Collection Routes Research Articles

Optimize the solid waste collection route in construction using the POA algorithm

Optimize the solid waste collection route in construction using the POA algorithm

Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection

The efficient collection of municipal solid waste poses a significant challenge for the prospective development of smart cities. Using Internet of Things (IoT) technology enables the detection of various kinds of waste-related information, facilitating the implementation of a comprehensive plan for efficient waste collection. In this paper, an innovative waste collection mechanism leveraging the IoT sensors and widely recognized Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a robust multi-criteria decision analysis method is introduced. Recognized for its ability to yield optimal solutions amidst multiple influencing parameters, TOPSIS proves particularly advantageous when confronted with diverse factors affecting decision making. A TOPSIS-based algorithm for optimal route selection is developed to collect waste using metrics such as toxicity, volume, and time duration of the waste while minimizing the route distance. To demonstrate the efficiency of our proposed algorithm, a comparative analysis with existing algorithms that are dependent on and optimized for single parameters is conducted. Through rigorous evaluation, it is shown that our proposed algorithm reduces the total distance traveled to collect the waste from all the bins by 14% by effectively considering multiple criteria and optimizing waste collection routes.

Inovasi Digital dalam Manajemen Persampahan: Perancangan Prototype E-Sampah sebagai Solusi untuk Mengoptimalkan Pengelolaan Sampah di Kota Gorontalo

Gorontalo City is the largest contributor of waste in Gorontalo Province. Waste production in Gorontalo City increases to 140 tonnes per day based on data from the National Waste Management Information System. One of the things that caused the waste problem in Gorontalo to become more severe is the difficulty in transporting waste due to the limited transport fleet. Furthermore, the Waste Management Reuse, Reduce, and Recycle (TPS3R) personnel in several sub-districts are no longer functioning. This article details the emergence of innovative waste management applications, providing a new paradigm for cities to become digitally connected hygiene centers. The app not only revolutionizes the way waste is perceived but also offers smart solutions to optimize waste management efficiently. With a modern and progressive approach, the app creates an interactive experience for the people of Gorontalo to manage their waste. Through smart center and route mapping features, the app empowers citizens to actively contribute to keeping the city clean. In addition, the integrated information system enables quick and informed decision-making for authorities, creating responsive and effective waste management. The research conclusion confirms that the design of E-Sampah with a focus on the centre point and route of waste collection by waste officers can provide a modern and accessible solution for the people of Gorontalo. The results of the System Usability Scale (SUS) assessment with a score of 80.5 indicate that this waste management information system received a positive response from users. This prototype is expected to change mindsets and shape behaviors that support sustainable waste management.

State of the Art in Textile Waste Management: A Review

Textile waste constitutes a significant fraction of municipal solid waste sent to landfill or incinerated. Its innovative management is important to enhance sustainability and circularity. This review aims to present the latest policies and the state-of-the-art technologies in the collection, sorting and recycling of textile waste. Policies at global and regional levels are increasingly made to address the sustainability of the textile industry and integrate the concept of circular economy. They are crucial to driving changes and innovations in current textile waste management. The Internet of Things, big data, blockchain and smart contracts have been proposed to improve transparency, traceability and accountability in the textile waste collection process. They optimize collection routes, and transactions and agreements among stakeholders. The sorting of textile waste using near-infrared spectroscopy, optical sorting and artificial intelligence enables its separation based on composition, color and quality. The mechanical recycling of textiles regenerates fibers with the same or different applications from those of the original fabrics. Fibers have been used for making building and slope protection materials. Chemical recycling depolymerizes waste textiles using chemicals to produce monomers for new textiles or other materials, while biological recycling uses enzymes and microorganisms for this purpose instead of chemicals. Thermal recycling recovers energy and fuels from textile waste through pyrolysis, gasification and hydrothermal liquefaction. These innovations may have the drawbacks of high cost and scalability. This review contributes to decision making by synthesizing the strengths and weaknesses of the innovations in textile waste management.

Developing an Optimization Model for Minimizing Solid Waste Collection Costs

With the increase in population in cities, the number of solid waste to be collected has also increased. Because the garbage collection route must be traveled repeatedly, even minor improvements in these routes can result in a significant increase in fuel usage. Shortening the journey would provide a significant contribution to lowering fuel expenses in all towns, especially given the rising cost of fossil fuels. Furthermore, lowering fuel usage is critical for Turkey to meet its national objectives under the Paris Agreement. The Simulated Annealing (SA) algorithm, one of the heuristic optimization techniques used to identify the best solutions to complicated problems, is employed to solve the routing problem of solid waste collection vehicles in this study. This method, which was inspired by the metal annealing process, stands out for its ability to avoid regional minima while looking for the optimal solution. The applicant region was selected as the Kosova Neighborhood of Konya's Selçuklu District. The container distances needed for the method to execute were acquired by extracting the coordinates of the containers. Kosova Neighborhood was separated into 7 distinct regions due to the restricted capacity of rubbish collection vans. All regions were analyzed independently, and the best feasible routes were estimated using the SA algorithm approach, and the results were compared to the greedy algorithm findings. The SA algorithm outperformed the greedy algorithm by 26.49%.

