Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm

Optimizing the greenhouse gas emissions of waste transfer and transport: An integration of life cycle assessment and vehicle routing problem

This study presents a comprehensive analysis of greenhouse gas (GHG) emissions associated with waste transfer and transport, incorporating derived leachate treatment-a factor often overlooked in existing research. Employing an integration model of life cycle assessment and a vehicle routing problem (VRP) methods, we evaluated the GHG reduction potential of waste transfer and transport system. Two Chinese counties with different topographies and demographics were selected, yielding 80 scenarios that factored in waste source separation as well as vehicle capacity, energy sources, and routes. The functional unit (FU) is transferring and transporting 1 tonne waste and treating derived leachate. The GHG emissions varied from 12 to 39 kg CO2 equivalent per FU. Waste source separation emerged as the most impactful mitigation strategy, not only for the studied system but for an integrated waste management system. Followings are the use of larger capacity vehicles and electrification of the vehicles. These insights are instrumental for policymakers and stakeholders in optimizing waste management systems to reduce GHG emissions.

A significant exploration on meta-heuristic based approaches for optimization in the waste management route problems

In metropolitan cities, it is very complicated to govern the optimum routes for garbage collection vehicles due to high waste production and very dense population. Furthermore, wrongly designed routes are the source of wasting time, fuel and other resources in the collection of municipal trash procedure. The Vehicle Routing Problem (VRP) published between 2011 and 2023 was systematically analysed. The majority of the surveyed research compute the waste collecting problems using metaheuristic approaches. This manuscript serves two purposes: first, categorising the VRP and its variants in the field of waste collection; second, examining the role played by most of the metaheuristics in the solution of the VRP problems for a waste collection. Three case study of Asia continent has been analysed and the results show that the metaheuristic algorithms have the capability in providing good results for large-scale data. Lastly, some promising paths ranging from highlighting research gap to future scope are drawn to encourage researchers to conduct their research work in the field of waste management route problems.

Optimizing City-Scale Demolition Waste Supply Chain Under Different Carbon Policies

In order to establish a green, low-carbon circular development economic system, imperative goals include achieving carbon peaking and carbon neutrality. This research delves into the resource utilization of city-scale demolition waste (C&DW), aligning with environmental protection needs and sustainable development principles. The paper introduces a unique closed-loop supply chain (CLSC) model tailored for C&DW and employs a distinctive mixed integer nonlinear programming (MINLP) model for optimization. Guangzhou serves as a case study for thorough analysis, verification, and practical application of the proposed model, especially under diverse scenarios of carbon price (CP) and carbon trading (CT) policies. The key conclusions drawn from this study include the following: (1) The cost of carbon emissions is intricately influenced by both carbon emissions and carbon price, with the latter effectively regulating the carbon emissions during C&DW recycling. (2) The implementation of a CT policy, with a fixed carbon price, contributes to a further reduction in the cost of C&DW recycling treatment. (3) Under equivalent conditions, the CT policy demonstrates the potential to decrease costs and enhance the economic benefits within the building environmental protection product market. The research outcomes not only contribute to the advancement of management theory in the C&DW recycling supply chain (SC) but also provide a robust theoretical foundation for governmental initiatives aimed at introducing effective C&DW recycling management policies.

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.

A modified particle swarm optimization algorithm for a vehicle scheduling problem with soft time windows

This article constructed a vehicle scheduling problem (VSP) with soft time windows for a certain ore company. VSP is a typical NP-hard problem whose optimal solution can not be obtained in polynomial time, and the basic particle swarm optimization(PSO) algorithm has the obvious shortcoming of premature convergence and stagnation by falling into local optima. Thus, a modified particle swarm optimization (MPSO) was proposed in this paper for the numerical calculation to overcome the characteristics of the optimization problem such as: multiple constraints and NP-hard. The algorithm introduced the "elite reverse" strategy into population initialization, proposed an improved adaptive strategy by combining the subtraction function and "ladder strategy" to adjust inertia weight, and added a "jump out" mechanism to escape local optimal. Thus, the proposed algorithm can realize an accurate and rapid solution of the algorithm's global optimization. Finally, this article made typical benchmark functions experiment and vehicle scheduling simulation to verify the algorithm performance. The experimental results of typical benchmark functions proved that the search accuracy and performance of the MPSO algorithm are superior to other algorithms: the basic PSO, the improved particle swarm optimization (IPSO), and the chaotic PSO (CPSO). Besides, the MPSO algorithm can improve an ore company's profit by 48.5-71.8% compared with the basic PSO in the vehicle scheduling simulation.

