Collection Routes Research Articles

Application of the Clark–Wright Method to Improve the Sustainability of the Logistic Chain

The incessant consumption of goods and materials underscores the need to address the growing problem of waste generation and its profound impact on environmental sustainability. The problem of waste removal can be approached in different ways, whether it is the routing of vehicles, the work of drivers, the optimal distribution of waste bins, or other matters in the entire waste process. The aim of this study is to investigate the possibilities of optimizing waste collection processes in the region using a slightly modified Clark–Wright method. Optimal waste collection routes are defined with a focus on cost reduction and overall optimization of logistic chain processes. The established mathematical model for the capacitated vehicle routing problem includes the principles of sustainability and environmental friendliness. The results indicate that the largest messenger of all the newly proposed routes are the routes containing the surrounding settlements. Newly designed routes lead to significant reductions in fuel consumption and vehicle maintenance, which has a positive impact on financial and environmental resources. The conclusion indicates that by applying the Clark–Wright method, we have achieved a reduction in the number of routes of twenty fewer routes. This study provides regions with a detailed plan to improve waste management practices, contributing to a future of increased sustainability and environmental awareness.

Evaluation of municipal solid waste (MSW) generation rates: A case study in the district of Cumba, Northern Peru

The objective of this research was to evaluate the unit rates of MSW generation in Cumba in the years 2016 and 2022. The calculations were based on the weights of the MSW disposed in the dump located 5 km from the city of Cumba since 2012. The GPC, physical composition, density, humidity were determined in the years 2016 and 2022, studied according to the methodology and group classification of Peruvian regulations. The results show that 5.45 Tn/day−1 are generated in 2016, 4.37 Tn/day−1 in 2022; according to its physical composition, 82% RO, 14% MICVC and 4% MISVC in 2016; 77% RO, 16% MICVC, 7% MISVC in 2022; density 137.90 kg/m−3 in 2016 and 172.69 kg/m−3 in 2022; humidity 67.67% in 2016 and 63.43% in 2022. It was also found that in 100.00% there is no solid waste treatment; Everything generated in homes, businesses and streets is evacuated to the final disposal site, which is a dump. In 2022, Cumba acquired 10 hectares to have adequate sanitary infrastructure and begin the closure and recovery of its current dump. This study will contribute to providing accurate data on MSW generation that allows the local government to promote the optimization of collection routes and schedules, resulting in cost savings and reduction of carbon emissions in the Amazon Region. Therefore, it is necessary to raise awareness at all levels of society through various means of communication and education, so that the risks of spreading health risks can be minimized by improving MSW management.

A-185 Impact Assessment of Centrifugation During Transportation on Examination Results

Abstract Background Laboratories are increasingly challenged to seek solutions to expedite the collection process and provide more convenience to patients. This study aims to validate the centrifugation of samples in transport cars for home collections without need to return to the basic unit after each sample collection for centrifugation using the traditional method for up to 2 hours, as recommended in the literature, providing an increase in the number of patient appointments and a greater portfolio of exams. Methods The study was carried out evaluating measurands that represent different methodologies and sample stability challenges, namely, PTH, free T4, testosterone, CA19.9, anti-HBS, anti-transglutaminase, LDH, potassium, glucose, sodium and alkaline phosphatase( FAL). The centrifuge used during sample transport was the Drucker, model Horizon - 6 Flex, calibrated and monitored during the study. Forty samples from volunteer patients were analyzed, with one tube centrifuged in the equipment installed in the car and a second tube in the basic unit's centrifuge within 2 hours of collection, both respecting the 30-minute clot retraction period. All tubes were sent to the same laboratory for execution. Results Throughout the study period, the calibration of the car's centrifuge remained stable. In the correlation, the variance, calculated by the F Test, showed an adequate result, below 1.69. The difference between the means of the test method and the comparative method calculated through the T Test was below the Critical T. The Pearson correlation for the mensurands Free T4, CA19.9, anti-transglutaminase, LDH, potassium, sodium and glucose was lower than the accepted limit of 0.950. Agreement analysis and Kappa index were performed and the data were evaluated from a qualitative point of view by the medical team, who considered the differences to have no clinical impact on the patient. PTH, CA19.9 and FAL were approved by the difference graph.In relation to glucose, the correlation between the serum-gel tube and fluoride was also evaluated, where we obtained lower results with the fluoride tube due to hemolysis caused by the additive. During the analysis of LDH results, a tendency to decrease the concentration of its dosages over time can be observed, and in this case there were lower results in samples centrifuged in the basic unit, due to the longer interval between collection and centrifugation. Conclusions After evaluation from a pre-analytical, quality and medical point of view, centrifugation from home samples collection directly in the car will allow a greater number of patients to be served, as there will be no need to return to the basic unit for centrifugation every 2 hours. It will also bring greater safety in centrifuging the sample at the correct time, avoiding any delays that may occur, mitigating potential deviations in results and greater process efficiency as the collector will not need to return to the basic unit for centrifugation, right during During the collection route, the samples will already be prepared for sending directly to the execution site. For glucose and LDH tests, it was possible to observe efficiency gains due to centrifugation 30 minutes after collection.