Strategic Decision-Making for Multi-Period Fleet Transition Towards Zero-Emission: Preliminary Study

Municipal Solid Waste Management (MSWM) struggles with significant policy and operational challenges, particularly concerning collection routes for recyclables and fleet composition. Within the European Union, phasing out traditional fuel-based vocational vehicles, like garbage trucks, in favor of zero-emission alternatives, is mandatory to achieve sustainable development objectives. This paper presents a preliminary study on the problem of multi-period fleet transition from combustive fuels towards more eco-friendly fueling types. Initially developed for energy sector, the MARKAL framework was used here to support the technological transition of the fleet. The mixed-integer program was formulated for the Fleet Transition Problem (FTP), a simplified theoretical problem. The objective of the FTP and a mixed-integer linear program used to solve it is minimizing the overall cost of fleet modernization throughout a multi-phase planning horizon so that the sustainable transition of the fleet can be assured. Computational experiments run on randomly generated data instances affirmed the model’s effectiveness in strategizing fleet transition. This research outlines a multi-period model for transitioning to a zero-emission fleet and demonstrates the FTP’s potential for strategic decision-making. Notably, the study observes consistent reductions in permissible emissions across the planning horizon.

Measuring Accessibility and Optimising Logistics of Marginal Land Grass Biomass in the Case of Northern Ostrobothnia, Finland

Abstract Grass biomass originating from marginal agricultural land unsuitable for food production can be utilised in variety of bio-based productions. The key profitability constraints for such productions are regional availability and transport costs. The aim of this paper is to methodologically test open access agricultural field parcel data – enabled by the EU INSPIRE directive – integrated with the road network in measuring biomass accessibility and optimising collection and transport using geographic information system (GIS) technology in two Finnish case areas. The analytical framework applies closest facility and vehicle routing analyses for tractor and truck transport scenarios. The key finding is that the area of field parcels and the amount of biomass accumulating within an observed area are not the only important factors influencing transport efficiency: cost-effectiveness of collection routes can be equally high in regions with scarce and abundant biomass areas.

Optimization of the routing problem using the physics-based electromagnetism-like algorithm

Waste collection is considered as a first objective in planning a sustainable waste management, and provision of substantial collection coverage in urban and rural areas should be satisfied before investing in more sophisticated infrastructure. Therefore, good waste collection service is essential to upgrade further the next levels of a municipal solid waste system. One of the ways to enhance performance on waste collection is to plan effective routing of trucks. The study formulated a Waste Collection Arc Routing Problem to model the waste collection routing problem of Parang, Marikina City. The study revealed that the EM-like with local search and selection strategy mechanism displayed significant minimization abilities of distance values, and the capacity consideration of truck is important to attain better efficiency and cost savings of route.

OPTIMIZATION OF COLLECTION ROUTES USING THE CENTRE OF GRAVITY METHOD: A CASE STUDY

The paper deals with the implementation of research concerning the collection routes of a selected company and subsequent streamlining processes in the field of distribution logistics. Specifically, the paper is focused on the optimization of collection routes of textile waste for the customer, i.e., a contractual partner of the company under investigation. The objective of the paper is to analyse step by step the current state of logistics of supplying a specific warehouse, followed by the application of the centre of gravity method for proposing a warehouse relocation. Finally, the individual routes to collection points are optimized. In general, optimization is done for two basic reasons: profit maximization and logistics costs minimization, which ultimately has a positive impact on earnings. The outcome is to determine the optimal routes with respect to costs and traffic while considering complications that may occur in a given transport territory.

