies. The final paper considers a promising research area, walkable neighborhoods and cognitive health, and argues for the importance of developing valid and reliable measures in emerging research. Taken together, these papers provide critical reflection on current practices in cognitive aging research and suggest directions in which to build future investigation. In addition, this collection of papers fulfills the clarion call of The Healthy Brain Roadmap for finding ways to improve the cognitive health of all adults.

Enhanced List-Based Simulated Annealing Algorithm for Large-Scale Traveling Salesman Problem

List-based simulated annealing (LBSA) algorithm is a novel simulated annealing algorithm where list-based cooling scheme is used to control the change of parameter temperature. Aiming to improve the efficiency of the LBSA algorithm for large-scale optimization problems, this paper proposes an enhanced LBSA (ELBSA) algorithm for solving large-scale traveling salesman problem (TSP). The ELBSA algorithm can drive more sampling at more suitable temperatures and from more promising neighborhoods. Specifically, heuristic augmented sampling strategy is used to ensure that more neighbors are from promising neighborhoods, systematic selection strategy is proposed to guarantee that each component of the current solution has a chance to be improved, and variable Markov chain length (VMCL), based on arithmetic sequence, is used to sample more neighbors at more suitable temperatures. Extensive experiments were performed to show the contribution of the heuristic augmented sampling strategy, and to verify the advantage of using systematic selection and VMCL. Comparative experiments, which were conducted on a wide range of large-scale TSP instances, show that the ELBSA algorithm is better than or competitive with most other state-of-the-art metaheuristics.

How Do Promise Neighborhoods’ Strategies Align With Research Evidence on Poverty and Education?

Promise Neighborhoods attempt to replicate the Harlem Children’s Zone’s “cradle to college pipeline” model of coordinated and continuous child and family services for a single neighborhood. We analyze the 46 planning and 18 implementation grants to determine which factors these neighborhoods plan to address and compare these efforts with research on poverty and academic performance. We conclude that Promise Neighborhoods more frequently focus on in-school factors and often fail to address many factors supported by prior research. We discuss the degree to which these neighborhoods are actually serving as all-encompassing cradle to college pipelines versus continuing current in-school efforts.

Place-Based Reform in the Context of Neighborhood Change: A Case Study of the Mission Promise Neighborhood

Nationwide, place-based initiatives aiming to improve school and community outcomes are in the midst of neighborhood demographic change. We explore this issue through a case study of the Mission Promise Neighborhood (MPN). We discuss how the social and educational context of MPN poses several challenges to implementing Promise Neighborhood reforms. Drawing on enrollment and residence data from San Francisco Unified School District (SFUSD) and the U.S. Census, we show that the MPN service population extends beyond the geographic boundaries of the neighborhood and includes two primary groups of increasingly unequal social and economic status: working-class Latinos and high-income Whites. We situate these findings within the context of SFUSD’s school assignment policy and gentrification in the Mission neighborhood. We conclude that complexities in the MPN service population have significant implications for MPN service provision and the definition of a “neighborhood community.” These implications apply to other place-based initiatives experiencing neighborhood demographic change.

Bigger data, less wisdom: the need for more inclusive collective intelligence in social service provision

Social service organizations have long used data in their efforts to support people in need for the purposes of advocacy, tracking, and intervention. Increasingly, such organizations are joining forces to provide wrap-around services to clients in order to “move the needle” on intractable social problems. Groups using these strategies, called Collective Impact, develop shared metrics to guide their work, sharing data, finances, infrastructure, and services. A major emphasis of these efforts is on tracking clients and measuring impacts. This study explores a particular type of Collective Impact strategy called Promise Neighborhoods. Based on a federal grant program, these initiatives attempt to close the achievement gap in particular geographic communities. Through an analysis of publicly available documents and information, the study analyzes the ways these strategies enact (and fail to enact) a collective intelligence for the common good. The analysis focuses specifically on issues surrounding data collection and use, youth agency, leadership and governance, and funding streams. Together, these foci develop a story of an increasingly used “intelligence” with a limited sense of “collective” and a narrow vision of a “common good.” Using this as a platform, the paper explores alternatives that might develop more robust practices around these concepts.

Rural Cross-Sector Collaboration

Schools throughout the United States apply comprehensive community partnership strategies to address students’ in- and out-of-school needs. Drawing from models like the Harlem Children’s Zone, Promise Neighborhoods, and full-service community schools, such strategies call for diverse professionals to reach beyond their own organizations to collaborate with complementary partners. Extant research on cross-sector collaboration focuses disproportionately on urban settings. This qualitative study examined three years of cross-sector collaboration in “Midvale,” a rural community in the western United States. Applying the conceptual framework of social frontiers, it illuminates how issues of difference, competition, and resource constraint impacted cross-sector collaboration in Midvale’s rural context.

How Do Promise Neighborhoodss Strategies Align with Research Evidence on Poverty and Education?

Promise Neighborhoods attempt to replicate the Harlem Children’s Zone’s “cradle to college pipeline” model of coordinated and continuous child and family services for a single neighborhood. We analyze the 46 planning and 12 implementation grants to determine which factors these neighborhoods plan to address and compare these efforts with research on poverty and academic performance. We conclude that Promise Neighborhoods more frequently focus on in-school factors and often fail to address many factors supported by prior research. We discuss the degree to which these neighborhoods are actually serving as all-encompassing cradle to college pipelines versus continuing current in-school efforts.

A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Different from most researches focused on the single objective hybrid flowshop scheduling (HFS) problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS) is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature.

Fast Micro-Differential Evolution for Topological Active Net Optimization.

This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.