Optimization of municipal solid waste collection and transportation routes, through linear programming and geographic information system: a case study from Şanlıurfa, Turkey

Empirical and simulated investigation of the solid waste reverse supply chain: A complex adaptive system perspective

Solid waste is increasing rapidly worldwide. In this study, the solid waste (household waste, construction and demolition waste and industrial waste) management systems are treated as reverse supply chain to analyze the critical operational issues based on complex adaptive system theory. At the single-layer, the complexity of the various nodes at a layer arises from rational decision-making and behavioral heterogeneity. The solid waste generation layer is employed as an example to investigate the complexity of node behavioral decisions. Regression analysis results reveal that both endogenous (Attitude, Subjective norm, and Perceived behavioral control) and exogenous factors (Economic incentive, Government supervision, Technical support) positively influence sorting behavior. The effect of Economic incentive (β=0.327P<0.001) and Attitude (β=0.249P<0.001) on sorting behavior are the largest. In the multi-layer system, different layers communicate with each other through the material and financial flows and have cross-layer impacts. An agent-based model is developed to investigate the multi-layer feedforward influence mechanism of changes in key layers (e.g., sorting rate, disposal rate) and the material and financial flows adaptive adjustment direction of the solid waste reverse supply chain. High rate of participation and accuracy of source sorting can shorten material flow paths and reduce storage and transportation costs. The increase in disposal rate encourages the transition of solid waste from backfill to resource utilization. This study provides a practice reference for solid waste reverse supply chain and related enterprises managers.

Environmental and economic assessment of waste collection and transportation using LCA: A case study

The present study is aimed to evaluate the environmental and economic burden associated with current waste collection practices in Dhanbad city, Jharkhand, India. In this study various alternatives were suggested to mitigate these impacts by optimizing resource utilization and maximizing material recovery using life cycle approach. The functional unit adapted is the daily collection service provided for 180 tonnes of municipal solid waste generated in the study area. GaBi 10.6.1 software was used for impact assessment and impacts were assessed for five scenarios in terms of five different impact categories. This study assessed the collection services and treatment options jointly. Baseline scenario (S1) representing the current collection system had the highest impacts on all the impact categories and landfilling contributed the highest (67%) to the overall impacts on the environment. Scenario S2 involved the provision of material recovery facility and considered recycling of plastic wastes having sorting efficiency of 75% which reduced the overall impacts significantly (∼971%) compared to the baseline scenario. Scenario S3 considered composting of food waste (80% food waste sent for composting) and offered further overall impacts reduction (∼1052%) compared to the baseline scenario. In scenario S4, use of electric tippers was accounted which did not offer any significant impacts reductions. Scenario S5 considered the future electricity mix at grid (2030) in India which increased the benefits of using electric tippers. S5 had the least environmental impacts providing overall reductions of ∼1063% compared to baseline scenario and provided maximum economic benefits. Sensitivity analysis results found that variation in recycling had significant change in the environmental impacts. Considering the decrease in recycling rate from 100% to 50%, the impacts on abiotic depletion fossil increased by 136%, acidification by 176%, global warming by 11%, human toxicity by 172% and terrestrial ecotoxicity by 56%.

Artificial intelligence for waste management in smart cities: a review

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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.

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.

An Optimization Model for the Design of a Sustainable Municipal Solid Waste Management System

Solid waste management is critical to sustainable, healthy, and eco-friendly cities and societies. In developing countries, only a small percentage of municipal solid waste is disposed safely, while the majority remains in the streets or disposed in open landfills. Most countries seek to establish effective and efficient solid waste management system (SWMS) that can handle and dispose of the daily generated waste at minimum cost and in a sustainable manner. Those systems usually consist of waste sources, waste collection stations, landfills, incinerators, and recycling plants, in addition to the transportation system that integrates the different sub-systems. The problem facing decision-makers while designing or reconfiguring a SWMS is to determine the optimal supply chain network design for such systems in a way that ensures the treatment and disposal of all daily generated waste at the lowest cost. In this context, this paper aims to develop a generic optimization model suitable for application in SWMS optimization in developing countries. A new mixed-integer linear programming (MILP) model is formulated for a SWMS configuration that integrates waste generation sources, collection/transfer stations, recycling plants, incinerators, and landfills. The proposed MILP model is formulated to determine the optimal number and locations of the different facilities, and the optimal flow of waste in the system that minimizes the net daily cost incurred in the system. The model has been applied in a case study on the SWMS in Fayoum Governorate, Egypt. The main contribution of this research refers both to the theoretical development of a generic MILP model that can be applied to optimally design the SWMS in developing countries, and to its operational counterpart, as per the design solutions provided in the SWMS of Fayoum Governorate (Egypt).

Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets

COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in İstanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1-A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives.

Towards the circular economy: Analysis of barriers to implementation of Turkey's zero waste management using the fuzzy DEMATEL method

Zero waste has an important position in the circular economy model in terms of manufacturing recyclable products instead of products to be consumed quickly, reducing the amount of waste, developing and implementing recycling/reuse technologies, and thus ensuring resource efficiency. A zero-waste strategy is one of the basic steps to achieve the goal of the circular economy. The number of studies conducted on Turkey's zero waste management strategies referring to the circular economy is quite limited. To fill this gap in the literature, this study aims to identify the potential barriers which have an important role in zero waste management practices in Turkey. Through a broad literature review and expert opinions, 12 key barriers for zero waste implementation in Turkey have been defined in view of circular economy principles. Then, fuzzy DEMATEL methodology is employed to examine which is the most influential barrier affecting zero waste management success and to identify the interdependence of these barriers. The results indicate that uncertainty of the goals and tactics relevant to the circular economy and lack of financial and economic aid are key causal barriers that affect Turkey's zero waste management.