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization

Background The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed. Methods By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization. Results The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers. Conclusions Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

In recent years, the waste produced as a result of the production and consumption activities of urban residents has led to significant environmental degradation and resource wastage. This paper focuses on the research object of municipal solid waste (MSW) collection and transportation based on the concept of “sustainable development and green economy”. Firstly, this study examines the current state of urban domestic garbage collection and transportation. It analyzes the following challenges and deficiencies of the existing collection and transportation system: (1) the operating efficiency of garbage collection vehicles is low, resulting in a significant accumulation of waste on the roadside and within the community; (2) the vehicle collection and transportation routes are fixed, and there are empty vehicles running; (3) the amount of garbage on a route exceeds the vehicle’s loading capacity, which requires the vehicle to perform a second round of collection and transportation. To enhance the efficiency of urban garbage collection and transportation and minimize the collection and transportation costs, we are investigating the problem of optimizing the path for green vehicles. To comprehensively optimize the fixed cost, variable cost, and carbon emission cost incurred during vehicle operation, a vehicle routing model with time windows is established, taking into account vehicle load constraints. Carbon emission coefficient and carbon tax parameters are introduced into the model and the “fuel-carbon emission” conversion method is used to measure the carbon cost of enterprises. An improved ant colony optimization (ACO) method is proposed: (1) the introduction of a vehicle load factor improves the ant state transfer method; (2) the updated pheromone method is improved, and additional pheromone is added to both the feasible path and the path with the minimum objective function; (3) the max–min ACO algorithm is introduced to address the issue of premature convergence of the algorithm; (4) the embedding of a 2-opt algorithm further prevents the ACO algorithm from falling into the local optimum. Finally, the calculation results based on the example data demonstrate that the algorithm has a significant advantage over the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The total transportation distance determined by this algorithm is shorter than that of the GA and PSO methods, and the total cost of the scheme is 1.66% and 1.89% lower than that determined by GA and PSO, respectively. Compared to the data from the actual case, the number of vehicles required in the operation of this algorithm and model is reduced by three. Additionally, the total cost, fixed cost, and carbon emission cost incurred by the vehicles during operation were reduced by 31.2%, 60%, and 25.3% respectively. The results of this study help the station to collect and distribute waste efficiently, while also achieving the goals of energy saving, consumption reduction, and emission reduction.

Waste collection routing: a survey on problems and methods

Waste collection is a vital service performed all over the world, which heavily relies on vehicle routing. Due to regulations and local conditions, the problems and their characteristics often differ greatly. This literature survey aims to review the current state of the art overlap in waste collection and vehicle routing literature. The most notable papers are categorized according to their underlying problem type, examined and brought into relation based on their common problem characteristics. The problem types comprise general, node and arc routing problems, with vehicle routing problems being the most common, followed by arc and location routing problems. Besides the use of intermediate facilities, which is naturally very common in waste collection literature, the authors point out other interesting characteristics found in the literature and in practical problems, such as uncertain demand, personnel planning aspects, alternative collection systems or vehicle types, and characteristics related to risk or sustainability. Additionally, the authors highlight prominent scopes and objectives as well as recent developments in this area. Overall, this survey provides a selective overview and calls attention to research gaps and possible future research directions.

A three-stage matheuristic for home blood donation appointment reservation and collection routing

AbstractIn Western countries, the so-called Blood Donation Supply Chain (BDSC) provides blood units to several health services. Its first echelon is the collection of unit from donors, which requires a careful management because an unbalanced supply of units to the rest of the chain could trigger alternating periods of blood shortage and wastage. However, the management of blood collection is only marginally studied in the literature, in comparison to other BDSC echelons. In this work, we propose a new organizational model for blood collection, in which blood is collected at donor’s homes, and provide a decision support tool for its management. This new model provides a novel contribution to the understudied blood collection echelon and, at the same time, it responds to the emerging need of delocalization of health services. The proposed decision support tool consists of an interconnected matheuristic framework with three decision stages: (i) a planning model to create the donation slots that will be assigned to donors, (ii) an online allocation of these slots using a flexible set of criteria, and (iii) a Multi-Trip Vehicle Routing Problem with Time Windows (MTVRP-TW) to route the bloodmobiles that collect blood at donors’ homes. The main goals are to balance the production of blood units between days and to minimize the distance travelled by the bloodmobile fleet, while respecting time windows negotiated with donors. This framework also has the feature of immediately providing a list of slots to choose from when a donor makes a booking request. The decision support tool has been tested on data from a real Italian provider. Results confirm its effectiveness, and the capability of providing good quality and economically sustainable solutions in reasonable timeframes.

