Reduce Travel Distance Research Articles

Scheduling, Income, and School Closures: Unlocking the Key Drivers of Home Care Personal Support Unplanned Absences Through Time-to-Event Regression Analysis.

Healthcare workers' unplanned workplace absences are a global challenge with consequences for care recipients, employers, and healthcare systems. With rising home care demand, understanding drivers of home care personal support worker (PSW) absences can inform management of labor supply through government and employer policies. In this paper, we examined predictors of PSWs' unplanned absences within a large, administrative, longitudinal dataset (2019-2021) from a home care organization in Ontario, stratified by time (pre-, early-, and mid-pandemic). After an initial spike, unplanned absence rates were generally lower during the pandemic. Cox-proportional hazard regression models for unplanned absences highlighted how increasing income and reducing travel distance between visits can be expected to decrease the hazard of unplanned absences. School closures significantly correlated with unplanned absences, highlighting disruptions within the broader care economy. As demand for home care accelerates, reducing unplanned absences will improve care consistency for those relying on PSWs to remain safe at home.

Sustainable strategies for cost-effective temporary infrastructure management: insights from fairs in West Midnapore District, India

ABSTRACT Efficient management of temporary infrastructure is pivotal for the success and sustainability of periodic fairs and festivals. Coordination among stakeholders involved in procurement, supply, and timely delivery is essential. The involvement of multiple stakeholders results in varying cost engagements at different levels. This study proposes a centralised operational framework for infrastructure distribution, potentially reducing overall costs. Focusing on West Midnapore district, India, the research aims to introduce a sustainable and cost-effective strategy for temporary infrastructure management. Recognising the significance of transportation in managing temporary infrastructures, the study emphasises reducing travel distances and minimising site occupancy time to enhance cost efficiency. Using a linear regression model, the required infrastructural goods for fairs in the district are quantified. A delivery schedule is devised, employing vehicle route planning techniques to determine reduced truck travel distances. Comparing different distribution frameworks, the study finds that a centralised approach utilising three trucks is the most sustainable and cost-effective solution. Moreover, it examines and quantifies the social cost incurred when infrastructure items exceed the allowed fair duration in the current system. Implementing this proposed approach enables stakeholders to balance sustainability cost-effectiveness and build an efficient infrastructure supply chain management.

Social-defeat stress exposure during pregnancy induces abnormalities in spontaneous activity, sociality, and resilience to stress in offspring of mice

BackgroundEnvironmental stress during prenatal periods can lead to neurodevelopmental disorders. Psychosocial stress can be studied using the social-defeat stress (SDS) animal model. However, the effects of prenatal exposure to SDS on the behavior of mature offspring mice have not been clarified. The present study assessed the spontaneous activity and social interaction of pups born to mothers exposed to SDS during gestation, as well as their post-maturity responses to environmental stimuli, focusing on changes in anxiety-like behavior following restraint stress exposure. MethodsPregnant C57BL/6J mice were subjected to SDS for 4 days, from E12.5–E15.5, using aggressive male ICR mice. We assessed the mature offspring (after 10 weeks of age) born to these mothers for spontaneous activity, anxiety-like behavior, and social interactions, and evaluated their activity levels post-maturity following restraint stress exposure. ResultsThe open-field test (OF) indicated reduced travel distance and duration in the SDS group versus controls, whereas home-cage monitoring showed increased area traveled. In a novel environment, the SDS group showed a decrease in interest in unfamiliar mice. In a multiple-animal rearing environment, the SDS group showed an increase in the frequency and number of contact with other individuals. Movement duration in the OF following restraint stress reduced significantly from 30min to 4h in the control versus SDS group. ConclusionsPrenatal exposure to SDS can result in behavior resembling developmental disorders, impacting spontaneous activity and social interactions. Altered responses to stress suggest potential brain function abnormalities in offspring after maturation due to maternal SDS exposure.

PKR downregulation prevents copper-induced synaptic dysfunction and cognitive impairment in a murine model of Wilson's disease.

