Simulation and optimization of dynamic waste collection routes

Quantifying odour impacts from aged organic waste to be considered as a priority constraint in route optimization for waste collection trucks

Route optimization has been used for years to plan the routes for municipal solid waste (MSW) collection trucks to achieve cost reductions. Historically, optimized routes had overlooked a number of aspects and parameters in their design. This study aims to consider MSW odour detection as a performance indicator and a priority constraint in the optimization process by quantifying the impact of objectionable odours from uncollected aged MSW that contains a high percentage of food waste (typically called wet garbage). Odours from 48 aged food waste samples were rated on a scale from 0 to 3 to mark the beginning of the critical time of objectionable odour detection. The critical time was found to take place approximately at the hour 13.6, which was then used, along with the estimated food waste weight in the bin, to define the beginning of a time window that puts the bin on a high priority status for collection over the other, less odoriferous bins. Three optimization scenarios for collection of 100 MSW bins in the city of Madaba, Jordan, were conducted under different constraints: least travelled distance, maximum collected volume and least odour impact. Without the application of the odour consideration, a total travelled distance of 143 km was the shortest travelled distance achieved, with 53 bins emitting odours and leaving 81 m³ of uncollected waste. However, when odour impact was the main routing constraint, a total travelled distance of 161 km was needed and 13 m³ of waste was left uncollected.

Advancing the bioconversion process of food waste into methane: A systematic review

With the continuous rise of food waste (FW) throughout the world, a research effort to reveal its potential for bioenergy production is surging. There is a lack of harmonized information and publications available that evaluate the state-of-advance for FW-derived methane production process, particularly from an engineering and sustainability point of view. Anaerobic digestion (AD) has shown remarkable efficiency in the bioconversion of FW to methane. This paper reviews the current research progress, gaps, and prospects in pre-AD, AD, and post-AD processes of FW-derived methane production. Briefly, the review highlights innovative FW collection and optimization routes such as AI that enable efficient FW valorization processes. As weather changes and the FW sources may affect the AD efficiency, it is important to assess the spatio-seasonal variations and microphysical properties of the FW to be valorized. In that case, developing weather-resistant bioreactors and cost-effective mechanisms to modify the raw substrate morphology is necessary. An AI-guided reactor could have high performance when the internal environment of the centralized operation is monitored in real-time and not susceptible to changes in FW variety. Monitoring solvent degradation and fugitive gases during biogas purification is a challenging task, especially for large-scale plants. Furthermore, this review links scientific evidence in the field with full-scale case studies from different countries. It also highlights the potential contribution of ADFW to carbon neutrality efforts. Regarding future research needs, in addition to the smart collection scheme, attention should be paid to the management and utilization of FW impurities, to ensure sustainable AD operations.

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site-disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.

A Decision Framework to Recommend Cruising Locations for Taxi Drivers under the Constraint of Booking Information

As the demand for taxi reservation services has increased, increasing the income of taxi drivers with advanced services has attracted attention. In this article, we propose a path decision framework that considers real-time spatial-temporal predictions and traffic network information. The goal is to optimize a taxi driver's profit when considering a reservation. Our framework contains four components. First, we build a grid-based road network graph for modeling traffic network information for speeding up the search process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide proper search directions for recommending cruising locations. One module of the taxi demand prediction is used to estimate the pick-up probabilities of passengers in the city. Another one is destination prediction, which can predict the distribution of drop-off probabilities and capture the flow of potential passengers. Finally, we propose the H* (Heuristic-star) algorithm, which jointly considers pick-up probabilities, drop-off distribution, road network, distance, and time factors based on the attentive heuristic function to dynamically recommend next cruising locations. Compared with existing route planning methods, the experimental results on a real-world dataset have shown that our proposed approach is more effective and robust. Moreover, our designed search scheme in H* can decrease the computing time and allow the search process to be more efficient. To the best of our knowledge, this is the first work that focuses on guiding a route, which can increase the income of taxi drivers under the constraint of booking information.

Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review

Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.