Economic and environmental benefits of optimized waste transportation routes in Khulna

Urban transportation is a big source of pollution and greenhouse gas emissions. Khulna City handles the waste of 1.23 million people daily, using Heavy Duty Diesel Vehicles (HDDV) that release major environmental pollutants. The current waste transportation routes lack proper planning, leading to extra fuel consumption and pollutant emissions. This study aims to reduce fuel consumption and socio-environmental costs by improving waste transportation routes. Present collection routes, collecting points, and the speed of 30 waste collector vehicles were tracked using GPS devices for ten months between July 2023 and April 2024. The network dataset was modified according to four types of waste collector vehicles. ArcGIS Pro was used to find the optimal paths and their lengths. Fuel usage and prices were estimated using simple fuel consumption rate calculations. The lengths of current and optimal routes were used in COPERT to measure pollutant emissions. The results show that improved routes lower fuel consumption costs by approximately USD 55.39 per day and CO2 emissions by about 38.242 tonnes per year. Reductions in CO, CH4, NMVOC, NO2, PM10, and PM2.5 were roughly 9.3%, 9.53%, 9.21%, 9.29%, 9.35%, and 9.3%, respectively. This study demonstrates a comprehensive approach for improving solid waste management covering both financial efficiency and environmental effects.

Urban growth and waste services: exploring scenarios in an inter-municipal cooperative framework in Sogamoso city, Colombia

ABSTRACT The spatial dimensions of city expansion in Latin America reveal the emergence of new urban agglomeration and metropolization phenomena. In this context, the goal is to implement a predictive model of land use change between 1976 and 2047 in the city of Sogamoso and neighboring municipalities in Colombia and to apply its results to a proposal for inter-municipal cooperation in the collection of solid household waste. The methodology used is based on a spatially explicit models using the CA_Markov model in Idrisi Selva software that incorporates Markov chains, multi-criteria evaluation, and cellular automata to identify future areas of urban expansion. The model forecasts urban growth from 2,420 ha in 2021 to 5,278 ha in 2047. The spatial distribution of the new urban areas implies collection routes that are ever farther away from the main urban centers. There is thus a vital need for new regional planning practices (such as inter-municipal cooperation).

Urban digital twin-based solution using geospatial information for solid waste management

With over 2 billion metric tons generated annually, global solid waste production has severe environmental consequences. Although it is not a primary Sustainable Development Goal, effective solid waste management (SWM) is vital for meeting targets 11.6, 12.4, and 12.5. South Africa, in particular, deals with significant SW generation and inadequate collection services. Therefore, this paper presents an Urban Digital Twin (UDT) prototype to tackle these issues, involving stakeholder prioritization, integrating real-time monitoring, citizen participation, waste generation simulations, optimized collection routes, and a control Dashboard where stakeholders’ system requirements were included. The UDT proposes optimized collection routes to reduce fuel use, operational costs, and emissions. The stakeholders’ opinions on the usefulness of the UDT varied due to their backgrounds and skills. Most of them appreciated the Dashboard visualization and the UDT possibilities for resource optimization. The performance of the UDT depends on computer capacity and local or online processing. This UDT prototype sets the foundation for digital twinning in SWM, scalable to different areas, vehicles, and production levels. The proposed approach, citizen involvement, and multi-stakeholder engagement enhance the SWM, benefiting resource-limited countries.

Production and changeover control of textile and PET recycling

Circular economies have become a strong candidate for addressing environmental challenges by managing end-of-life products to reduce landfill waste. We herewith focus on the recycling of PET from plastic waste and textiles. This paper focuses on recycling PET from plastic waste and textiles and proposes a model for controlling plant operations, emphasizing Quality-Based Changeovers over cleaning to ensure production continuity. This paper also identifies technological and managerial challenges in PET recycling plants as raised in the related literature, such as the need for technology improvement, more effective collection routes and sorting processes, and devoted managerial strategies. Since a relevant industrial need is to manage the changeovers, we develop a model to control the operative management of the plant and to find the feedstock quality to be processed at a given time for profit maximization. A discrete event simulation model is built to represent the behavior of the system under an approximate state-based control policy, i.e. a two-threshold policy. Numerical results and sensitivity analysis highlight the impact of cleaning costs on system behavior, offering insights into optimal operational conditions.