Synaptic efficacy is critical for memory formation and consolidation. Accumulating evidence suggest that synapses are impaired during Wilson's disease (WD), contributing to neuronal dysfunction and cognitive decline. WD is a prototypical condition among the copper metabolism disorders. Cognitive impairment is a common feature of affected patients with neurological symptoms, presenting as memory deficits, decreased cognitive flexibility, and impaired learning capabilities. These cognitive deficits can significantly impact the quality of life, affecting work and academic performance. However, the mechanisms mediating the inhibitory synaptic dysfunction in WD are incompletely understood. We investigated the effects of the double-stranded RNA-dependent protein kinase/eukaryotic initiation factor 2α (PKR/eIF2α) pathway on synaptic structure and function in WD using a murine model, toxic milk (TX mice). During mouse open-field tests, we noted a substantial rise in the mobility/immobility ratio among WD model animals compared to that in WT mice. Additionally, WD mice exhibited diminished central area exploration, as evidenced by reduced travel distance. Moreover, they displayed prolonged escape latency in the Barnes maze, suggesting that chronic copper accumulation is associated with neuropsychiatric alterations and cognitive impairment. We also found a decrease in the expression of synapse-associated proteins (synapsin 1, synaptophysin, postsynaptic density protein-93 [PSD93], postsynaptic density protein-95 [PSD95]), and vesicle-associated membrane protein2 [VAMP2]) besides abnormal neurotransmitter levels (including glutamate and GABA), indicating the presence of synaptic dysfunction in TX mice. Inhibiting PKR via C16 prevented these changes, suggesting that dysfunctional cognition is associated with the PKR/eIF2α pathway. We also observed changes in synapses, vesicles, dendritic spine density, and dendritic length that were associated with the presence of cognitive dysfunction. Further investigation revealed that C16 treatment decreased the TUNEL-positive cell numbers in the hippocampus of TX mice and prevented 8-OHdG-induced synaptic dysfunction. Results suggest that PKR downregulation prevents copper-induced synaptic dysfunction in the murine WD model. Therefore, targeting PKR pharmacologically may be a potential therapeutic strategy for treating the copper-induced neuropathology of patients with WD.

Pemodelan Sistem Pemilihan Tempat POI Terdekat di Wilayah Klaten Kota Menggunakan Metode Dijkstra oleh PT. Telkom Klaten

The Point of Interest (POI) represents a specific geographic location defined by its coordinates, including longitude and latitude, and holds value as a site of interest or utility. Examples of POIs include tourist attractions, hotels, restaurants, ATMs, pharmacies, health centers, retail shops, gas stations, and other categories integral to modern navigation systems. PT. Telkom Klaten has faced increased demands to conduct POI surveys efficiently, driven by advancements in technology that call for faster and more streamlined processes. A primary challenge in these surveys is the selection of an optimal route that minimizes time, costs, and fuel consumption, given that the company currently relies on manual input in Google Maps to determine distance and travel time. This manual approach may hinder survey efficiency and increase operational costs. To address this, an application employing the Dijkstra algorithm was developed to determine the shortest route effectively. The Dijkstra algorithm, known for selecting edges with minimal weight to connect sequential nodes, requires defined origin and destination points, thereby generating the most efficient route between them. This study applies the Dijkstra algorithm to optimize survey routes by modeling them as graph-based routes, aiming to identify the shortest and most efficient paths between multiple POI locations. The findings indicate that the Dijkstra algorithm significantly reduces travel distance and time, thereby achieving notable savings in fuel and operational time for POI surveys.

Perancangan Rute Optimal Distribusi Kargo Bahan Bakar Minyak dengan Metode Brute - Force Untuk Meminimasi Biaya Konsumsi Bahan Bakar Minyak Perusahaan Pelayaran PT XYZ

TSP of fuel oil distribution using tankers, the projected route search often does not contain the most minimal mileage, which can lead to adverse results such as significant additional costs. This research analyzes four different routes using several iterative proposals to reduce travel distance, time, and fuel costs compared to actual conditions. Problem solving is carried out using the Brute-Force method which enumerates all permutation results. The analysis results show that all proposed iterations were able to provide significant reductions in all three aspects. The four research routes achieved optimization results of 21,7% compared to the actual route modeling. This optimization proves that the iterative methods applied can substantially improve operational efficiency. Therefore, the determination of the tanker route must consider all the possibilities required in determining the route. The optimal route created is expected to have a new model with the aim of minimizing fuel costs based on the smallest distance and time calculations.