Study on the Method of Household Waste Collection: Case Study

This article presents research on how household waste is collected. An online survey, with 348 participants, from the Romanian region of Bacau, was conducted from October 2018 to May 2019. The online questionnaire included a set of over 40 questions, some with the aim of identifying the nature of the people participating, but most of the questions being designed to determine the collection methods for household waste. The major goal of the current study, as previously stated, was to determine the primary way of collecting household garbage from the public, while also learning various details about the participants, including their residence location, gender, age, and level of education. Referring to the means used for collecting household waste, the following items were noted: trash cans, cardboard boxes, dumpsters, and raffia bags. As a result of the study carried out, the following conclusions were drawn: it was noticed that 70 percent of those who participated in the survey came from urban areas; a larger percentage of female respondents took part in the survey (128 from 348); the majority of respondents were aged 18–29 (182); 178 respondents had a higher education level; collection of household waste in garbage bags represented 62.9 percent of the total collection methods. Following statistical processing of the data, and an overview of the main ways in which household waste was collected, a number of connections were found between the characteristics of the respondents and their household waste collection. What is noteworthy is that the characteristics of the respondents could be grouped into cumulative factors that played an important role in household waste collection: the first group formed by level of education and location of the respondents, and the second group formed by age and gender of the respondents.

Multicriteria Route Planning for In-Operation Mass Transit under Urban Data

Multicriteria route planning is a crucial transportation planning issue under the field of GIS-based multicriteria decision analysis (GIS-MCDA) with broad applications. A searching algorithm is proposed to solve the multicriteria route planning problem with spatial urban information and constraints such an existing transit network in operation, certain vertices to be visited in the path, total number of vertices been visited, and length or range for the path. Evaluation of two in-operation mass-transit systems from Chicago and Tainan show that our method can retrieve solutions in a Pareto-optimal sense over comparative methods between profit under queried constraints (the expected passenger flow to be maximized, referring to the social welfare for the public) and cost for construction as well as maintenance (the cost of route to be minimized, referring to the sustainability for the government) with reasonable runtime over comparative methods.

A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic

COVID‐19 has swept across the globe and disrupted all vectors of social life. Every informed measure must be taken to stop its spread, bring down number of new infections and move to normalization of daily life. Contemporary research has not identified waste management as one of the critical transmission vectors for COVID‐19 virus. However, most underdeveloped countries are facing problems in waste management processes due to the general inadequacy and inability of waste management. In that context, smart intervention will be needed to contain possibility of the COVID‐19 spread due to inadequate waste management. This paper presents a comparative study of the artificial intelligence/machine learning based techniques, and potential applications in the COVID‐19 waste management cycle (WMC). A general integrated solid waste management (ISWM) strategy is mapped for both short‐term and long‐term goals of COVID‐19 WMC, making use of the techniques investigated. By aligning current health/waste‐related guidelines from health organizations and governments worldwide and contemporary, relevant research in area, the challenge of COVID‐19 waste management and, subsequently, slowing the pandemic down may be assisted.

Optimization of Vehicle Routing for Waste Collection and Transportation

For the sake of solving the optimization problem of urban waste collection and transportation in China, a priority considered green vehicle routing problem (PCGVRP) model in a waste management system is constructed in this paper, and specific algorithms are designed to solve the model. We pay particular concern to the possibility of immediate waste collection services for high-priority waste bins, e.g., those containing hospital or medical waste, because the harmful waste needs to be collected immediately. Otherwise, these may cause dangerous or negative effects. From the perspective of environmental protection, the proposed PCGVRP model considers both greenhouse gas (GHG) emission costs and conventional waste management costs. Waste filling level (WFL) is considered with the deployment of sensors on waste bins to realize dynamic routes instead of fixed routes, so that the economy and efficiency of waste collection and transportation can be improved. The optimal solution is obtained by a local search hybrid algorithm (LSHA), that is, the initial optimal solution is obtained by particle swarm optimization (PSO) and then a local search is performed on the initial optimal solution, which will be optimized by a simulated annealing (SA) algorithm by virtue of the global search capability. Several instances are selected from the database of capacitated vehicle routing problem (CVRP) so as to test and verify the effectiveness of the proposed LSHA algorithm. In addition, to obtain credible results and conclusions, a case using data about waste collection and transportation is employed to verify the PCGVRP model, and the effectiveness and practicability of the model was tested by setting a series of values of bins’ number with high priority and WFLs. The results show that (1) the proposed model can achieve a 42.3% reduction of negative effect compared with the traditional one; (2) a certain value of WFL between 60% and 80% can realize high efficiency of the waste collection and transportation; and (3) the best specific value of WFL is determined by the number of waste bins with high priority. Finally, some constructive propositions are put forward for the Environmental Protection Administration and waste management institutions based on these conclusions.

Artificial intelligence applications in solid waste management: A systematic research review

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.