Smart textile waste collection system - Dynamic route optimization with IoT

Increasing textile production is associated with an environmental burden which can be decreased with an improved recycling system by digitalization. The collection of textiles is done with so-called curbside bins. Sensor technologies support dynamic-informed decisions during route planning, helping predict waste accumulation in bins, which is often irregular and difficult to predict. Therefore, dynamic route-optimization decreases the costs of textile collection and its environmental load. The existing research on the optimization of waste collection is not based on real-world data and is not carried out in the context of textile waste. The lack of real-world data can be attributed to the limited availability of tools for long-term data collection. Consequently, a system for data collection with flexible, low-cost, and open-source tools is developed. The viability and reliability of such tools are tested in practice to collect real-world data. This research demonstrates how smart bins solution for textile waste collection can be linked to a dynamic route-optimization system to improve overall system performance. The developed Arduino-based low-cost sensors collected actual data in Finnish outdoor conditions for over twelve months. The viability of the smart waste collection system was complemented with a case study evaluating the collection cost of the conventional and dynamic scheme of discarded textiles. The results of this study show how a sensor-enhanced dynamic collection system reduced the cost 7.4% compared with the conventional one. We demonstrate a time efficiency of -7.3% and that a reduction of 10.2% in CO2 emissions is achievable only considering the presented case study.

Trajectory Planning for Coal Gangue Sorting Robot Tracking Fast-Mass Target under Multiple Constraints

Aiming at the problems of grab failure and manipulator damage, this paper proposes a dynamic gangue trajectory planning method for the manipulator synchronous tracking under multi-constraint conditions. The main reason for the impact load is that there is a speed difference between the end of the manipulator and the target when the manipulator grabs the target. In this method, the mathematical model of seven-segment manipulator trajectory planning is constructed first. The mathematical model of synchronous tracking of dynamic targets based on a time-minimum manipulator is constructed by taking the robot's acceleration, speed, and synchronization as constraints. The model transforms the multi-constraint-solving problem into a single-objective-solving problem. Finally, the particle swarm optimization algorithm is used to solve the model. The calculation results are put into the trajectory planning model of the manipulator to obtain the synchronous tracking trajectory of the manipulator. Simulation and experiments show that each joint of the robot's arm can synchronously track dynamic targets within the constraint range. This method can ensure the synchronization of the position, speed, and acceleration of the moving target and the target after tracking. The average position error is 2.1 mm, and the average speed error is 7.4 mm/s. The robot has a high tracking accuracy, which further improves the robot's grasping stability and success rate.

Resource saving and carbon footprint reduction potential of urban symbiosis strategy in express packaging waste recycling network

The booming express delivery industry corresponds to the environmental challenges caused by massive express packaging waste (EPW). An efficient logistics network is necessary link to support EPW recycling. This study, therefore, designed a circular symbiosis network for EPW recycling based on urban symbiosis strategy. The treatment of EPW in this network includes reuse, recycling and replacing. An optimization model with multi-depot collaboration combining material flow analysis and optimization methods was developed and a hybrid non-dominated sorting genetic algorithm-II (NSGA-II) was designed as technical support for designing the circular symbiosis network while quantitatively assessing the economic and environmental benefits of the network. The results show that the designed circular symbiosis option has better resource saving and carbon footprint reduction potential than both the business as usual option and circular symbiosis option without service collaboration. In practice, the proposed circular symbiosis network can save EPW recycling costs and reduce carbon footprint. This study provides a practical guideline for the application of urban symbiosis strategies to help urban green governance and the sustainable development of express companies.

Dual-objective modeling and optimization of a low-carbon waste-classified collection problem

With improved quality of life, types of waste have become increasingly complex, and waste classification has become a global issue. As a result, the world is facing a waste-classified collection problem. However, the existing research on waste collection has paid little attention to waste classification. In this paper, we consider the pretreatment and classification of waste transfer stations. In recent years, global warming caused by carbon emissions has become a serious problem. Therefore, this work proposes the first dual-objective multi-depot two-echelon green vehicle routing system with various pickups to optimize waste-classified collection based on a mixed-integer programming model. To ensure the efficiency of our developed model, we designed a multiobjective brainstorming optimization algorithm with a novel clustering strategy based on the rank-clustering method and differential mutation. Compared with two classical multiobjective optimization algorithms in various generated test instances and a real-world case, the experimental results showed that the proposed model can help sanitation departments improve the economic and environmental benefits of waste-classified collection, and the proposed algorithm is an excellent optimizer for solving associated problems.

The evaluation of energy consumption in transportation and processing of municipal waste for recovery in a waste-to-energy plant: a case study of Poland

Refuse-derived fuel (RDF) can be produced from combustible materials contained in municipal waste. This article investigates energy and material flow of waste in different scenarios for production of RDF from bulky waste, separately collected waste, and mixed municipal solid waste (MSW). We compare the proportion of energy consumption in transportation, handling waste, and processing using data from the waste collection company in South Poland. The findings show the components of the reverse supply chain consuming the highest value of energy. A model of material and energy flow has taken into consideration collection of waste and transportation by two categories of waste collection vehicles: light commercial vehicles and garbage trucks. The shipping of RDF from pre-treatment facilities uses tipper semi-trailers and walking floor trailers. The findings of the study show production of RDF from municipal solid waste consumes almost 10% of energy potential in RDF. Less energy is required for the production of RDF from bulky waste (2.2-4.8%) or separately collected waste (1.7-4.1%) depending on the efficiency of collection and selected vehicles. Transportation consumes the greatest portion of energy. For mixed municipal solid waste (MSW), it can reach 79%; for separated collection waste, 90%; and for bulky waste, up to 92% of the total energy consumed. Comparing emissions for two categories of the collection vehicles, no significant difference was found for the bulky waste collections. For mixed MSW and separately collected waste, the emissions are higher for garbage trucks. A recommendation for practitioners is optimization of routing to achieve a higher collection rate at a minimized route length. For transportation of RDF to WtE plants, vehicles with higher loading capacity are essential.