176 Reducing travel distances in Sport – good for people and the planet

176 Reducing travel distances in Sport – good for people and the planet

Exploring Factors Influencing Absenteeism and Academic Performance: A Study on Early Childhood and Primary Education (ECPE) Classes

This study delves into the various factors influencing absenteeism and academic performance among children enrolled in Early Childhood and Primary Education (ECPE) classes. Data was collected through fieldwork in 2011, focusing on demographics, attitudes of parents and teachers towards ECPE classes, methods used for retention of learners, roles of teachers in children’s performance, causes of absenteeism, and proposed solutions. The analysis revealed that poor teaching methods, inadequate structures, long distances to schools, weak administration, absenteeism of teachers, and harsh punishments contribute significantly to absenteeism among ECPE learners. These factors impact the academic performance of absentees, leading to subpar educational outcomes. Additionally, positive attitudes towards ECPE classes were found to correlate with better academic performance, emphasizing the importance of a conducive learning environment. Based on the findings, recommendations were made to address these challenges, including the elimination of harsh punishments, establishment of more schools to reduce travel distances, penalties for absent teachers, and the adoption of effective teaching methods. The study underscores the need for targeted interventions to improve attendance and enhance academic performance in ECPE classes. Keywords: Absenteeism, Academic performance, Early Childhood and Primary Education

Optimizing order picking processes in warehouses: strategies for efficient routing and clustering

The study employed a simulation based on a detailed dataset containing over 307,046 unique order identifiers and 1,050 unique product identifiers. This dataset included information such as order placement dates, product codes, quantities, and precise locations within the warehouse, including coordinates. The simulation modeled the order-picking route using the Single-Picker Routing Problem (SPRP) algorithms to minimize distance and travel time. The methods compared various wave-picking strategies and grouping methods (single-line and multi-line) for their effectiveness.The applied method significantly reduced the travel distance required by the order picker in the warehouse. The key to this optimization was consolidating orders into waves of specific sizes, achieving a fourfold distance reduction for the studied dataset. Additionally, the solution proposed grouping products by location within the warehouse, either in a single aisle or across multiple aisles based on proximity. Although this method often enhances efficiency, it did not in this particular case. However, it was included as it may yield better results with different datasets and further reduce travel distances in the warehouse. The research underscored the critical role of efficient routing and grouping strategies in warehouse operations. Although wave picking significantly reduced travel distances, the effectiveness of clustering strategies depended on the characteristics of the specific dataset, suggesting the need for tailored solutions based on the warehouse layout and the features of the orders. Future research could extend to integrating product volume and weight variations, which may further optimize order-picking strategies.

Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

Effective or useless? Assessing the impact of park entrance addition policy on green space services from the 15-min city perspective

This study investigates an innovative approach to enhance urban green space access by adding new park entrances rather than expanding the green space itself, aiming for sustainable urban development. Taking Shenzhen as a case study, we first developed a universal framework to identify potential locations for new entrances and used network analysis in ArcGIS to quantify green service improvements. Then, under the 15-min city concept, the impact on green accessibility and equity was assessed using the two-step floating catchment area (2SFCA) method and Gini coefficient analysis. Results indicate that (1) new entrances enabled 21.10% of residents to access parks with an average reduced travel distance of 214.71 m, and increased the population within a 15-min walk to parks by 7.34%. (2) This strategy proved cost-effective, serving 27.66% of residents at 1/38 the cost of creating new green spaces. (3) Furthermore, the accessible green area within a 15-min walking or cycling increased by 0.18 km2 and 1.42 km2, respectively, without affecting green equity. Our findings support the policy of adding park entrances as a feasible and efficient urban strategy, supporting Sustainable Development Goals (SDGs) 3 and 11, and the policy can be implemented across almost cities.