Planning the optimal debris removal of destroyed buildings in the Midan region in the Old City of Mosul

After the liberation of the Old City in Mosul, the Midan region appears to be the most affected part of the Old City. In the current study, debris of the Midan region was studied in detail by defining the destroyed areas using geographic information systems (GIS), then calculating the volume and weight of the total debris. The ratio of each component of debris (building stones, gypsum and concrete) was also determined during the study. The results showed that the destruction was about 89.47% of the Midan total area. In addition, the study showed that the total volume of the debris in the Midan area is estimated at 0.73 million cubic meters, the residential buildings accounting for about 86.87% of the total volume of the debris. The total weight of debris is estimated at 1.2 million tons, of which 0.91 million tons are the ruins of old building materials (building stones, gypsum). Finally, the optimal route for collecting the debris from the Midan region was found by utilizing ArcGIS network analyst extension, taking into account the archaeological sites in the study area, the proposed collection system assumed to have the benefits of minimizing collection time, distance travelled, man-effort and consequently financial and environmental costs. Total distance and accumulated time were illustrated in a table providing detailed information on the proposed route.

Interactions of residential waste composition and collection truck compartment design on GIS route optimization

Waste collection is an important functional element in a modern waste management system; and may account for up to half of the total expenditure on waste management in industrialized nations. Most optimization of waste collection studies include truck route distance and fuel consumption considerations without explicitly considering the inter-relationships of the model parameters. This study however delineates the complex inter-relationships of waste composition, collection frequency, collection type, and truck compartment configurations in a small waste collection zone in Austin, Texas. A total of 48 different scenarios are modelled and investigated. Truck travel distances are found sensitive to collection frequency, truck capacity, volume ratio of truck compartment, and waste density. The results showed that the increase in waste density and waste collection frequency helped to save up to 18.2% in travel distances and 41.9% in travel time. Waste composition is significant in travel distance, regardless of truck design. Increasing truck capacity by 25% helped to save 4.1-24.4% of truck travel distances. Optimal volume ratio of truck compartments was 50:50 (50% volume for garbage and 50% volume for recyclables); a finding that is different than what is currently reported in the literature; pointing to the site-specific nature of studies of this type. The use of dual compartment trucks helps to reduce travel distances by up to 23.0% and travel time by up to 14.3%. It appears that the minimization of operation time within the collection area is key to an efficient system.

A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions

In order to solve the optimization problem of wet waste collection and transportation in Chinese cities, this paper constructs a chance-constrained low-carbon vehicle routing problem (CCLCVRP) model in waste management system and applies certain algorithms to solve the model. Considering the environmental protection point of view, the CCLCVRP model combines carbon emission costs with traditional waste management costs under the scenario of application of smart bins. Taking into the uncertainty of the waste generation rate, chance-constrained programming is applied to transform the uncertain model to a certain one. The initial optimal solution of this model is obtained by a proposed hybrid algorithm, that is, particle swarm optimization (PSO); and then the further optimized solution is obtained by simulated annealing (SA) algorithm, due to its global optimization capability. The effectiveness of PSOSA algorithm is verified by the classic database in a capacitated vehicle routing problem (CVRP). What's more, a case of waste collection and transportation is applied in the model for acquiring reliable conclusions, and the application of the model is tested by setting different waste fill levels (WFLs) and credibility levels. The results show that total costs rise with the increase of credibility level reflecting dispatcher's risk preference; the WFL value range between 0.65 and 0.75 can obtain the optimal solution under different credibility levels. Finally, according to these results, some constructive proposals are propounded for the government and the logistics organization dealing with waste collection and transportation.

A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization

Urban waste collection is one of the principal processes in municipalities with large expenses and laborious operations. Among the important issues raised in this regard, the lack of awareness of the exact amount of generated waste makes difficulties in the processes of collection, transportation and disposal. To this end, investigating the waste collection issue under uncertainty can play a key role in the decision-making process of managers. This paper addresses a novel robust bi-objective multi-trip periodic capacitated arc routing problem under demand uncertainty to treat the urban waste collection problem. The objectives are to minimize the total cost (i.e. traversing and vehicles' usage costs) and minimize the longest tour distance of vehicles (makespan). To validate the proposed bi-objective robust model, the ε-constraint method is implemented using the CPLEX solver of GAMS software. Furthermore, a multi-objective invasive weed optimization algorithm is then developed to solve the problem in real-world sizes. The parameters of the multi-objective invasive weed optimization are tuned optimally using the Taguchi design method to enhance its performance. The computational results conducted on different test problems demonstrate that the proposed algorithm can generate high-quality solutions considering three indexes of mean of ideal distance, number of solutions and central processing unit time. It is proved that the ε-constraint method and multi-objective invasive weed optimization can efficiently solve the small- and large-sized problems, respectively. Finally, a sensitivity analysis is performed on one of the main parameters of the problem to study the behavior of the objective functions and provide the optimal policy.