Evaluation of Garbage Management Based on IoT

Smart Waste Monitoring: To track the amount of waste in bins and containers, IOT-enabled garbage management systems use sensors and connected devices. These sensors can communicate real-time data to a centralized monitoring system and can identify the fill level. This data aids in streamlining waste collection routes, cutting back on pointless pickups, and enhancing garbage management effectiveness as a whole. Effective Resource Allocation: By giving precise data on waste generation patterns and trends, IOT-based garbage management systems enable optimal resource allocation. This information can be used by municipal authorities to make well-informed decisions on waste collection schedules, resource deployment, and staffing levels. IOT-based waste management solutions have the potential to make trash management procedures more effective and efficient while also being more affordable. The best garbage collection routes, operational cost reductions, and resource utilization may all be achieved with the aid of research into the best deployment strategies for IOT sensors and devices. Environmental Impact and Sustainability: Research Objective: Clearly identify the research objective, for example, by assessing how well IOT-based garbage management systems gather waste and allocate resources. Data gathering: Compile pertinent information on the methods used for trash generation, collection, and resource use. On-site observations, employee interviews, and database access for waste management operations are all effective ways to accomplish this. Gather information on IOT sensor technologies and their capabilities as well. Taken As alternative for Smart Waste Bins, Waste Level, Sensors, AI Recycling, Robots, E-Waste Kiosks. Taken for Evaluation preference is Reliability, Mobility, Service Continuity, User Convenience., and Energy Efficiency. Smart Waste Bins has performed more when compare to with other Real-Time Monitoring: The Internet of Things (IOT) can be used in waste management to enable real-time monitoring of trash cans or bins can be used to enhance garbage sorting procedures. Smart bins with cameras and sensors can automatically recognize and sort various types of rubbish. These smart bins can identify and categorise rubbish by utilizing IOT technology. on their material composition or recycling category.

Ant colony algorithm to solve a drone routing problem for hazardous waste collection

Waste management issues are affecting the economic and environmental aspects of modern societies. Thus, growing the interest of academic and industrial research and development in optimizing the process of waste management. As these issues greatly impact human health and environmental aspects and impose a threat, hazardous waste management requires even much more attention. The problem studied in this research is a variant of the vehicle routing problem using an unmanned aerial vehicle (UAV). The focus of this research is on planning the routes for waste collection and disposal using a UAV. The aim is to collect all the waste as early as possible respecting two constraints; the maximum flying and load capacities of the UAV. A two-phase approach has been proposed to solve the investigated problem. This approach is a hybridization of a developed heuristic (IMWMTT) and an Ant Colony Optimization (ACO) algorithm. The experimental study showed that the hybrid approach outperforms a recently published heuristic MWMTT for all tested instances of various sizes.

Supply chain models for non-reusable packaging logistics

Packaging logistics plays an important role in today’s economy, because a well-chosen packaging can significantly increase the market share of a company. However, packaging has advantageous impacts on the purchasing, production and distribution processes, but negative impact must be also taken into consideration focusing on sustainability, costs, environmental impacts and logistics. Within the frame of this article, the author suggests a potential model structure for non-reusable packaging logistics focusing on the type of the collection and distribution routes, number of senders and receivers, location of recycling or disposal, maintenance and cleaning location, existence of agents, logistics centers and central depots. The suggested approach makes it possible to derive the typical supply chain models for both non-reusable and reusable packaging. Within the frame of this article, the most important supply chain models for non-reusable packaging logistics are described.

Holistic Trash Collection System Integrating Human Collaboration with Technology

Effective waste management is of paramount importance as it contributes significantly to environmental preservation, mitigates health hazards, and aids in the preservation of precious resources. Conversely, mishandling waste not only presents severe environmental risks but can also disrupt the balance of ecosystems and pose threats to biodiversity. The emission of carbon dioxide, methane, and greenhouse gases (GHGs) can constitute a significant factor in the progression of global warming and climate change, consequently giving rise to atmospheric pollution. This pollution, in turn, has the potential to exacerbate respiratory ailments, elevate the likelihood of cardiovascular disorders, and negatively impact overall public health. Hence, efficient management of trash is extremely crucial in any society. It requires integrating technology and innovative solutions, which can help eradicate this global issue. The internet of things (IoT) is a revolutionary communication paradigm with significant contributions to remote monitoring and control. IoT-based trash management aids remote garbage level monitoring but entails drawbacks like high installation and maintenance costs, increased electronic waste production (53 million metric tons in 2013), and substantial energy consumption for always-vigilant IoT devices. Our research endeavors to formulate a comprehensive model for an efficient and cost-effective waste collection system. It emphasizes the need for global commitment by policymakers, stakeholders, and civil society, working together to achieve a common goal. In order to mitigate the depletion of manpower, fuel resources, and time, our proposed method leverages quick response (QR) codes to enable the remote monitoring of waste bin capacity across diverse city locations. We propose to minimize the deployment of IoT devices, utilizing them only when absolutely necessary and thereby allocating their use exclusively to central garbage collection facilities. Our solution places the onus of monitoring garbage levels at the community level firmly on the shoulders of civilians, demonstrating that a critical aspect of any technology is its ability to interact and collaborate with humans. Within our framework, citizens will employ our proposed mobile application to scan QR codes affixed to waste bins, select the relevant garbage level, and transmit this data to the waste collection teams’ database. Subsequently, these teams will plan for optimized garbage collection procedures, considering parameters such as garbage volume and the most efficient collection routes aimed at minimizing both time and fuel consumption.