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.

PSO Algorithm-Based Design of Intelligent Education Personalization System

The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.

Collaborative Routing Optimization Model for Reverse Logistics of Construction and Demolition Waste from Sustainable Perspective

The construction industry is developing rapidly along with the acceleration of urbanization but accompanied by an increased amount of construction and demolition waste (CDW). From the perspective of sustainability, the existing research has mainly focused on CDW treatment or landfill disposal, but the challenge of reverse logistics of CDW recycling that provides overall CDW route planning for multiple participants and coordinates the transportation process between multiple participants is still unclear. This paper develops an optimization model for multi-depot vehicle routing problems with time windows (MDVRPTW) for CDW transportation that is capable of coordinating involved CDW participants and suggesting a cost-effective, environment-friendly, and resource-saving transportation plan. Firstly, economic cost, environmental pollution, and social impact are discussed to establish this optimization-oriented decision model for MDVRPTW. Then, a method combined with a large neighborhood search algorithm and a local search algorithm is developed to plan the transportation route for CDW reverse logistics process. With the numerical experiments, the computational results illustrate the better performance of this proposed method than those traditional methods such as adaptive large neighborhood search algorithm or adaptive genetic algorithm. Finally, a sensitivity analysis considering time window, vehicle capacity, and carbon tax rate is conducted respectively, which provides management implications to support the decision-making of resource utilization maximization for enterprises and carbon emission management for the government.

A global satisfaction degree method for fuzzy capacitated vehicle routing problems

There are several uncertain capacitated vehicle routing problems whose delivery costs and demands cannot be estimated using deterministic/statistical methods due to a lack of available and/or reliable data. To overcome this lack of data, third–party information coming from experts can be used to represent those uncertain costs/demands as fuzzy numbers which combined to an iterative–integer programming method and a global satisfaction degree is able to find a global optimal solution. The proposed method uses two auxiliary variables α,λ and the cumulative membership function of a fuzzy set to obtain real–valued costs and demands prior to find a deterministic solution and then iteratively find an equilibrium between fuzzy costs/demands via α and λ. The performed experiments allow us to verify the convergence of the proposed algorithm no matter the initial selection of parameters and the size of the problem/instance.

Sustainable multi-trip periodic redesign-routing model for municipal solid waste collection network: the case study of Tehran

Daily transportation of wastes due to its environmental, financial, and social aspects has been considered a challenging issue in developing countries' municipal solid waste management systems. The location of transfer stations as intermediate nodes in municipal solid waste management network affects optimal collection frequency. A sustainable multi-period and multi-trip vehicle routing problem integrated with relocation models was developed to redesign the intermediate transfer stations and find optimal vehicle routes and the best collection frequency for each municipal solid waste generation point. Regarding the social aspects of a sustainable solid waste management system, an extended social life cycle assessment methodology for redesign and routing operations was developed based on the UNEP guidelines. The social life cycle assessment methodology evaluated the probable social effects of the system throughout the entire life cycle using an iterative policy. In this study, selected impact subcategories and inventory indicators for the routing and redesign system were utilized to quantify the system social score. Besides, the developed model was solved for different problem instances. The results indicated that system social score was affected by collection frequencies decisions, redesign policy, and the number of demand nodes. Furthermore, the model was applied to a real-world case study resulting in a total cost reduction of 66% that occurred by a 86% reduction in weekly traveled distance and a 12% decrease in routing social score.

Waste collection routing problem: A mini-review of recent heuristic approaches and applications

The waste collection routing problem (WCRP) can be defined as a problem of designing a route to serve all of the customers (represented as nodes) with the least total traveling time or distance, served by the least number of vehicles under specific constraints, such as vehicle capacity. The relevance of WCRP is rising due to its increased waste generation and all the challenges involved in its efficient disposal. This research provides a mini-review of the latest approaches and its application in the collection and routing of waste. Several metaheuristic algorithms are reviewed, such as ant colony optimization, simulated annealing, genetic algorithm, large neighborhood search, greedy randomized adaptive search procedures, and others. Some other approaches to solve WCRP like GIS is also introduced. Finally, a performance comparison of a real-world benchmark is presented as well as future research opportunities in WCRP field.

