Inventory routing for dynamic waste collection

Comparison of different waste bin monitoring approaches: An exploratory study

Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.

Mixed-method approach incorporating Geographic information system (GIS) tools for optimizing collection costs and convenience of the biowaste separate collection

Nowadays, dealing with organic waste (or biowaste) remains a global phenomenon. Especially developing countries worldwide generate more than 50 percent organicwaste. In the European Union (EU) with a share of 34%, biowaste is a dominantfraction of the municipal waste (EEA, 2020). Therefore, separate collection at source and environmentally sound treatment of biowaste are of key importance. An intensive optimisation of biowaste separate collection is needed to balance demands of municipal representatives and households' needs. Based on the mixed-method approach we developed a MCDA model complemented by expert-based weighting assessment and combined with the GIS localisation tools aimed at the optimisation of biowaste container locations that reflects various spatial conditions, preconditions for the localisation of containers and its cost intensity. We concluded that changing the density of containers, distance between the address point and container, and selecting container locations that respect the habits of households and demands of the collection technology significantly affect the total and collection costs. We confirmed that the decreases in the total costs were not significant for maximum walking distances of over 95 m, and would approach zero for distances of over 230 m. When the maximum walking distance exceeds 268 m, 40% of all inhabitants would not participate in the system as it would be inconvenient for them. A recycling campaign is needed to increase their willingness to participate in the system. We provided arguments for decision-makers how to balance convenience of the biowaste separation system and collection costs by proper localisation of biowaste containers.

A BWM-TOPSIS Hazardous Waste Inventory Safety Risk Evaluation

Hazardous waste can cause severe environmental pollution if not disposed of properly, which in turn can seriously affect the sustainable development of the entire ecology and will inevitably bring disaster to companies. However, because of limited available disposal capacity, it is often difficult to safely dispose of hazardous waste, meaning that it must be kept as passive inventory. For the passive inventory of hazardous waste, risk evaluation of safe operation of the inventory is crucial and urgently needs to be resolved. Based on this, this paper focuses on the risk management of hazardous waste inventory of waste-producing companies and proposes a risk evaluation system for safely dealing with hazardous waste inventory, which expands the scope of inventory safety management and provides guidance to companies on developing appropriate measures to ensure hazardous waste inventory safety. First, the risk evaluation index system for hazardous waste inventory is constructed from equipment, management level, nature of hazardous waste and operational aspects. Then, the best worst method (BWM) is employed to calculate the criteria weights and the technique for order performance by similarity to ideal solution (TOPSIS) is employed to rank the alternatives. Finally, risk evaluation on four waste-producing companies was conducted using the developed method. The results show that Case Company 4 has the greatest risk of hazardous waste inventory, which should be reduced by improving storage method and the amount of hazardous waste. It was found that the proposed evaluation system was effective for hazardous waste inventory safety risk assessments and that the designed index system could assist companies improve their hazardous waste inventory management.

Heterogeneous Sensing Data Analysis for Commercial Waste Collection

Waste collection has become a major issue all over the world, especially when it is offered as a service for businesses; unlike public waste collection where the parameters are relatively homogeneous. This industry can greatly benefit from new sensing technologies and advances in artificial intelligence that have been achieved over the last few years. However, in most situations waste management systems are based on obsolete technologies, with a low level of interoperability and thus offering static processes. The most advanced solutions are generally limited to statistical, non-predictive approaches and have a limited view of reality, making them weakly effective in dealing with day-to-day business issues (overflowing containers, poor quality of service, etc.). This paper presents a case study currently being developed in Luxembourg with a company offering a business waste collection service, which has a significant amount of constraints to consider. Our main objective is to investigate the use of multiple waste data sources to derive useful indicators for improving collection processes. We start with company-owned historical data and then investigate GPS information from tracking devices positioned on collection trucks. Furthermore, we analyze data collected from ultrasonic sensors deployed on almost 50 different containers to measure fill levels. We describe the deployment steps and show that this approach, combined with anomaly detection and prediction techniques, has the potential to change the way this business operates. We also discuss the interest of the different datasets presented and multi-objective optimization issues. To the best of our knowledge, this article is the first major work dedicated to the world of professional waste collection.

Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review

The general aim of circular economy is the most efficient and comprehensive use of resources. In order to achieve this goal, new approaches of Industry 4.0 are being developed and implemented in the field of waste management. The innovative K-project: Recycling and Recovery of Waste 4.0 - "ReWaste4.0" deals with topics such as digitalisation and the use of robotic technologies in waste management. Here, a summary of the already published results in these areas, which were divided into the four focused topics, is given: Collection and Logistics, Machines and waste treatment plants, Business models and Data Tools. Presented are systems and methods already used in waste management, as well as technologies that have already been successfully applied in other industrial sectors and will also be relevant in the waste management sector for the future. The focus is set on systems that could be used in waste treatment plants or machines in the future in order to make treatment of waste more efficient. In particular, systems which carry out the sorting of (mixed) waste via robotic technologies are of interest. Furthermore "smart bins" with sensors for material detection or level measurement, methods for digital image analysis and new business models have already been developed. The technologies are often based on large amounts of data that can contribute to increase the efficiency within plants. In addition, the results of an online market survey of companies from the waste management industry on the subject of waste management 4.0 or "digital readiness" are summarized.