Designing a sustainable collection and transportation routes for domestic wastes in the agro-pastoral ecotone of the Tibetan Plateau

The absence of an efficient and safe routes for the timely collection and transportation of domestic waste (DW) may have negative effects on the environment and public health. However, the existing collection and transportation routes (CTR) for domestic waste (DW) based on territorial management are not suitable for the special socio-ecological system of the agro-pastoral ecotone (APE). Therefore, it is crucial to develop a low-cost, high-efficiency, and risk-free CTR to mitigate the threat of DW to the environmental sustainability in the APE of the Tibetan Plateau. This study selected Haidong as a research case and constructed a sustainable CTR optimization framework based on an integrated perspective on temporal, spatial and eco-safety risk. We used the improved Ant Colony Optimization (ACO) to simulate optimal spatial-temporal routes, and the eco-safety risk level of the CTR was assessed by using the Minimum Cumulative Resistance model (MCR). Results demonstrated that: (1) After the sustainable model was optimized, the total transportation mileage and the frequency of collection and transportation were reduced by 45.88% and 38.07% respectively, the economic cost savings were decreased by 32.29%. Optimized routes were more effective and can better adapt to the dispersed pollution-producing characteristics in the APE. (2) The optimized routes reduced greenhouse gas (GHG) emissions by 41.09%, and reduced the eco-safety risk of the high and relative high-risk routes, which account for 29.05% of total routes, can protect important ecological functions and reduce the adverse impacts of DW transportation on soil, atmosphere, water, and the living environment. (3) The cores of adaptive management for sustainable CTR in APE were to change from the current single-county administrative organization to a cross-county administrative organization; adjust the transportation cycle based on pollution-producing characteristics; sort the DW locally; and cultivate environmental awareness among farmers and herdsmen. This study designed new sustainable collection and transportation routes for domestic waste to improve environmental sustainability in the agro-pastoral ecotone.

Optimizing Municipal Waste Collection: a Case Study of a City in Poland

The main problem of waste management is the increasing amount of municipal waste, and one of the key processes generating high costs is the waste collection process. The aim of the article was to optimize the route of a garbage truck using information technology (IT) software in one of the most populated Polish cities. The article tests the study hypothesis: the use of route optimization software will reduce the route length traveled by the garbage truck of the MZO in Pruszków. The data for the study was made available with the consent of the Municipal Treatment Plant in Pruszków. The received materials included information on, among others, Global Positioning System (GPS) readings of the garbage truck, including route start and end times, route length, average speed, driving time, and time of stops, points selected by the planners to collect waste along the route, information on the amount of waste collected during the implementation of the route, technical data on the moving vehicle and characteristics of the sorting plant were received. The article proposes the optimization of the routes of collection and transportation of municipal waste using the traveling salesman problem (TSP). The minimization of route length was assumed as the optimization criterion. All calculations were made in the Routimo program dedicated to route planning and optimization. As a result of the optimization, the route length was reduced by nearly 32%, and the working time by 9%. Thus, the research hypothesis stated in the article was positively verified.

A Pilot Project Using Sensors in the Municipal Solid Waste Collection of a Medium-Sized City

Municipal solid waste collection must reach the recycling rates required by new regulations, like with the introduction of new bins to collect the organic fraction. The filling pattern of those bins is unknown, and the collection frequency and collection routes can change. Sensors can help to identify the filling degree and to reorganize the waste collection routes. Yet, equipping all the bins with new technologies can represent a significant amount of money for enterprises and public authorities. The aim of this work is to analyze the use of sensors in waste collection systems where the collection frequency is high. Bins from two zones of a town (a residential and a commercial zone) were selected, and a volumetric sensor was installed to study the filling pattern. In the first scenario, the organic fraction and the mixed waste bin were monitored. In the second experiment, data from mixed waste bins were recorded during two days of the week. Results show that the filling degree pattern is similar in all the cases and that citizens do not respect the recommended times for taking their waste out. Additionally, the cost of the sensors represents a significant amount for a medium-sized town. Although sensors can provide a great amount of information, it is not necessary to install them in all the bins but only at the points to be analyzed. This work proposes a new way to use sensors to validate and calibrate the containerization network of a town with a high collection frequency.