Assessing the Link between Maternal Transport Modes and Childhood Mortality in Nigeria

ObjectivesThe study explored the association between maternal transport modes and childhood mortalities in Nigeria.MethodUtilizing data and definitions from the 2018 Nigeria Demographic and Health Survey report, the ten-year early mortality rates of the five childhood mortalities and the percentage of live births in the 5 years before the survey, transported by eight identified means of transportation, were statistically correlated for each of Nigeria’s 36 states and the federal capital territory (FCT) in the R environment at a significance level of α < 0.05.ResultsIn the spatial distribution of the five childhood mortalities, a notable north-south dichotomy was observed, contrasting with the spatial spread of maternal transport modes. The five childhood mortalities exhibited a significant, moderately positive correlation with transportation by Private Car or Truck, while their associations with Public Transport or Bus and Walking were notably moderate but negative.Conclusion for PracticeWhile the use of private cars or trucks should be encouraged as a means of maternal transport, public transport should be better organized to provide efficient services to women who need such services for maternal and child healthcare. Additionally, steps should be taken to reduce travel distances to health facilities to manageable distances for mothers.

Trajectory Planning for Cooperative Double Unmanned Surface Vehicles Connected with a Floating Rope for Floating Garbage Cleaning

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water’s surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs.

Optimizing Routes for Sustainability: A Comparative Analysis of Parcel Distribution Methods in South Jakarta

This study focuses on improving the efficiency of PT. XYZ’s logistics services by examining the optimization of parcel distribution routes in South Jakarta. It compares the Nearest Neighbor Method and the Clarke and Wright Savings Method to determine the optimal strategy for delivering 53 packages to 8 locations. The Nearest Neighbor Method generates two routes, one encompassing 32 packages across four locations and the other encompassing 21 packages across four locations. This method significantly reduces travel distance, saving 27,3 kilometers in comparison to actual courier routes. The Clarke and Wright Savings Method generates two routes, one covering 35 packages across six locations and the other covering 18 packages across two locations. Compared to the actual van courier route, this method reduces the distance by 24,8 kilometers. The Nearest Neighbor Method reduces travel distances by 27.3 kilometers compared to actual routes, surpassing the Clarke and Wright Savings Method by 2.5 kilometers. Reduced travel distances reduce fuel consumption and greenhouse gas emissions, making logistics operations more sustainable on an environmental and economic level. This study concludes that the Nearest Neighbor Method is the optimal strategy for improving parcel distribution efficiency in South Jakarta, thereby substantially reducing the travel distances of PT. XYZ’s logistics operations.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

Sustainable waste collection optimization using electric vehicles

Solid waste management is a crucial municipal service directly influencing urban livability. Optimizing refuse collection routes enhances service efficiency and reduces costs. The recent introduction of fully electric refuse trucks, boasting features like zero emissions, low noise, and lower operating costs compared to diesel trucks, holds promise. However, their limited autonomy presents challenges that need addressing before widespread deployment. This study proposes a realistic formulation for the refuse collection routing problem with electric trucks, introducing electromobility constraints never considered before in related works. The goal is to find optimal routes covering all collection points while minimizing travel distance within vehicle battery and capacity limits. An accurate energy consumption model is presented, accounting for route elevation, vehicle speed, waste collection operations, and regenerative braking. Genetic algorithms are used to find solutions to the problem, as well as the well-known Clarke and Wright heuristic for comparison purposes. The algorithms are tested on six diverse instances reflecting real-world scenarios in New York City (United States) and Puerto Real (Spain). Results indicate the genetic algorithm’s superiority, yielding solutions with reduced travel distance and consumption while requiring fewer routes compared to the state-of-the-art heuristic.

Influence of Wetland and Landscape Characteristics on Freshwater Turtle Relative Abundance and Movement Patterns in West Virginia, USA

Wetland management practices often alter habitat characteristics to improve the function of the wetland (e.g., removing emergent vegetation for aesthetics or dredging for fish stocking), potentially at the cost of reducing habitat quality for wetland-dependent species such as freshwater turtles. We identified wetland and surrounding landscape characteristics related to painted turtle (Chrysemys picta) and snapping turtle (Chelydra serpentina) relative abundance and snapping turtle movement among wetlands. We surveyed turtles at 29 wetland sites (0.04–1.71 ha) in a mixed-use watershed in north-central West Virginia, USA, where hardwood forests and wetlands have been heavily fragmented by agriculture and roads. We also applied radio transmitters to 33 adult snapping turtles (17 females and 16 males) across 17 wetlands. Snapping turtle relative abundance was best estimated with mean substrate depth, mean wetland depth, and minimum distance from roads. Painted turtle relative abundance was best estimated with the null model. We documented movement among wetlands for 22 snapping turtles (67%), including 10 females and 12 males. The probability of inter-wetland movement decreased with increased minimum distance from wetlands. Our results suggest that the focal turtle species readily used shallow, mucky wetlands with deep substrate and that increasing the density of wetlands could increase snapping turtle population connectivity. Managers could consider restoring a diversity of wetland types that result in reduced travel distance between wetlands and that collectively have characteristics conducive to multiple species.