A Vehicle Routing Problem Arising in the Distribution of Higher Education Institutions Exam Booklets

In this paper, the exam booklet distribution plan for the Higher Education Institutions Exam (HEIE) is studied. The exam booklets distribution should be considered as capacitated vehicle routing problem (VRP). There are vehicles with a certain capacity that are carrying exam boxes and schools where exams are held. Every school has a demand for exam boxes. Therefore, the problem is minimizing the cost/distance of distribution from the depot where exam booklets are kept to the schools with capacitated vehicles. The case of Gaziantep city with 135 nodes (one depot and 134 schools) is considered. To model and solve the problem, a mixed integer programming (MIP) model is developed and applied. Due to the large size of the problem, the VRP tool of Esri ArcGIS (well-known geographic information system (GIS) software) and OR-tool of Google are also applied to get an acceptable solution in a reasonable time. Finally, the proposed three distribution plans are compared to each other and the results are discussed. Our numerical results show that the tools of Esri ArcGIS and OR-tool of Google decrease the total route distance by 8.21% and 3.02% compared to the MIP model, respectively.

Behavior-based swarm model using fuzzy controller for route planning and E-waste collection

Nowadays, because of the increase in consumption of electronic equipment and its resource utilization, household e-waste has been generated gradually. The increase in e-waste generation brought environmental burdens as well as a health risk in several nations. The disposal of e-waste in landfills is not recommended due to some poisonous and contaminated chemicals. The improper collection of e-waste leads to a negative impact on human health and also causes air pollution, as well as the long-term effects on the environment. To address such issues, the behavior-based swarm model using a fuzzy controller (BSFC) is proposed for efficient e-waste collection. The proposed algorithm is employed to solve the problem based on routing associated with the time window for the heterogeneous fleet of the e-waste collection vehicle. The approach is provided for the online system that enables the people to request for the collection of e-waste components and also to solve the vehicle's routing problem. The optimization result demonstrates the decrease in the collection cost and also the on-time e-waste collection from the household. The method comprises the implementation of e-waste collection requests in China and India for several urban arrangements of buildings and streets. The proposed approach fetches considerable enhancement in vehicle routing plans for the e-waste collection, counting the positive social impacts for the waste collection, particularly in urban regions.

Government intervention in municipal waste collection with a sustainable approach: a robust bi-level problem

Conservation of the environment has taken a prime position among areas of concern for managers and practitioners worldwide. This study aims to provide a bi-level mathematical model for municipal waste collection considering the sustainability approach. The mathematical model with conflicting objects was proposed at the upper level of the model of maximizing government revenue from waste recycling and at the lower level of minimizing waste collection and recycling costs, which had stochastic parameters and was scenario based. A case study was conducted in the Saveh processing site (Iran). Due to the complexity of the bi-level model, the KKT approach was adopted to unify the model. Finally, the relevant calculations were performed based on actual information. The results of the problem in the case study showed the efficiency of the proposed method. Several computational analyses randomly generated different waste recycling rates and obtained significant management results.

Evaluate the locations for smart waste bins using BWM and WASPAS methods under a probabilistic linguistic environment

 The decreasing resources of the earth and the deterioration of the environment are offering new challenges for handling waste management practices. The establishment of the smart waste bins plays an important role in promoting the development of waste classification and treatment fundamentally. We developed the evaluation system for the location selection problem of smart waste bins. Considering the uncertainty in the location selection of smart waste bins, the probabilistic linguistic term sets (PLTSs) are selected to express the evaluation information. Because of the excellent performance in weight-determing, the best worst method (BWM) is chosen to get the weight of criteria. While the weighted aggregated sum product assessment (WASPAS) method could handle both the qualitative and quantitative information, which are considered to derive the final ranking of the alternatives. This paper proposed a new group multi-criteria decision making approach integrating the BWM and the WASPAS with probabilistic linguistic information. Finally, in the empirical example, a sensitivity analysis shows that the proposed method is stable, a comparison analysis with PL-TOPSIS, PL-VIKOR, and PL-TODIM reflects its effectiveness and rationality, and the managerial implication verifies its usefulness and practicability, which also give guide to the company, government and resident.

Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model

Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes; moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixed-integer linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously, of the population's exposure to pollution. This paper considers some realistic assumptions for the first time, including location-routing problem, time window-based green vehicle routing problem, vehicles scheduling, vehicles failure, split delivery, population risk, and load-dependent fuel consumption to manage both infectious and non-infectious medical waste. We apply a fuzzy goal programming approach for solving the proposed bi-objective model, and the efficiency of the proposed model and solution approach is assessed using data related to 13 nodes of medical waste production in a location west of Tehran.

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

Hybrid modified ant system with sweep algorithm and path relinking for the capacitated vehicle routing problem

Vehicle routing problem is a widely researched combinatorial optimization problem. We developed a hybrid of three strategies—a modified ant system, a sweep algorithm, and a path relinking—for solving a capacitated vehicle routing optimization problem, a vehicle routing problem with a capacity constraint. A sweep algorithm was used to generate initial solutions and assign customers to vehicles, followed by a modified ant system to generate new generations of good solutions. Path relinking was used for building a better solution (candidate) from a pair of guiding and initial solutions. Finally, a local search method—swap, insert, reverse and greedy search operations—was used to prevent solutions from getting trapped in a local minimum. Performance of the proposed algorithm was evaluated on three datasets from CVRPLIB. Our proposed method was at least competitive to state-of-the-art algorithms in terms of the total route lengths. It even surpassed the best-known solution in the P-n55-k8 instance. Our findings can lower the transportation cost by reducing the travelling distance and efficiently utilizing the vehicle capacity.