WASTE COLLECTION AND TRANSPORT OPTIMIZATION IN ACCORDANCE WITH ZERO WASTE PRINCIPLES OF KARAMAN PROVINCE

The zero waste approach introduces a novel perspective to waste management, allowing for the categorization of waste at its origin. This enables each identified waste group to be assessed and optimized individually. One of these optimization studies is the optimization of waste collection and transportation. Container numbers, placement and collection routes of vehicles are very important for optimizing collection and transportation. In this study, the number of containers placed in Karaman, their placement, the distance between the containers and the optimum distances that should be and the vehicle route optimization were made. The analysis of the number of containers and their placement in Karaman shows us that the number of containers is not used properly and the distance between the containers is very short (33 meters). Moreover, studies on optimizing routes in five pilot areas have led to an average efficiency gain of 32%, characterized by saving 17 kilometers, 50 minutes, and 8 liters of fuel per route. Based on these findings, a 32% improvement is anticipated not only in economic and time efficiency but also in reducing the carbon footprint during the collection and transportation stages.

Research on a Matching Method for Vehicle-Borne Laser Point Cloud and Panoramic Images Based on Occlusion Removal

Vehicle-borne mobile mapping systems (MMSs) have been proven as an efficient means of photogrammetry and remote sensing, as they simultaneously acquire panoramic images, point clouds, and positional information along the collection route from a ground-based perspective. Obtaining accurate matching results between point clouds and images is a key issue in data application from vehicle-borne MMSs. Traditional matching methods, such as point cloud projection, depth map generation, and point cloud coloring, are significantly affected by the processing methods of point clouds and matching logic. In this study, we propose a method for generating matching relationships based on panoramic images, utilizing the raw point cloud map, a series of trajectory points, and the corresponding panoramic images acquired using a vehicle-borne MMS as input data. Through a point-cloud-processing workflow, irrelevant points in the point cloud map are removed, and the point cloud scenes corresponding to the trajectory points are extracted. A collinear model based on spherical projection is employed during the matching process to project the point cloud scenes to the panoramic images. An algorithm for vectorial angle selection is also designed to address filtering out the occluded point cloud projections during the matching process, generating a series of matching results between point clouds and panoramic images corresponding to the trajectory points. Experimental verification indicates that the method generates matching results with an average pixel error of approximately 2.82 pixels, and an average positional error of approximately 4 cm, thus demonstrating efficient processing. This method is suitable for the data fusion of panoramic images and point clouds acquired using vehicle-borne MMSs in road scenes, provides support for various algorithms based on visual features, and has promising applications in fields such as navigation, positioning, surveying, and mapping.

A Heuristic Procedure for Improving the Routing of Urban Waste Collection Vehicles Using ArcGIS

This paper proposes a heuristic procedure created to improve the collection routes obtained with the support of the ArcGIS software and its complement, Network Analyst. After a series of experiments, it was found that the software presents several inconsistencies with logistical and operational management concepts, such as the unnecessary realization of U-turns in a dead end and unnecessary access to areas with difficult access to a single customer. These are issues that a collection route planner must consider to make a good decision that considers the cost of visiting areas with difficult access and the benefits of reaching that area. In this sense, our heuristic procedure considers a set of best practices in operational and logistical strategies that could be programmed within the Network Analyst. As it is well known in the science of vehicle routing, U-turns and sub-tours in the routes travelled by vehicles increase distances and operating costs, so in our work, we propose a systematic heuristic procedure to reduce the number of U-turns performed by a municipal waste collection truck, while using the ArcGIS-Network Analyst add-on to reduce the number of sub-tours in the route under the Directed-Capacitated Arc Routing Problem approach. It is then shown how the routes improved using our conceptual heuristic procedure, which provides better quality than the original routes obtained with ArcGIS and Network Analyst. Specifically, reducing the total distances travelled by the vehicle fleet, increasing the coverage of sidewalks visited by the truck in the urban perimeter of a city, and minimizing the time used in municipal solid waste collection operations. The importance of our work lies in the fact that effective MSW management is an essential municipal service whose regulation can drive innovation, sustainability, and efficiency in the MSW sector.