Designing a sustainable dynamic collection service for WEEE: an economic and environmental analysis through simulation

The integration of economic and environmental objectives is crucial in the waste collection sector, especially for flows characterized by a high economic value like waste from electric and electronic equipment (WEEE). WEEE needs a complex and flexible reverse logistics system to face high uncertainty and variability of waste flows, while keeping a high efficiency. A few efforts in the literature have focused on planning an efficient collection service on a local scale. In this paper, a simulation-based methodology is adopted to compare different alternatives for a WEEE collection service in Italy. A dynamic collection scheme (i.e. with variable collection frequencies based on the actual level of waste flow) is simulated in two different logistics configurations, i.e. one based on direct connection and one based on an "hub-and-spoke" network. The impact of adopting electric vehicles is also evaluated. Alternatives are compared through economic and environmental key performance indicators to assess the level of sustainability. Results show the advantages of a "hub-and-spoke" configuration with the use of electric vehicles.

Optimization of Municipal Waste Collection Routing: Impact of Industry 4.0 Technologies on Environmental Awareness and Sustainability

The accelerated movement of people towards cities led to the fact that the world’s urban population is now growing by 60-million persons per year. The increased number of cities’ population has a significant impact on the produced volume of household waste, which must be collected and recycled in time. The collection of household waste, especially in downtown areas, has a wide range of challenges; the collection system must be reliable, flexible, cost efficient, and green. Within the frame of this paper, the authors describe the application possibilities of Industry 4.0 technologies in waste collection solutions and the optimization potential in their processes. After a systematic literature review, this paper introduces the waste collection process of downtowns as a cyber-physical system. A mathematical model of this waste collection process is described, which incorporates routing, assignment, and scheduling problems. The objectives of the model are the followings: (1) optimal assignment of waste sources to garbage trucks; (2) scheduling of the waste collection through routing of each garbage truck to minimize the total operation cost, increase reliability while comprehensive environmental indicators that have great impact on public health are to be taken into consideration. Next, a binary bat algorithm is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and then evaluates its performance to increase the cost-efficiency and warrant environmental awareness of waste collection process.

Life-cycle cost as basis to optimize waste collection in space and time: A methodology for obtaining a detailed cost breakdown structure

Extensive research has been carried out on waste collection costs mainly to differentiate costs of distinct waste streams and spatial optimization of waste collection services (e.g. routes, number, and location of waste facilities). However, waste collection managers also face the challenge of optimizing assets in time, for instance deciding when to replace and how to maintain, or which technological solution to adopt. These issues require a more detailed knowledge about the waste collection services' cost breakdown structure. The present research adjusts the methodology for buildings' life-cycle cost (LCC) analysis, detailed in the ISO 15686-5:2008, to the waste collection assets. The proposed methodology is then applied to the waste collection assets owned and operated by a real municipality in Portugal (Cascais Ambiente - EMAC). The goal is to highlight the potential of the LCC tool in providing a baseline for time optimization of the waste collection service and assets, namely assisting on decisions regarding equipment operation and replacement.

Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling

The complexity of collection systems for Waste from Electric and Electronic Equipment (WEEE) in the EU is increasing, due to the latest directive that sets new collection targets and modes. The high variability and the uncertainty of reverse flows require innovative logistic approaches. One recent option for increasing efficiency and responsiveness in waste collection services, boosted by new technological solutions for waste level monitoring, is to adopt a dynamic collection scheme, where the collection frequency is not established a priori (based on a fixed plan), but it is based on the actual filling levels of waste bins. This option can allow the service provider to plan the collection service following the actual demand, resulting in a more responsive service, while improving the logistic efficiency. This paper evaluates the implementation of dynamic scheduling schemes for the collection of WEEE. A hybrid simulation model has been developed in order to support researchers and practitioners in assessing quantitative impacts of adopting dynamic scheduling in WEEE collection. Three logistic alternatives (a fixed collection schedule scheme, a pure dynamic scheme and a mixed one) have been compared in a test case based on data of an Italian municipality; collection services for different types of WEEE (i.e. large appliances and small items) have been analyzed. Results show a promising performance of dynamic schedules compared to the fixed one, revealing, for the specific test case, how a mixed solution can combine the advantages of dynamic and fixed scheduling, gaining flexibility towards customer demand while improving truck resource utilization.

Optimization of municipal solid waste transportation by integrating GIS analysis, equation-based, and agent-based model

The amount of municipal solid waste (MSW) has been increasing steadily over the last decade by reason of population rising and waste generation rate. In most of the urban areas, disposal sites are usually located outside of the urban areas due to the scarcity of land. There is no fixed route map for transportation. The current waste collection and transportation are already overloaded arising from the lack of facilities and insufficient resources. In this paper, a model for optimizing municipal solid waste collection will be proposed. Firstly, the optimized plan is developed in a static context, and then it is integrated into a dynamic context using multi-agent based modelling and simulation. A case study related to Hagiang City, Vietnam, is presented to show the efficiency of the proposed model. From the optimized results, it has been found that the cost of the MSW collection is reduced by 11.3%.