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.

IoT-Enabled Solid Waste Management in Smart Cities

The Internet of Things (IoT) paradigm plays a vital role for improving smart city applications by tracking and managing city processes in real-time. One of the most significant issues associated with smart city applications is solid waste management, which has a negative impact on our society’s health and the environment. The traditional waste management process begins with waste created by city residents and disposed of in garbage bins at the source. Municipal department trucks collect garbage and move it to recycling centers on a fixed schedule. Municipalities and waste management companies fail to keep up with outdoor containers, making it impossible to determine when to clean them or when they are full. This work proposes an IoT-enabled solid waste management system for smart cities to overcome the limitations of the traditional waste management systems. The proposed architecture consists of two types of end sensor nodes: PBLMU (Public Bin Level Monitoring Unit) and HBLMU (Home Bin Level Monitoring Unit), which are used to track bins in public and residential areas, respectively. The PBLMUs and HBLMUs measure the unfilled level of the trash bin and its location data, process it, and transmit it to a central monitoring station for storage and analysis. An intelligent Graphical User Interface (GUI) enables the waste collection authority to view and evaluate the unfilled status of each trash bin. To validate the proposed system architecture, the following significant experiments were conducted: (a) Eight trash bins were equipped with PBLMUs and connected to a LoRaWAN network and another eight trash bins were equipped with HBLMUs and connected to a Wi-Fi network. The trash bins were filled with wastes at different levels and the corresponding unfilled levels of every trash bin were monitored through the intelligent GUI. (b) An experimental setup was arranged to measure the sleep current and active current contributions of a PBLMU to estimate its average current consumption. (c) The life expectancy of a PBLMU was estimated as approximately 70 days under hypothetical conditions.

Optimization and profit distribution in a two-echelon collaborative waste collection routing problem from economic and environmental perspective

In order to reduce waste collection costs and realize sustainable urban development, this paper investigates a two-echelon collaborative waste collection vehicle routing problem (2E-CWCVRP), considering the cooperation and profit distribution between participants in the collection network. An optimization model for 2E-CWCVRP with the aim to minimize total costs and carbon emissions is constructed. Then, a three-stage solution approach is developed to solve this model, including a k-means clustering for simplifying the problem, and a hybrid heuristic for searching the optimal vehicle routes based on Clarke & Wright algorithm and an adaptive large neighborhood search algorithm (CW-ALNS). Finally, an improved Shapley value model is constructed for determining the costs and carbon emissions reduction amount and the best alliance sequence of each participant. The experiment results indicate that: (1) the effectiveness of CW-ALNS algorithm is verified through the benchmark instances; (2) the costs and carbon emissions of the collection network could be reduced simultaneously after the implementation of cooperation; (3) constructing a large collection and transfer network is more efficient than dividing the network into several individual parts. Finally, different alliance sequences are analyzed from the economics and environment perspective and the best alliance sequences are determined.

The influencing factors of Harbin (China) residents' satisfaction with municipal solid waste treatment

China is experiencing an enormous increase in municipal household solid waste (MHSW) generation and is facing multiple problems associated with the treatment of MHSW. This paper analyses factors affecting residents' satisfaction with MHSW treatment performance. Six factors were identified by the Delphi method: (a) pick-up frequency by waste collection vehicles, (b) fund supply situation, (c) charging standard for waste treatment, (d) waste bin arrangement, (e) laws and regulations, (f) publicity and education. We examine the significance of these six influencing factors, estimating binary logistic regression models. Data for this study are derived from the survey responses of 469 households in Harbin, one of the largest cities in northeast China. The results indicate that 'pick-up frequency by waste collection vehicles' is ranked the first and most important determinant of Harbin residents' satisfaction with MHSW treatment; this is closely followed by 'publicity and education'. The third and fourth significant influencing factors, respectively, are 'fund supply situation' and 'charging standard for waste treatment'. The last two factors are 'laws and regulations' and 'waste bin arrangement'. By understanding the influence of various factors on residents' satisfaction, this study aims to help in designing an effective waste management system to reduce the cost of MHSW management, and to raise the residents' satisfaction with municipal solid waste treatment. Based on the research findings, we advocate that establishing a reasonable waste transport (pick-up) system as well as strengthening publicity and education of waste management are key to improving residents' satisfaction with the MHSW treatment performance.