Research on Ecoguard Solar Powered IOT for Efficient Garbage Tracking

Abstract: The "Eco Guard" project offers a novel way to improve the effectiveness of waste tracking and management by utilizing solar-powered Internet of things technology coupled with an ESP32 microcontroller. The inability of traditional waste management systems to precisely monitor waste levels and optimize collection routes results in resource waste and negative environmental effects.The Eco Guard project seeks to overcome these obstacles by putting in place a system that powers Internet of Things (IoT) devices with sensors for monitoring waste levels in real-time using renewable solar energy. The central processing unit is the ESP32 microcontroller, which makes it easier to gather, process, and send data to a cloud platform or centralized server.The Eco Guard project's salient characteristics comprise:Solar-Powered Mechanism: Utilizing solar energy guarantees continuous

Optimising Urban Waste Management Through Smart Monitoring Systems: A Case Study in Enhancing Efficiency and Sustainability

Abstract This paper illustrates the significant potential of advanced waste management strategies, particularly the implementation of a trash level monitoring system, to enhance urban sustainability and operational efficiency. This research rigorously evaluates the superiority of real-time data-driven systems over conventional waste management practices by leveraging a dynamic model to simulate trash fill levels and employing algorithms to identify optimal collection routes.

Development of Automatic Object Detection and IoT for Garbage Pickup Assignment Problem

Waste management remains a challenge in certain cities, particularly in allocating fleets responsible for collecting garbage from temporary disposal sites. Inadequate planning can lead to the accumulation of substantial waste piles. This study aims to enhance truck assignment by considering truck capacity and the collection route. The assignment process incorporates the fundamental concept of the transportation problem, precisely the northwest corner method. The volume of waste transported aligns with the resident or industrial population within the designated service area. The waste generation capacity determines the future fleet and quantity, forming a crucial element of the ensuing distribution channel. A monitoring system integrating object detection and the Internet of Things (IoT) has been devised to ensure effective garbage collection. Cameras strategically positioned at temporary disposal sites transmit real-time images. The system evaluates garbage collection capacity through object detection facilitated by neural network training. The research outcomes demonstrate the system's capability to identify waste pile levels and validate the garbage pickup process by the designated fleet. Future research should focus on assignment and scheduling in waste transportation, enabling fleet allocation within specific timeframes. Additionally, an object detection algorithm refinement is necessary for more precise identification of waste pile locations.

GreenRoute Ballari: A Hybrid Approach for Optimizing Waste Collection Routes Using K-Means Clustering and Genetic Algorithm

Abstract: The exponential growth of the population in Ballari has exacerbated the challenge of municipal waste management, necessitating the development of efficient waste collection strategies to address environmental and social concerns. In response to that our system introduces a hybrid approach that employs K-means clustering and Genetic Algorithm, for optimizing waste collection routes tailored to the unique waste generation profiles of the region. By utilizing Geographic Information System (GIS) data that is in the form of GeoJSON files containing the spatial distribution of waste collection points, we propose a methodology that combines K-means clustering and Genetic Algorithm to develop intelligent and fuel-efficient routes. Our system not only benefits municipal authorities in resource deployment and planning but also aids waste carrier vehicle drivers in prioritizing routes. Our findings contribute to the transformation of Ballari City towards eco-friendly and smart urban development

IOT Garbage Monitoring System with Weight Sensing

This paper presents a novel approach towards efficient waste management utilizing Internet of Things (IoT) technology. The proposed system integrates garbage monitoring and weight sensing capabilities to create a smart waste management solution. Traditional waste collection methods are often inefficient and lack real-time monitoring, leading to overflows, environmental hazards, and unnecessary resource allocation. By employing IoT sensors embedded within garbage bins, real-time data on fill-level and weight can be collected and transmitted to a central server. AI calculations are applied to break down the information and predict future fill levels, optimizing waste collection schedules and routes. The system also includes a command-line interface available through web or mobile applications, enabling users to request timely pickups or report issues. Furthermore, the weight sensing functionality provides insights into the type and volume of waste being generated, facilitating better waste categorization and recycling initiatives. Field tests conducted in urban environments demonstrate the adequacy of the proposed system, reducing operational costs, minimizing environmental impact, and enhancing overall waste management efficiency. The coordination of IoT innovation with garbage monitoring and weight sensing represents a significant advancement towards sustainable and smarter cities