Regional Distance Routes Estimation for Municipal Solid Waste Disposal, Case Study São Paulo State, Brazil

The urban population increase in the world, the economic expansion, and the rise in living standards associated with society’s habits and lifestyles accelerated the municipal solid waste (MSW) generation in undeveloped countries, such as in Brazil, in which the generation increased by 25% from 2012 to 2017. In the same period, the São Paulo state, the richest Brazilian state, increased its municipal solid waste generation by 51%. All this MSW needed to be collected and transported, and this process has a high economic and environmental cost. Therefore, this study aims to identify, using spatial analysis, the routes used by MSW trucks to estimate the distances traveled to dispose of the MSW on a regional scale considering all municipalities in the São Paulo state. The findings showed that the landfill numbers decrease, mainly individual ones, which receive MSW only from the city where it is located. Otherwise, the consortium landfills number is increasing, as well as the number of municipalities that share the same disposal site. Consequently, the distances to transport MSW from urban areas to final disposal sites increased by about 55% from 2012 to 2017, reaching 613 million kilometers during this period. This total distance is sufficient to make more than 12,806 laps on Earth and contribute to high fuel consumption and greenhouse gas emission.

Building new paths for responsible solid waste management

The amount of solid waste produced across the planet in the past decade was 1.3 billion tons (1.2 kg/year per person). Also, the significant number of publications on solid waste management (SWM) draws attention to the importance of discussing the topic to improve public health and to mitigate environmental impacts. The objectives of this article are to identify the state of the art and the scientific gaps on SWM and to propose a framework to promote it in the coming years. For this, a content analysis was carried out with the support of a bibliometric study, considering articles published in the Scopus database. The field of SWM study was classified into 12 different themes, and from this division, it was sought to identify the evolution of each of them between the 2005 and 2018 period. Content analysis and bibliometric study indicate that thermal and biological treatments are a promising trend to improve the performance of SWM. Its most important applied contribution is the generation of qualified information about SWM that can support the decision-making of public and private managers to reduce environmental impacts and improve life in urban spaces. The main academic contribution of the paper is the articulation of the most important themes on SWM, identifying the individual impact of each one of them in this field of study and the indication of the scientific trends that should guide the development of future research.

Site selection for waste vegetable oil and waste battery collection boxes: a GIS-based hybrid hesitant fuzzy decision-making approach

The amount of municipal solid waste (MSW) has been increasing rapidly in the urban centres of developing countries during the last few decades; however, municipal solid waste management (MSWM) remains inadequate. One of the largest aspects of cost of the MSWM system is the collection of waste. This paper describes a methodology that combines geographic information systems (GIS), hesitant fuzzy linguistic term set (HFLTS), and the full multiplicative form of multi-objective optimization by ratio analysis (MULTIMOORA), to determine suitable locations for waste collection boxes (named AYPIKUT), which have been designed specifically for collection of domestic waste vegetable oil and waste batteries. It takes as case study, Atakum, a district of Samsun city, Turkey. As a solution to the problem, first, a total of 88 items have been identified for consideration by seven criteria elicited from the insights of experts, and spatial analyses were performed. Multi-criteria HFLTS was then used to determine weights of the criteria. Population density was the most significant criterion affecting the selection process, and proximity to housing complexes with more than 150 dwellings was the least important. According to the weights of the seven criteria, and three rules determined by the experts, 15 AYPIKUT locations were identified using GIS. As a final step, the alternative locations (A1-A15) were ranked with the MULTIMOORA method. A5 was the most suitable site, and A6 was the least suitable site for an AYPIKUT. The results indicated the ability of the proposed model to select the suitable locations for waste collection box.

Collection of recyclable wastes within the scope of the Zero Waste project: heterogeneous multi-vehicle routing case in Kirikkale

There is an increase in the amount of resource use due to the rise in population, urbanization, and industrialization. Also, the amount of waste increases due to an increase in consumption and resource use. Countries are developing new policies depending on both decreasing resources and environmental problems caused by waste. The "Zero Waste" project was launched to recycle waste and to reduce environmental pollution in Turkey. The project aims to separate recyclable waste at its source and recycle them. One of the problems encountered in the implementation of the project is collecting the waste from temporary storage areas. In this study, the problem of transportation of wastes from temporary warehouses to the main warehouse was discussed in Kırıkkale/Turkey. A three-step solution approach has been proposed to the solution of the problem. In the first stage, the amounts of waste generated at the addresses to collect were estimated. In the second stage, the addresses to be visited are classified with an approach based on Pareto analysis according to the calculated waste amounts. According to this classification, it is planned which addresses will be visited on which day of the week. At the last stage, the problem is modeled as a heterogeneous multi-vehicle routing problem, which also takes into account the daily working hours and vehicle capacity constraints. According to the result of the mathematical model, the number of vehicles needed for waste collection, the types of vehicles, and the routes of the vehicles were found. Considering the implementation stages of the Zero Waste project, three different case studies are handled for Kırıkkale. These case studies have been solved by considering different waste rates. According to the results, the waste collection plan was made economically by visiting fewer spots in a week.