Municipal solid waste transfer station planning through vehicle routing problem-based scenario analysis

Collection, transfer and transport of municipal solid waste (MSW) is one of the most challenging tasks of local municipalities and occupies a significant portion of the municipal expenses. Appropriately planned transfer stations (TSs) can increase system performance and reduce costs. Therefore, this study aims to develop a spatial modelling approach for investigating the optimum siting and economic impacts of MSW TSs. A geographic information system-based land suitability analysis was conducted to identify potential TS sites followed by a scenario analysis to determine optimum TS sites and waste collection routes for various collection vehicle capacities through vehicle routing problem modelling. The approach was implemented in the southeastern region of İzmir where a new landfill is to be built to serve three district municipalities. The addition of a TS in the study area reduced the collection time and number of shifts by 9%. Similarly, collection with large vehicles decreased the collection time and number of shifts by 25% and 17%, respectively. However, the unit cost of the system increased from 17.52 to 18.60 US$ metric tonnes^-1 waste with the TS addition because of the additional costs of the TS. The results indicated that TS addition is not economically feasible in the study area because of the small collection vehicle fleet (eight collection vehicles), proximity of landfill to areas with high waste density and district level collection. On the other hand, TS addition resulted in lower fuel consumption which may help reduce fuel-induced air pollution.

Hybrid Cuckoo Search for the Capacitated Vehicle Routing Problem

Having the best solution for Vehicle Routing Problem (VRP) is still in demand. Beside, Cuckoo Search (CS) is a popular metaheuristic based on the reproductive strategy of the Cuckoo species and has been successfully applied in various optimizations, including Capacitated Vehicle Routing Problem (CVRP). Although CS and hybrid CS have been proposed for CVRP, the performance of CS is far from the state-of-art. Therefore, this study proposes a hybrid CS with Simulated Annealing (SA) algorithm for the CVRP, consisting of three improvements—the investigation of 12 neighborhood structures, three selections strategy and hybrid it with SA. The experiment was conducted using 16 instances of the Augerat benchmark dataset. The results show that 6 out of 12 neighborhood structures were the best and the disruptive selection strategy is the best strategy. The experiments’ results showed that the proposed method could find optimal and near-optimal solutions compared with state-of-the-art algorithms.

Development of a coded suite of models to explore relevant problems in logistics

Logistics is the aspect of the supply chain which is responsible of the efficient flow and delivery of goods or services from suppliers to customers. Because a logistic system involves specialized operations such as inventory control, facility location and distribution planning, the logistic professional requires mathematical, technological and managerial skills and tools to design, adapt and improve these operations. The main research is focused on modeling and solving logistic problems through specialized tools such as integer programing and meta-heuristics methods. In practice, the use of these tools for large and complex problems requires mathematical and computational proficiency. In this context, the present work contributes with a coded suite of models to explore relevant problems by the logistic professional, undergraduate/postgraduate student and/or academic researcher. The functions of the coded suite address the following: (1) generation of test instances for routing and facility location problems with real geographical coordinates; (2) computation of Euclidean, Manhattan and geographical arc length distance metrics for routing and facility location problems; (3) simulation of non-deterministic inventory control models; (4) importing/exporting and plotting of input data and solutions for analysis and visualization by third-party platforms; and (5) designing of a nearest-neighbor meta-heuristic to provide very suitable solutions for large vehicle routing and facility location problems. This work is completed by a discussion of a case study which integrates the functions of the coded suite.

Network design of a household waste collection system: A case study of the commune of Renca in Santiago, Chile

This study proposes a design of a household waste collection system based on a two-stage procedure. First, the bin location-allocation problem is solved by selecting collection sites from a set of potential sites, and determining the type and number of bins at each selected collection site. Second, bin-to-bin waste collection routes are obtained for a fleet of homogeneous vehicles that are restricted by either work shift duration or vehicle capacity. Mixed integer linear programming (MILP) models are proposed for both stages, considering the particular characteristics of the problem. The models are applied to a real-world instance in the commune of Renca in Santiago, Chile. The results of first stage indicate an important preference for small bins since they have a lower unitary cost. Due to the large size of the real instance, a Large Neighborhood Search (LNS) heuristic is used in the second stage to find good feasible vehicle routing solutions in a reasonable period of time. The results for the routing phase suggest a larger number of routes in the morning work shift since these routes have shorter distances. The LNS heuristic presents a satisfactory behavior when compared to the MILP model with small instances. The proposed bin-to-bin household waste collection vehicle routing presents a more efficient solution than the existing door-to-door waste collection in the commune of Renca with respect to the total daily traveled distance and the average work shift duration. Finally, a sensitivity analysis is presented and discussed for both models.

Biodegradable and non-biodegradable fraction of municipal solid waste for multifaceted applications through a closed loop integrated refinery platform: Paving a path towards circular economy

An increase in population, rapid urbanization and industrialization has accelerated the rate of municipal solid waste generation. The current disposal of solid waste is a burgeoning issue and it's in immediate need to shift the existing disposal processes to a sustainable manner. Circular economy (CE) is a conceptual model which is been used for better use of resources and minimization of waste in a closed loop approach which could be appropriate for waste management. In this context, the present review illustrates the effective use of biodegradable and non-biodegradable fraction of solid waste in a closed loop integrated refinery platforms for the recovery of bioenergy resources and for the production of value added products. The biodegradable fraction of solid waste could be treated by advanced biological processes with the simultaneous production of bioenergy such as biohydrogen, biomethane, bioelectricity, etc., and other value added products like butanol, ethanol, methanol etc. The scheme illustrates the closed loop approach, the bioenergy generated from the biodegradable fraction of solid waste could be used for the operation of internal combustion engines and the energy could be further used for processing the waste. The non-biodegradable fraction of solid waste could be used for construction and pavement processes. Overall the study emphasizes the paradigm shift of solid waste management concepts from linear economy to a circular economy following the "Zero Waste" concept. The study also explains the circular economy policies practiced for solid waste management that stimulates the economy of the country and identify the pathways to maximize the local resources. In addition the review addresses the advanced information and communication technologies to unfold the issues and challenges faced in the solid waste management. The smart governance of managing waste using the "Internet of Things" (IoT) is one of the great precursors of technological development that could lead innovations in waste management.