Autonomous Trash Tracking System

Our revolutionary waste management project introduces a comprehensive solution leveraging cutting-edge technology to transform traditional waste disposal systems. By integrating a smart mobile application and sensor-equipped trash cans, our initiative aims to streamline the waste disposal process, addressing challenges such as timing issues, accessibility concerns, and data management inefficiencies. The mobile app empowers users with real-time information on bin availability, optimal disposal times, and recycling practices, fostering a sense of responsibility and environmental sustainability among communities. Sensor-equipped trash cans strategically placed in public spaces communicate with the mobile app and municipal waste management systems via the Internet of Things (IoT), providing accurate, up-to-date data on bin statuses. This data-driven approach allows for dynamic optimization of waste collection routes, minimizing operational costs and environmental impact. Furthermore, municipalities can utilize real-time data insights to allocate resources efficiently, schedule maintenance based on usage patterns, and implement targeted educational campaigns. Overall, our technology-driven solution not only enhances efficiency in waste management but also contributes to reducing environmental pollution and promoting sustainable waste disposal practices. Keywords—Smart dustbins, proximity sensors, moisture sensors, App development, software and hardware integration

DEVELOPMENT OF TIME-EFFICIENT WASTE COLLECTION ROUTES FOR THE UP DILIMAN CAMPUS USING GIS-BASED NETWORK ANALYSIS

Abstract. Accumulation of waste poses significant environmental and public health concerns. Currently, the Campus Maintenance Office (CMO) of the University of the Philippines Diliman (UPD) facilitates all large-scale waste management procedures through contractual means with the Department of Sanitation and Cleanup Works of Quezon City (DSQC), being guided by CMO upon entry into UPD. The study aims to create time-efficient routes and maps for the current conditions of UPD and CMO through GIS-based network analyses. The datasets utilized in this study include the current UPD traffic scheme, the managed CMO waste collection points, impedance factors like speed bumps, and digitized and estimated traffic data obtained through combined sources from field measurements and online materials. The workflows for the network analysis in ArcGIS involve the preparation of the road network layer, calibration of the network considering the observed collection routes, and the generation of time-efficient routes with appropriately split collection points for the north and south sectors of UPD. The study's new routes found slightly different collection times than the observed collection routes but were able to accommodate more stops that were not observed during fieldwork in comparison.

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

AbstractMedical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.

Modification of the collective dynamic routing method in heterogeneous networks

In the heterogeneous networks, a complex signal-interference environment is formed, characterized by a intra-system interference high level, which significantly reduces the heterogeneous networks throughput. One of the ways to increase such networks throughput by effectively reducing the intra-system interference influence is to use the integrated optimization method of these networks. However, its application is hampered by the complexity of implementing the collective dynamic routing method that underlies it. Therefore, modification of the collective dynamic routing method in relation to large heterogeneous networks is relevant. As a result of the application analysis of the collective dynamic routing method for optimizing the data flows distribution in heterogeneous networks, it is shown that the main limitation of this method applicability is the computational complexity of finding the optimal data transmission route. For this value the corresponding expressions are obtained. A modification of the collective dynamic routing method has been developed, in which the influence of intra-system interference is taken into account at the subnet segment level, and data distribution between subnets is carried out at the zone level. Using the obtained expressions, it is shown that the computational complexity of implementing the collective dynamic routing method in this case depends only on the number of users in the zone, which ensures the scalability of such a solution. Therefore, the use of the developed modification of the collective dynamic routing method will make it possible to use it to increase the throughput of large heterogeneous networks.

Communication channel parameters estimation using a ray tracing model in heterogeneous networks

Currently, modern networks LTE, Wi-Fi, 5GNR are combined into a single heterogeneous network, which leads to an increase in intra-system interference. According to the integrated optimization method, to distribute data flows in a heterogeneous network, taking into account the intra-system interference influence, the collective dynamic routing method is used. At the analysis stage of this method it is necessary to estimate the channel data rate in all valid routes. To obtain correct channel data rates estimates, it is necessary to reliably estimate the channel matrix. Today, the ray tracing model is widely used to estimate the channel matrix. The verification results of the using ray tracing model given in [1] show the need for additional research to increase the channel matrix estimates reliability. Therefore increasing the reliability of channel matrix transmission coefficients estimates by analyzing the influence of the number of rays taken into account is the actual task. The work analyzes the influence of the number of rays taken into account on the reliability of estimating the complex transmission coefficients of the channel matrix for the cases of the presence and absence of line of sight be-tween the transmitter and the receiver. Expressions are obtained to take into account the influence of diffracted, singly reflected and passed through an obstacle rays. A program has been developed to find all possible rays arriving at the receiver location point and determine their parameters based on a digital 3D map indicating the map objects materials. Using the developed program, it is shown that taking into account the influence of additional rays makes it possible to increase the channel matrix estimation reliability, and therefore, the modified ray tracing model can be used to estimate channel data rates when applying the collective dynamic routing method in heterogeneous net-works.