Optimizing Segregated Waste Collection Routes as a Decision-Making Problem in the Municipal Solid Waste Management System in Small Town

Routing vehicles is a generic decision-making problem of strategic management in any business serving customers in distributed locations with a limited fleet including municipal solid waste collection. This paper introduces an approach tailored for optimizing waste collection routes in small towns and rural areas where dead-end streets are typical in the road network. Leveraging the Traveling Salesperson Problem (TSP), the approach employs optimal route determination taking advantage of the specific character of a road network with many dead-end streets. The service area for waste collection was aggregated, so the graph representing the map results in a relatively small data instance. This facilitated the application of the exact method, i.e. Mixed-Integer Programming using the OpenSolver tool in Excel. Conducted in collaboration with a municipal agency in southeastern Poland, this study outlines a comprehensive methodology, emphasizing the reduction of complex dead-end streets into singular nodes within the graph for effective route optimization. A comprehensive methodology is outlined alongside the solutions attained. Computational experiments focused on minimizing total travel distance during waste collection operations, demonstrating the methodology's success. Beyond efficiency gains, optimized routes hold potential for significant environmental impact reduction. The reduced travel distances result in decreased fuel consumption and emissions, aligning with sustainability goals. Developing an Excel-based decision support tool for municipal solid waste management is a significant contribution, particularly for decision-makers less familiar with Operations Research. The tool's compatibility within the spreadsheet environment streamlines waste management processes for municipal units, enhancing decision-making efficiency in optimizing waste collection routes.

Enhancing Food Supply Chain Efficiency: A Consolidation Shipping Approach For Multi-Product and Multiechelon

This research applies the application of delivery consolidation in the context of a multi-product and multi-echelon food supply chain, as a strategy to address operational efficiency and cost management. The aim is to implement consolidation in multi-product and multi-echelon distribution systems, in order to optimize product placement and distribution efficiently. The method adopted is the Linear Programming model, which considers key variables such as the number of goods to be sent, delivery route, resource allocation, fleet capacity and delivery time limit. The research results show that shipment consolidation, by scheduling shipments that are delayed until the second or third day, allows combining orders into one large shipment, thereby increasing load and route efficiency, reducing trip frequency, and achieving significant transportation cost savings. This strategy successfully increased load and route efficiency, reduced the number of trips required, and resulted in substantial transportation cost savings. Using this method, the total shipping and storage costs are 1,328,834. This consolidation strategy has proven crucial in optimizing delivery types, in addition to facilitating more effective route planning, reducing travel distances and times, and resulting in significant operational cost savings.

Mengurai Kemacetan dalam Konteks Tata Ruang Perkotaan Yogyakarta

This study discusses the role of spatial planning in overcoming the increasingly worsening problem of traffic congestion in the urban area of Yogyakarta. This research explains how good spatial planning can contribute to reducing congestion, improving mobility and creating a more sustainable environment. The author explains that effective spatial planning must consider aspects such as residential location, land use patterns, transportation accessibility, and road infrastructure. The research results show that good spatial planning can optimize land use, reduce travel distances, and increase accessibility to various important facilities. In addition, this study also discusses the importance of community participation in the spatial planning process, because opinions and input from residents can help create solutions that are more appropriate to local needs and reduce resistance to change. The results of this research provide a better understanding of how wise spatial planning can help overcome traffic congestion, improve the quality of life in the Yogyakarta urban area, and support sustainable development. The policy implications drawn from this research can provide important guidance for policymakers, urban planners, and the public in joint efforts to face the challenges of congestion in other cities in the future.