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.

Modeling the Municipal Waste Collection Using Genetic Algorithms

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.

Optimization of a Capacitated Vehicle Routing Problem for Sustainable Municipal Solid Waste Collection Management Using the PSO-TS Algorithm

Sustainable management of municipal solid waste (MSW) collection has been of increasing concern in terms of its economic, environmental, and social impacts in recent years. Current literature frequently studies economic and environmental dimensions, but rarely focuses on social aspects, let alone an analysis of the combination of the three abovementioned aspects. This paper considers the three benefits simultaneously, aiming at facilitating decision-making for a comprehensive solution to the capacitated vehicle routing problem in the MSW collection system, where the number and location of vehicles, depots, and disposal facilities are predetermined beforehand. Besides the traditional concerns of economic costs, this paper considers environmental issues correlated to the carbon emissions generated from burning fossil fuels, and evaluates social benefits by penalty costs which are derived from imbalanced trip assignments for disposal facilities. Then, the optimization model is proposed to minimize system costs composed of fixed costs of vehicles, fuel consumption costs, carbon emissions costs, and penalty costs. Two meta-heuristic algorithms, particle swarm optimization (PSO) and tabu search (TS), are adopted for a two-phase algorithm to obtain an efficient solution for the proposed model. A balanced solution is acquired and the results suggest a compromise between economic, environmental, and social benefits.

A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against restricted mass balances

Long-term planning of municipal solid waste management systems is a complex decision making problem which includes a large number of decision layers. Since all different waste treatment and disposal processes will show different responses to each municipal solid waste component, it is necessary to separately evaluate all waste components for all processes. This obligation creates an obstacle in the programming of mass balances for long-term planning of municipal solid waste management systems. The development of an ideal mixed integer linear programming model that can simultaneously respond to all essential decision layers including waste collection, process selection, waste allocation, transportation, location selection, and capacity assessment has not been made possible yet due to this important modeling obstacle. According to the current knowledge of the literature, all mixed integer linear programming studies aiming to address this obstacle so far have had to restrict many different possibilities in their mass balances. In this study, a novel mixed integer linear programming model was formulated. ALOMWASTE, the new model structure developed in this study, was built to take into consideration different process, capacity, and location possibilities that may occur in complex waste management processes at the same time. The results obtained from a case study showed the feasibility of new mixed integer linear programming model obtained in this study for the simultaneous solution of all essential decision layers in an unrestricted mass balance. The model is also able to provide significant convenience for the multi-objective optimization of financial-environmental-social costs and the solution of some uncertainty problems of decision-making tools such as life cycle assessment.

Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

Due to the rise of social and environmental concerns on global climate change, developing the low-carbon economy is a necessary strategic step to respond to greenhouse effect and incorporate sustainability. As such, there is a new trend for the cold chain industry to establish the low-carbon vehicle routing optimization model which takes costs and carbon emissions as the measurements of performance. This paper studies a low-carbon vehicle routing problem (LC-VRP) derived from a real cold chain logistics network with several practical constraints, which also takes customer satisfaction into account. A low-carbon two-echelon heterogeneous-fleet vehicle routing problem (LC-2EHVRP) model for cold chain third-party logistics servers (3PL) with mixed time window under a carbon trading policy is constructed in this paper and aims at minimizing costs, carbon emissions and maximizing total customer satisfaction simultaneously. To find the optimal solution of such a nondeterministic polynomial (NP) hard problem, we proposed an adaptive genetic algorithm (AGA) approach validated by a numerical benchmark test. Furthermore, a real cold chain case study is presented to demonstrate the influence of the mixed time window’s changing which affect customers’ final satisfaction and the carbon trading settings on LC-2EHVRP model. Experiment of LC-2EHVRP model without customer satisfaction consideration is also designed as a control group. Results show that customer satisfaction is a critical influencer for companies to plan multi-echelon vehicle routing strategy, and current modest carbon price and trading quota settings in China have only a minimal effect on emissions’ control. Several managerial suggestions are given to cold chain logistics enterprises, governments, and even consumers to help improve the development of cold chain logistics.

A fuzzy optimization approach to the capacitated node-routing problem for municipal solid waste collection with multiple tours: A case study

In many countries, municipal solid waste management is considered a very important challenge, and the most relevant costs in this field are dedicated to the collection process. Therefore, this study aimed to propose a mathematical model with multiple depots and multiple intermediate facilities to minimize fixed and variable costs of waste collection. Intermediate facilities are used in the developed countries in their waste collection network, because these facilities reduce the long-term costs of waste management and increase the quality of the waste collection process. Also, in reality, the amount of waste generated per day is not deterministic, so, to cope with the issue of uncertainty in the amount of waste, a fuzzy optimization approach was considered. Furthermore, a system where vehicles that could collect the wastes in multiple tours, with a maximum number of tours for each vehicle, was also considered. Due to the high complexity of this model, a genetic algorithm was elaborated. Further, the efficiency of the proposed algorithm was confirmed by comparison with the exact solution in small dimensions. It should be noted that the initial solution of this algorithm was obtained by a proposed heuristic algorithm. Finally, a case study on the vehicle routing of municipal solid waste was conducted in a district of Tehran, Iran. Moreover, the solutions of the model were validated by comparing the results of the proposed model and the current real-life situation. The contractors could improve vehicle routes and reduce costs by implementing the results of the proposed model, without any additional cost.

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.

Regional Differences in Municipal Solid Waste Collection Quantities in China

The rapid growth in urban population has led to a dramatic increase in municipal solid waste (MSW) generation, with ramifications more pronounced in developing countries. The regional Chinese governments have made great efforts to reduce MSW generation and collection quantities. However, the results of these efforts vary across cities. The purpose of this paper is to analyze the regional differences in MSW collection quantities. A two-level hierarchical linear model (HLM) was used to examine the variations in MSW collection quantities among 287 prefecture-level cites in China over the period from 2008 to 2017. The analysis reveals a strong negative correlation between the regional economic development level and the growth trend of MSW collection quantities. The empirical findings indicate that the level of economic development and waste collection measures are critical determinants of MSW collection quantities.

Optimal Path Planning for Selective Waste Collection in Smart Cities

Waste collection is one of the targets of smart cities. It is a daily task in urban areas and it entails the planning of waste truck routes, taking into account environmental, economic and social factors. In this work, an optimal path planning algorithm has been developed together with a practical software platform for smart and sustainable cities that enables computing the optimal waste collection routes, minimizing the impact, both environmental (CO₂ emissions and acoustic damage) and socioeconomic (number of trucks to be used and fuel consumption). The algorithm is executed in Net2Plan, an open-source planning tool, typically used for modeling and planning communication networks. Net2Plan facilitates the introduction of the city layout input information to the algorithm, automatically importing it from geographical information system (GIS) databases using the so-called Net2Plan-GIS library, which can also include positions of smart bins. The algorithm, Net2Plan tool and its extension are open-source, available in a public repository. A practical case in the city of Cartagena (Spain) is presented, where the optimal path planning for plastic waste collection is addressed. This work contributes to the urban mobility plans of smart cities and could be extended to other smart cities scenarios with requests of optimal path planning.

Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading

In order to cut the costs of third-party logistics companies and respond to the Chinese government’s low-carbon economy plans, this paper studies the more practical and complex open vehicle routing problem, which considers low-carbon trading policies. A low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW) model is constructed with minimum total costs, which include the driver’s salary, penalty costs, fuel costs and carbon emissions trading costs. Then, a two-phase algorithm is proposed to handle the model. In the first phase, the initial local solution is obtained with particle swarm optimization (PSO); in the second phase, we can obtain a global optimal solution through a further tabu search (TS). Experiments proved that the proposed algorithm is more suitable for small-scale cases. Furthermore, a series of experiments with different values of carbon prices and carbon quotas are conducted. The results of the study indicate that, as carbon trading prices and carbon quotas change, total costs, carbon emission trading costs and carbon emissions are affected accordingly. Based on these academic results, this paper presents some effective proposals for the government’s carbon trading policy-making and also for logistics companies to have better route planning under carbon emission constraints.

Using applied operations research and geographical information systems to evaluate effective factors in storage service of municipal solid waste management systems

One of the important elements of municipal solid waste management (MSWM) system is waste storage service. Residents deal with the system in this stage and the efficiency of waste collection element, the most expensive element of waste management system, relies on the performance of storage service. In this study, the performance of two different models including minimize facilities (MF) and maximize capacitated coverage (MCC) was investigated to find optimal locations for storage stations in Alandasht district located in Mashhad- Iran. Four effective factors including total service coverage, residential engagement, surplus container capacity devoted to each station and the ratio of the standard deviation to the arithmetic mean of solid waste allocated to each station were considered to compare these models. The MF model provided the highest service coverage by proposing 26 stations covering 98.56 percent of total residences. According to 26 stations proposed with MF model, MCC was run with 26, 27, 25 and 24 stations. MCC-27 provided the maximum attendance of residents with 54.47 percent. However, the most economical container distribution to the stations proposed with MCC-24 by presenting the minimum ratio of surplus devoted capacity to total demand, 33.74 percent. Finally, MCC-25 provided better distribution of residents to the storage stations, i.e., the minimum ratio of the standard deviation to the average solid waste devoted to stations, 22.13 percent. service area (SA) analysis applied to MF, MCC-25 and MCC-24 showed more than 60 percent of residences are located between 0 and 100 m walking distance for these analyses.