Use of Physical Accessibility Modelling in Diagnostic Network Optimization: A Review

Diagnostic networks are complex systems that include both laboratory-tested and community-based diagnostics, as well as a specimen referral system that links health tiers. Since diagnostics are the first step before accessing appropriate care, diagnostic network optimization (DNO) is crucial to improving the overall healthcare system. The aim of our review was to understand whether the field of DNO, and especially route optimization, has benefited from the recent advances in geospatial modeling, and notably physical accessibility modeling, that have been used in numerous health systems assessment and strengthening studies. All publications published in English between the journal’s inception and 12 August 2021 that dealt with DNO, geographical accessibility and optimization, were systematically searched for in Web of Science and PubMed, this search was complemented by a snowball search. Studies from any country were considered. Seven relevant publications were selected and charted, with a variety of geospatial approaches used for optimization. This paucity of publications calls for exploring the linkage of DNO procedures with realistic accessibility modeling framework. The potential benefits could be notably better-informed travel times of either the specimens or population, better estimates of the demand for diagnostics through realistic population catchments, and innovative ways of considering disease epidemiology to inform DNO.

Universal Decision Support System for Selection of MSW Route Optimization Method

Municipal Solid Waste (MSW) collection utilizes the highest percentage of the MSW management budget. Additionally, choosing a vehicle route optimization method is complex, difficult and does not always yield the most practical approach. There is limited published information about a decision support system (DSS) that assists in selecting the appropriate route optimization algorithm. This study aims to design and develop a universal DSS framework that suggests effective route optimization method(s). The system consists of 21 optimization data items and four criteria that assess the available constraints and recommends the most suitable optimization method(s). The DSS prototype was validated by testing it on the available literature and observing if the suggested method by the system complies with that utilized by the researchers. It was found that the system was able to predict the method which is used in 73% of studies. Moreover, the system suggested an enhanced version of the methods used in 18% of studies. It could be concluded that the proposed framework can help to select the best algorithms in almost all existing scenarios that have been used during development. Therefore, it is recommended to use the framework for selecting the appropriate route optimization algorithm for MSW collection.

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers).