Moving Towards Electrified Waste Management Fleet: State of the Art and Future Trends

Abstract

Efficient waste management remains critical to achieving sustainable urban development, addressing challenges related to resource conservation, environmental preservation, and carbon emissions reduction. This review synthesizes advancements in waste management technologies, focusing on three transformative areas: optimization techniques, the integration of electric vehicles (EVs), and the adoption of smart technologies. Optimization methodologies, such as vehicle routing problems (VRPs) and dynamic scheduling, have demonstrated significant improvements in operational efficiency and emissions reduction. The integration of EVs has emerged as a sustainable alternative to traditional diesel fleets, reducing greenhouse gas emissions while addressing infrastructure and economic challenges. Additionally, the application of smart technologies, including Internet of Things (IoT), artificial intelligence (AI), and the Geographic Information System (GIS), has revolutionized waste monitoring and decision-making, enhancing the alignment of waste systems with circular economy principles. Despite these advancements, barriers such as high costs, technological complexities, and geographic disparities persist, necessitating scalable, inclusive solutions. This review highlights the need for interdisciplinary research, policy standardization, and global collaboration to overcome these challenges. The findings provide actionable insights for policymakers, municipalities, and businesses, enabling data-driven decision-making, optimized waste collection, and enhanced sustainability strategies in modern waste management systems.

1. Introduction

1.1. Background and Challenges in Waste Management

Effective waste management has become a global imperative as urban populations expand and economic development accelerates. Municipal solid waste (MSW) generation has grown exponentially, with estimates suggesting a rise to 3.4 billion tons annually by 2050 if current trends continue. Inefficient waste management systems contribute to significant environmental and societal challenges, including greenhouse gas (GHG) emissions, resource depletion, and public health risks [1,2]. Increasingly, policymakers and researchers recognize the need for transformative approaches that combine efficiency with sustainability.

Advancements in waste collection and treatment methodologies reflect this shift towards sustainable systems. Recent studies emphasize integrating technologies such as EVs, smart IoT devices, and AI to address operational inefficiencies while reducing the environmental footprint [3,4]. Additionally, concepts like the circular economy have gained traction, encouraging a rethinking of waste as a resource rather than a disposal problem [5,6].

Despite these advances, the sector faces persistent challenges. Traditional waste collection methods, dominated by diesel-powered vehicles and static routing systems, contribute significantly to carbon emissions [7]. Moreover, urban congestion, varying waste generation patterns, and inefficient recycling processes exacerbate operational inefficiencies [8]. Globally, inadequate waste management systems account for approximately 3–5% of anthropogenic GHG emissions and they are major contributors to marine plastic pollution and land degradation [9].

While advanced strategies, such as transfer stations (TSs), compartmentalized vehicles, and real-time sensor data, can improve efficiency, reduce collection time, and lower fuel consumption, their implementation often comes with high costs and logistical challenges. For example, in İzmir, Turkey, the introduction of TSs reduced collection shifts by 9% but increased unit costs, making the solution economically unfeasible in resource-constrained settings [10]. Additionally, limited vehicle fleets, proximity to disposal sites, and localized operations further complicate the adoption of advanced systems. These global challenges highlight the need for innovative and scalable waste management strategies that address financial, logistical, and environmental constraints to ensure long-term sustainability. The broader challenge of MSW management lies in achieving a balance between operational efficiency, economic feasibility, and environmental sustainability. Rapid urbanization has significantly increased waste volumes, straining municipal systems and leading to inefficiencies and environmental degradation, particularly in emerging economies where infrastructure is inadequate, and traditional practices like fixed-route collection and mixed waste processing remain prevalent [11].

1.2. Objectives and Scope of the Review

This review aims to provide a comprehensive examination of contemporary strategies in waste management, focusing on three transformative themes:

• Optimization Techniques in Waste Collection and Transportation: examining the latest methodologies, including VRPs, dynamic scheduling, and simulation-based models, to improve the cost-efficiency and environmental impact of waste logistics.
• EVs integration in Waste Logistics: assessing the role of EVs in reducing the carbon footprint of MSW collection while addressing the challenges of infrastructure and economic feasibility.
• Role of Smart Technologies in Modern Waste Management Systems: exploring the integration of AI, IoT, and big data to enhance operational efficiency, enable real-time decision-making, and support circular economy principles.

By synthesizing insights from cutting-edge research, this review seeks to bridge the gap between theoretical advancements and practical implementations. The goal is to equip policymakers, urban planners, and industry leaders with a survey of actionable strategies for establishing efficient, sustainable, and technology-driven waste management systems. These objectives are discussed throughout the review: practical applications of optimization, EV integration, and smart technologies are explored in Section 4, while policy and implementation challenges are addressed in Section 5. Recommendations for overcoming these barriers and achieving sustainable waste management are summarized in the Conclusion.

To better illustrate the scope, key challenges, and proposed solutions, Figure 1 provides a categorized summary of insights and gaps identified in the literature. The figure offers a structured overview of the interconnected elements in waste management, emphasizing optimization as the core driver of efficiency. Optimization is enhanced through GIS, AI, and IoT, improving waste collection routing and enabling smarter EV fleet operations through predictive scheduling and real-time monitoring. However, these advancements are not isolated; rather, significant overlaps exist among optimization, smart technologies, and EV integration, demonstrating their interdependence in modern waste management systems. Smart technologies contribute through advancements in AI and IoT tools, improving both waste collection and EV fleet management, but are hindered by technological barriers and policy gaps that affect their integration. AI and IoT, for instance, simultaneously support optimization strategies and enhance EV fleet operations, illustrating the cross-cutting nature of these innovations. Similarly, EV integration benefits from optimization and smart technologies but faces challenges such as data integration issues, impacting its role in achieving sustainability goals. The updated figure explicitly highlights these overlaps, reinforcing the need for a holistic approach that considers the mutual dependencies between these domains to drive efficiency, sustainability, and technological advancement in waste management.

The remainder of this paper is structured as follows: Section 2 outlines the framework and methodology, Section 3 provides a thematic literature review of optimization, EV integration, and smart technologies, Section 4 discusses and analyzes findings, Section 5 discusses identified gaps and barriers and Section 6 concludes with recommendations. An annex is included, presenting

Table A1. Recent literature in waste collection and optimization techniques.

Methodology	Objective	Advantages	Disadvantages/Gaps	Limitations	Dataset	Case Study	No.	Year
Machine Learning (Support Vector Machine (SVM), Random Forest, XGBoost)	Enhance economic efficiency and reduce environmental impact in waste management	High accuracy in forecasting waste generation trends	Challenges in data quality and generalization of results	Requires more standardized and comprehensive datasets	World Bank’s dataset	Global	[3]	2024
Life Cycle Assessment (LCA)	Evaluate carbon emissions for waste collection vehicles	Identifies optimal fuel types and routes for reduced emissions	Limited consideration of spatial constraints in routing	Focused only on carbon footprint without broader environmental impact considerations	GlobalTRANS tool data	Madrid, Spain	[4]	2017
Generalized Vehicle Routing Model	Develop a vehicle routing model with multiple transfer stations and time constraints	Reduced traveling distance and operational time	Limited to specific node structures and vehicles’ characteristics	Results dependent on specific node structure configurations	Local MSW data	Danang, Vietnam	[10]	2016
Mixed-Integer Linear Programming (MILP)	Develop optimization models for real-time waste collection	Real-time data integration improves collection efficiency	Limited integration of factors like climate and seasonal variations	Requires practical implementation testing in varied geographical contexts	Simulated and real-time data	Not specified	[11]	2020
Multi-objective MINLP; AMSEO	Introduce a coordinated framework for sustainable waste management optimizing financial, environmental, and social objectives	Achieved practical solutions aligning with sustainability goals; AMSEO outperformed SEO and SA algorithms	Limited real-world cases; deterministic model lacks stochastic elements	No dynamic modeling; requires closed-loop logistics networks	Medium-scale synthetic data	Not specified	[12]	2021
Discrete Choice Model (DCM); Hybrid Genetic-PSO algorithm; Best Worst Method (BWM)	Optimize municipal waste collection with dynamic routing and IoT-enabled bins	Real-time adaptability; multi-compartment vehicles enhance efficiency	Relies on precise data for effectiveness; data collection challenges	Requires AI for routing and expansion to multi-depot scenarios	IoT-enabled real-time waste data	Hypothetical urban setup	[13]	2023
Stochastic VRP; Chance-Constrained Programming; Metaheuristic algorithms	Minimize transportation costs and maximize recycling revenue	Addresses stochastic uncertainty; enhances recycling efficiency	Limited real-world applications; computational complexity	Needs integration with smart city frameworks for broader adoption	Simulated urban data	Smart city simulation	[15]	2021
Bipartite graph model; Metaheuristic algorithm	Optimize pickup/delivery routes with limited container availability	Tailored to real-world constraints; fleet size optimized	Limited generalizability; static approach lacks dynamic factors	Requires incorporation of dynamic elements and real-time routing adjustments	Real-world data from Italy	Case study in Italy	[16]	2018
PCGVRP; Hybrid LSHA (PSO + SA)	Minimize GHG emissions and optimize dynamic routing	Environmentally friendly; dynamic and adaptive routing	Limited scalability; single-objective focus	Needs multi-objective optimization for broader environmental benefits	Sensor-based waste-level data	Not specified	[17]	2020
BSA; TWL optimization	Optimize routes and minimize fuel costs/CO2 emissions	High collection efficiency; TWL optimization improves operations	Limited to specific datasets; lacks stochastic elements	Requires testing scalability with larger datasets and real-time factors	Synthetic datasets with TWL	Hypothetical urban environment	[18]	2017
Two-echelon VRP; Metaheuristic + novel heuristics; BWM	Reduce costs and CO2 emissions; integrate IoT data	IoT integration enhances real-time adaptability	Dependence on IoT infrastructure; computational complexity	Needs exploration of dynamic routing with stochastic travel times and real-world validations	IoT-based datasets	Hypothetical smart city	[19]	2023
Multi-trip VRP; SA; Case study in Iran	Minimize costs; optimize multiple trips and time windows	Practical and cost-effective; supports time-window constraints	Limited scalability; single-location validation	Requires expansion to larger datasets and stochastic elements	Real-world data	Urban Iran case study	[20]	2019
Capacitated VRP; PSO; TWL scheduling	Optimize routes and improve waste collection efficiency	Adaptive scheduling; computational efficiency	Static optimization; limited real-world validation	Needs testing with dynamic and multi-depot scenarios	Synthetic datasets	Not specified	[21]	2018
Linear programming; Hybrid GA	Optimize dynamic routes; minimize costs and penalties.	Effective cost reduction; dynamic adaptability.	Limited real-world implementation.	Focuses on transportation costs and penalties; excludes uncertain factors like time windows.	Modified Valorsul dataset	None	[22]	2023
Hybrid PSO-Tabu Search (TS); Two-phase algorithm	Optimize economic, environmental, and social costs.	Integrates sustainability objectives comprehensively.	Social factors simplified to penalty costs.	Assumes pre-determined vehicle/depot parameters.	Simulated datasets for MSW collection	None	[23]	2020
SA; MATLAB	Minimize route costs and distances.	Effective for clustered demand; cost-efficient.	Simplistic assumptions in MATLAB implementation.	Limited adaptability for real-world cases.	GPS data from Malaysia; Solomon benchmark datasets	Bidor region, Malaysia	[24]	2021
GIS-based route optimization; ArcGIS Pro	Minimize travel distance and fuel costs.	Significant fuel cost savings (11.6%).	Limited analysis of operational feasibility.	Assumes static driver preferences.	GPS data from Khulna, Bangladesh	Khulna City, Bangladesh	[25]	2024
GIS-based Smart Collection System (SCS); Knowledge-based decision-making	Implement dynamic routes based on bin fill levels.	Real-world applicability in UAE; operational cost savings.	Limited generalizability beyond UAE.	Excludes labor-related constraints.	Field survey data from UAE households	Um Gafa, UAE	[26]	2019
Multi-objective optimization; Evolutionary algorithms	Minimize route distance; maximize waste collection efficiency.	Intelligent system leveraging waste profiles; prioritization of overflow bins.	Limited real-time dynamic response.	Lacks time-window constraints for drivers.	Real data from Jeju Island, South Korea	Jeju Island, South Korea	[27]	2020
Spatial GIS; Modified Dijkstra; GA	Minimize vehicle travel routes; integrate GIS with optimization.	GIS-based interface; reduced travel time.	Focuses only on GIS data; excludes external constraints.	Assumes consistent GIS parameters.	Real dataset from Sfax City, Tunisia	Sfax City, Tunisia	[28]	2018
GIS-based land suitability analysis; Scenario-based VRP	Optimize transfer station location and collection routes	Strategic planning; environmental benefits	High infrastructure costs; not scalable for small fleets	Requires cost-effective TS planning for broader applications	Real-world spatial data	Case study in Izmir, Turkey	[64]	2020
Ant Colony Optimization (ACO); High-Performance Computing (HPC) infrastructure	Optimize large-scale waste collection routes.	Significant computational time reduction; effective for large datasets.	Requires HPC infrastructure; scalability to other cases untested.	Dependent on supercomputing resources.	Waste collection dataset	Salomon supercomputing cluster	[65]	2018
NSGA-III + Simulated Annealing; Probabilistic insertion	Balance economic, environmental, and social aspects.	Comprehensive model; real-world validation.	Assumes deterministic input values; excludes multiple distribution centers.	Lacks real-time adaptability.	Solomon datasets	Xuhui District, Shanghai	[66]	2022
Bi-level optimization; ACO with route improvement	Minimize distance and vehicle use; optimize scheduling.	Improved service level and efficiency.	Focuses on specific policy context; broader applications untested.	Assumes limited vehicle classes and routes.	Simulations and optimization experiments	Taiwan municipal waste system	[67]	2015
Modified K-Means (M-KMA); Variable Neighborhood Search (VNS)	Handle dynamic requests; minimize routing risks.	Adaptability to dynamic requests.	Correlation between dynamism and travel distance unexplored.	Lacks hybrid methodologies.	Simulated waste collection data	None	[68]	2017
ACO	Optimize MSW collection routes; minimize transportation cost and carbon emissions.	31.2% reduction in total cost, 60% in fixed cost, and 25.3% in emission cost.	Limited to a single urban case.	Generalizability to different cities or dynamic conditions not explored.	Urban MSW system	None	[69]	2023
Bee Algorithm (BA); CVRP and CVRPTW models	Optimize waste routes; include capacity and time windows.	High vehicle utilization; route efficiency improvement.	Limited scope for multi-depot scenarios.	Tested on ITF Sunter project-specific data.	ITF Sunter project		[70]	2021
Google OR-Tools; GLS, SA, TS metaheuristics	Minimize collection costs; fast algorithm execution.	Extremely fast computation (<2s); cost-effective.	Static approach limits real-world dynamic adaptability.	Simplifies time-related constraints.	Real dataset from Bragança, Portugal	Bragança, Portugal	[71]	2023
Mixed Integer Programming; Heuristic solutions	Minimize collection and transportation route lengths.	Achieves over 30% reduction in route length.	Limited to path optimization; excludes operational factors.	Focus on path reduction only.	Simulation and real testbed results	None	[72]	2015
Artificial Neural Networks (ANNs)	Predict energy and environmental impacts of incineration and landfill processes	High prediction accuracy; provides insights into optimizing energy recovery from incineration	Transportation-related emissions dominate, requiring improved routing logistics	Limited consideration of seasonal and geographical variations	Waste Management Organization	Tehran, Iran	[73]	2017
Genetic Algorithm	Optimize vehicle routing for waste collection	Cost reduction and higher efficiency achieved through advanced routing optimization	Deterministic parameters limit adaptability to real-life uncertainties	Limited inclusion of dynamic real-world constraints	Construction waste data	Sydney, Australia	[74]	2021
Binary Bat Algorithm	Optimize waste collection routing considering cost, reliability, and environmental impact	Enhanced cost efficiency and environmental awareness	Higher costs associated with differentiated waste collection	Limited testing with real-life datasets	Simulated data	Not specified	[75]	2019

,

Table A2. Recent literature in waste collection and EV integration.

Methodology	Objective	Advantages	Disadvantages/Gaps	Limitations	Dataset	Case Study	No.	Year
Hybrid GA	Optimize recyclable waste routing using electric vehicles	Reduces carbon emissions and enhances resource efficiency	Limited focus on EV charging logistics	Charging-related issues and real-time traffic conditions not included	Simulated data	Not specified	[7]	2021
HTA; realistic energy consumption functions	Optimize waste collection routes using plug-in hybrid electric refuse vehicles	Realistic fuel and energy consumption modeling; Outperforms state-of-the-art EVRP algorithms	Requires robust refueling/recharging infrastructure; high computational demands	Limited scalability to other vehicle types or logistics scenarios	Simulation-based data	Urban waste collection with hybrid vehicles	[29]	2022
Mixed-Integer Programming (MIP); AVNS	Reduce collection costs and emissions using heterogeneous electric vehicles	Supports multi-compartment, time windows, and split deliveries; significant emission reductions	Requires extensive computational power for large datasets	Limited application beyond urban settings	Real-life data from urban regions	Case study in a metropolitan city	[30]	2022
MOEA/D-ALNS; Multi-objective mixed-integer linear programming	Minimize energy consumption and infection risk in medical waste transportation	Efficient multi-objective optimization; adaptive recharge policies	Complex model setup; limited real-world validation	Assumes fixed infection risk parameters	Simulation and benchmark datasets	Real-world scenarios for medical waste	[31]	2023
Mathematical model; Hybrid ACO + VNS	Optimize multi-compartment EV routing for classified waste collection	Cost-effective routing; supports multiple trips and compartmentalized vehicles	Computationally intensive for large-scale problems	Limited scalability to other fleet types	Structured instances; simulation-based data	Real-life application in classified waste collection	[32]	2024
HOTDMDEVRPBRS; VNS	Optimize EV routes for MSWM considering energy constraints	Efficient routing; significant reductions in computation time	Assumes fixed parameters for energy consumption	Limited validation in non-urban contexts	Data from New York recycling system	Urban municipal solid waste management	[33]	2024
MIP; ALNS-based matheuristic	Minimize costs while accounting for waiting times at recharging stations	Incorporates time-dependent queuing; efficient routing	High model complexity; computationally expensive	Limited scalability to large logistics networks	Benchmark datasets	Urban logistics networks	[34]	2019
Simulation-based methodology	Compare logistics configurations for dynamic WEEE collection	Combines economic and environmental KPIs to evaluate sustainability	Limited scalability of results	Focuses only on two configurations; limited exploration of hybrid solutions	Local WEEE data	Italy	[35]	2019
IoT data; DNN; Blockchain for security	Optimize EV routing with IoT, traffic systems, and secure data transmission	Enhanced security; real-time routing with IoT integration	High reliance on advanced infrastructure	Limited scalability in non-urban contexts	IoT-enabled datasets	Urban smart city networks	[36]	2021
MIP; HALNS + Ant Colony Optimization	Optimize routes with hybrid energy replenishment strategies	Effective replenishment strategies; high-quality solutions	Requires detailed traffic and energy data	Limited validation in diverse urban networks	Simulation and real-world datasets	Urban distribution for 3PL fleets	[37]	2023
Smart bin data integration; dynamic routing algorithms	Optimize dynamic waste collection with multi-compartment EVs	Real-time adaptability; smart bin integration enhances efficiency	High reliance on IoT and smart infrastructure	Limited scalability to non-IoT regions	IoT-enabled bin data	Urban waste management	[59]	2022
Evolutionary Decomposition-based ALNS (E-ALNS/D)	Optimize disposal locations and routes to reduce infection risk and energy use	Balances infection risk, energy, and cost; case study validation	Requires extensive data collection for practical implementation	Assumes stable infrastructure	Urban healthcare waste datasets	Urban settings with healthcare waste logistics	[60]	2024
Ant Colony (AC) metaheuristics; energy-efficient routing model	Minimize energy consumption of EVs with recharging constraints	Effective for large instances; reduces energy use significantly	Limited validation in real-world logistics networks	Relies on static assumptions for energy use	Benchmark datasets	Simulated logistics scenarios	[62]	2018
Location-routing model; time-window constraints; resource sharing strategies	Optimize charging station locations and routes with time windows	Enhances resource sharing and operational efficiency	High model complexity; computational overhead	Requires robust logistics infrastructure	Real-world urban data	Case study in Chongqing, China	[61]	2022
MIP; optimal route determination	Determine optimal EV routes minimizing energy and travel time	Optimized routes for logistics companies; energy-efficient	Computationally expensive for large-scale problems	Assumes static energy parameters	Structured datasets; benchmark instances	Generic logistics scenarios	[76]	2021
MIP model; Improved Ant Colony Algorithm (IACA)	Minimize logistics costs and emissions with charging station variability	Significant emission and cost reductions; adaptive routing	Requires extensive data on station energy differences	Limited validation in dynamic scenarios	Generated instances	Logistics companies’ real-world scenarios	[77]	2023
Bi-objective model; Gaussian Clustering + IMOGA-TS	Optimize routes with collaborative depots and shared charging	Improves cost efficiency; supports multi-depot collaboration	High computational complexity; limited real-world data	Requires extensive infrastructure for collaboration	Urban logistics datasets	Case study in Chongqing, China	[78]	2023
MIP; real-road network simulation	Optimize EV routes with intermediate nodes and dynamic demand	Realistic modeling of urban road networks; cost-efficient routing	High computational load for large-scale networks	Limited scalability to rural areas	Real-life road network data	University shuttle services	[79]	2022
Four MIP formulations; charging strategies	Minimize costs while avoiding battery degradation	Protects battery health; efficient route planning	Assumes stable recharging infrastructure	Limited to small and medium-sized datasets	Structured instances	Simulated EV logistics networks	[80]	2022
MILP; Modified Clarke-Wright Heuristic + VNS	Optimize two-echelon EV distribution with time windows	Supports multi-echelon logistics; cost-efficient	Limited validation in large-scale urban settings	Assumes static time window parameters	Benchmark datasets	Urban last-mile delivery networks	[81]	2022
SSH-VNS algorithm; Bin Packing Problem (BPP)	Optimize multi-depot EV routing and capacity allocation	Efficient bin packing; reduces carbon emissions	High computational demands for large datasets	Limited generalizability beyond urban settings	Benchmark datasets	Practical distribution case study	[82]	2020
Time-dependent model; Extended ALNS with two-dimensional coding	Optimize EV routing with prioritized time windows and hybrid recharging	Effective prioritization; supports hybrid recharging strategies	High model complexity; computational overhead	Requires precise data for hybrid recharging	Benchmark datasets	Urban logistics distribution	[83]	2024
Hidden Markov Model; Modified Genetic Algorithm; Agent-based architecture	Integrate grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services in EV routing	Supports grid services; adaptive routing	High computational complexity; requires robust infrastructure	Limited real-world validation	Generated datasets	Generic urban logistics scenarios	[84]	2017
Bi-objective programming; Weighted-sum and ε-constraint methods; ALNS	Minimize transportation costs and GHG emissions	Significant GHG reductions; supports mixed fleets	High computational overhead; requires detailed fleet data	Limited scalability to smaller fleets	Data from Ontario, Canada	Case study in Greater Toronto Area	[85]	2023
Estimation of Distribution Algorithm (EDA-LF); Lévy flight for local search	Minimize costs in multi-compartment EV routing with recharging constraints	Robust solutions; cost-efficient for medium-large datasets	High computational complexity; limited validation in real-world cases	Simulation datasets	Generic logistics networks		[86]	2021
Bayesian Network (BN) model; Sensitivity and propagation analysis	Identify optimal EV charging station locations considering sustainability criteria	Incorporates qualitative and quantitative factors; flexible decision-making	Requires expert judgment for setup; high model complexity	Limited real-world applications	Generated data and expert input	Urban EV charging station planning	[87]	2019
Hybrid SA + Variable Neighborhood Search (VNS)	Optimize energy use and minimize the number of EVs in routing	Effective for last-mile logistics; energy-efficient routing	Limited validation in diverse network configurations	Assumes static vehicle and infrastructure data	Benchmark datasets	Last-mile delivery networks	[88]	2023

and

Table A3. Recent literature in waste collection and smart technologies.

Methodology	Objective	Advantages	Disadvantages/Gaps	Limitations	Dataset	Case Study	No.	Year
Data Analysis	Explore circular economy strategies for urban climate neutrality	Holistic approach combining waste management and circularity	Financial constraints limit practical implementation of proposed strategies	Results focus on ambitious cities, limiting generalization to less proactive regions	Horizon Europe mission data	362 European cities	[5]	2023
Narrative literature review; Case study	Explore the impact of circular economy business models on achieving SDGs	Highlights significant SDG contributions through circular economy	Limited quantitative analysis of SDG contributions	Case study approach limits broader applicability	Company-specific data	Contarina SpA, Italy	[6]	2022
IoT	Leverage IoT for waste management optimization.	Comprehensive waste categorization methods.	Limited focus on hazardous waste solutions.	Lack of advanced automation systems.	No dataset specified	Not explicitly mentioned	[38]	2017
IoT sensors, spatio-temporal optimization, simulation	Enable smart waste collection for greener cities.	Real-time waste level monitoring and route optimization.	Focused on waste bins; excludes broader IoT integrations.	Future work limited to urban prototypes.	Pune, India (Open Data)	Pune, India	[39]	2017
Graph theory, ML (LoRa data transfer)	Optimize waste collection using IoT and ML.	Low-cost and energy-efficient system implementation.	Limited scalability beyond university campuses.	Data collection focused on small-scale.	Ton Duc Thang University, Vietnam	Ton Duc Thang University	[40]	2020
Decision Support System (DSS)	Enhance waste collection through IoT and ITS.	Real-time monitoring and dynamic routing features.	Restricted to accessible areas.	Limited integration with advanced ITS.	Urban datasets	Unspecified urban context	[41]	2015
Ultrasonic-level, gas sensors	Automate waste monitoring via cloud and IoT.	Effective hazardous gas detection; app integration.	Expensive cloud dependency.	Focused on urban waste only.	Municipal datasets, urban context	Urban municipal areas	[42]	2018
CNN, IoT sensors	Classify and monitor waste using IoT and deep learning.	High classification accuracy; scalable model.	Works with limited waste types.	Needs larger datasets for validation.	Dataset for waste images	Generalized study	[43]	2022
Dynamic scheduling, IoT sensors	Improve collection efficiency using IoT architecture.	Enhanced monitoring of bin surroundings.	Lack of advanced automation features.	Limited testing in urban setups.	High-density residential data	No case study specified	[44]	2018
Smart sensors, NIR spectroscopy	Prevent overflowing bins and manage waste segregation.	Automated waste segregation with biogas generation.	High dependency on NIR technology.	Limited scope for urban deployment.	None specified	Not explicitly mentioned	[45]	2015
IoT sensors, GIS optimization	Optimize waste collection using IoT and GIS.	Real-time monitoring and optimized routing.	Increased travel distance in simulations.	Economic feasibility analysis pending.	Open Data, Copenhagen	Copenhagen, Denmark	[46]	2015
IoT middleware, smart bins	Improve waste collection with citizen engagement.	Citizen access via apps enhances transparency.	Cost of large-scale deployment.	Prototypical setup	Simulated urban settings		[48]	2020
LoRaWAN, route optimization	Implement low-power IoT nodes for rural waste collection.	Significant cost and energy savings.	Limited urban application validation.	Case study-specific data	Salamanca, Spain		[49]	2018
LoRa, energy-efficient nodes	Design energy-efficient IoT sensor nodes for waste monitoring.	Extended operational life reduces maintenance.	No real-world long-term tests conducted.	Prototype-based testing	Simulated urban settings		[50]	2018
CNN, IoT sensors	Improve municipal waste classification and monitoring.	High classification accuracy with MobileNetV3.	Limited to specific waste categories.	Experimental datasets	Generalized urban context		[51]	2021
K-Nearest Neighbors (KNN), IoT sensors	Optimize household waste management with IoT and ML.	Improved segregation at multiple levels.	Focus on household-specific implementations.	Small-scale datasets utilized.	Simulated urban setups	Simulated society	[52]	2020
LoRa, TensorFlow	Replace traditional waste systems with IoT and AI.	Multi-compartment smart bins with efficient segregation.	Model accuracy depends on training data.	Limited testing environments.	Experimental data	Generalized study	[53]	2020
GIS-based spatial analysis	Quantify and manage demolition waste using GIS.	Optimized recycling and landfill use.	Requires extensive GIS data for replication.	Demolition waste-specific data	Shenzhen, China		[54]	2016
GIS-based analysis	Identify suitable landfill locations using GIS.	Effective identification of legal landfill areas.	GIS reliance limits scalability.	Municipal waste data, Goiás	Goiás, Brazil		[55]	2018
GIS-based network analysis	Optimize waste transport and assess vegetation loss.	Significant reduction in travel distance for waste collection.	Limited to specific vegetation impacts.	Urban datasets	Vellore, India		[56]	2017
Location allocation, CVRP modeling	Improve waste collection efficiency through GIS optimization.	Efficient allocation of bins and reduced travel.	Focused only on urban contexts.	Urban datasets, Mashhad	Mashhad, Iran		[57]	2017
ANN predictions, GIS route optimization	Optimize collection routes based on waste characteristics.	Dual-compartment trucks save travel distances.	Tradeoff between emissions and travel time.	Waste data, Austin, Texas	Austin, Texas		[58]	2019
Serverless architecture, edge computing	Develop IoT-enabled monitoring for waste violations.	Effective violation tracking and data management.	Heavy reliance on edge-computing devices.	Limited real-world scenarios tested.	Azure IoT Hub data	Not explicitly stated	[89]	2018
Cloud-based IoT integration	Develop a cloud-integrated waste monitoring solution.	Route optimization improves fuel efficiency.	Over-reliance on cloud infrastructure.	Limited urban datasets	Smart city context		[90]	2016
Comparative analysis	Analyze sensor applications in IoT-based smart environments.	Comprehensive categorization of IoT sensor use cases.	Broad theoretical focus, limited specifics.	General IoT applications	None specified		[91]	2019
Ultrasonic sensors, Global System for Mobile (GSM) communications	Monitor bin levels and alert municipalities using IoT.	Cost-effective implementation for flats.	Focused only on flat residential areas.	Simulated conditions	Flat residential areas		[92]	2017
Capacitance sensors, ultrasonic sensors	Design IoT-based smart bins for real-time monitoring.	Real-time monitoring integrated with cloud systems.	Limited focus on robotic mobility features.	Simulated experimental data	Smart city environments		[93]	2019

that show recent research in waste management and

Table A4. Detailed classification of most focused KPIs in the waste management literature.

Reference	GHG Emissions Reduction	Fuel Consumption Optimization	Operational Scalability	Economic Feasibility	Technological Integration	Dynamic Adaptability
[1]	✔		✔	✔		✔
[2]	✔	✔	✔	✔	✔	✔
[3]			✔	✔
[4]			✔	✔
[5]	✔	✔	✔	✔	✔	✔
[6]	✔	✔	✔	✔	✔	✔
[7]	✔	✔	✔	✔	✔	✔
[8]			✔	✔
[9]	✔	✔	✔	✔	✔	✔
[10]	✔	✔	✔	✔
[11]	✔	✔	✔	✔	✔	✔
[12]		✔	✔	✔	✔
[13]	✔		✔	✔
[14]	✔	✔	✔		✔
[15]			✔	✔
[16]	✔		✔	✔		✔
[17]	✔	✔	✔	✔	✔
[18]		✔	✔	✔	✔
[19]	✔	✔	✔	✔	✔	✔
[20]	✔		✔	✔
[21]		✔	✔	✔
[22]	✔	✔	✔	✔	✔
[23]		✔	✔	✔
[24]	✔	✔	✔	✔	✔	✔
[25]		✔	✔	✔	✔
[26]	✔	✔	✔	✔	✔
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Note: ✔ indicates that the referenced study explicitly addresses or contributes to the corresponding criterion.

that categorizes Key Performance Indicators (KPIs) discussed in the literature.

2. Framework and Methodology

2.1. Overview of the Research Approach

This paper employs a systematic literature review methodology to analyze advancements in waste management, focusing on three core themes: optimization in waste logistics, EV integration, and the adoption of smart technologies.

The methodology involved defining the scope, conducting searches in academic databases, and applying inclusion and exclusion criteria. Selected studies were categorized under key themes, and their findings were analyzed using both quantitative and qualitative techniques. The subsequent sections elaborate on the selection process, data extraction, and analytical framework, providing an account of the steps taken to synthesize insights from the literature. An overview of the research methodology and approach in this review work is provided in Figure 2.

2.2. Selection of Sources

Relevant academic publications were identified through extensive searches of electronic databases, including Scopus, Sage Journals, and Google Scholar. The primary keywords used in the search included “waste management”, “vehicle routing optimization”, “electric vehicles in waste logistics”, “smart technologies in waste collection”, “IoT in waste management”, and “artificial intelligence in waste optimization”. Boolean operators (AND, OR) were applied to refine search queries, ensuring the inclusion of studies from diverse subfields. For example, a typical query applied in Scopus and Google Scholar was: (“waste management” OR “waste collection”) AND (“optimization” OR “vehicle routing problem”) AND (“electric vehicles” OR “fleet electrification”). These structured queries ensured that only relevant publications aligning with the research themes were included.

This review examines studies published in peer-reviewed journals, conference proceedings, and reputable industry reports from 2015 to 2024. This period was selected to capture the most recent advancements in waste management technologies, optimization strategies, and EV integration, reflecting the rapid digitalization and increasing emphasis on sustainability-driven policies in municipal waste management. The selected timeframe ensures a balance between historical developments and cutting-edge research, providing a comprehensive perspective on emerging trends and technological innovations. The inclusion criteria were as follows:

• Articles addressing waste management optimization, EV integration, or smart technologies.
• Studies incorporating environmental and economic impact assessments.
• Papers presenting experimental, simulation-based, or case-study methodologies.
• Exclusion criteria included the following:
• Studies unrelated to waste collection or treatment processes.
• Duplicates or non-English publications.

An initial pool of 500 articles was identified. Titles and abstracts were screened for relevance, reducing the pool to 200 articles. The full-text review further narrowed the selection to 100 studies, which formed the core dataset for this review. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework is utilized to document the selection process. Figure 3 presents the distribution of articles across various countries. A total of 73 case studies were identified, spanning 23 countries. China emerged as the most frequently studied country, with 10 articles, followed by India with 5 articles and Iran with 4 articles.

2.3. Data Extraction and Analysis

Each selected study was categorized under one or more of the three key themes. A coding framework was developed to identify recurring themes, methodologies, and outcomes across the literature:

• Theme 1: Optimization techniques included subcategories like VRPs, dynamic routing models, and AI-based optimizations.
• Theme 2: EV integration covered studies on environmental impact assessments, life-cycle analyses, and cost–benefit evaluations.
• Theme 3: Smart technologies encompassed IoT-enabled systems, real-time monitoring, and AI-driven analytics.

A mixed-method approach was employed for data analysis. Quantitative studies were analyzed for metrics such as carbon emissions, cost savings, and operational efficiency. Qualitative studies provided insights into challenges, barriers, and policy recommendations. Comparative tables and charts were developed to synthesize findings across studies. To ensure the validity and reliability of findings, cross-validation of data was conducted by comparing results across multiple studies. Studies with contradictory findings were further scrutinized for methodological differences and contextual factors. Additionally, the quality of studies was assessed using the Mixed Methods Appraisal Tool (MMAT), ensuring the inclusion of high-quality research.

2.4. Bias Mitigation and Review Validation

To ensure the robustness, transparency, and reliability of the review process, several bias mitigation strategies were employed throughout the study. First, a multi-reviewer approach was adopted during the article selection, thematic categorization, and coding phases. Two independent reviewers initially screened titles and abstracts, with discrepancies resolved through discussion or third-party adjudication to reduce individual bias and subjective interpretation. Second, a pilot round of full-text review was conducted on a subset of papers to harmonize understanding of inclusion criteria and thematic classification, resulting in a refined coding framework aligned with the three core themes of optimization, EV integration, and smart technologies. Third, during data extraction and synthesis, cross-validation techniques were used to compare findings across multiple studies, ensuring consistency and identifying potential outliers. Additionally, inter-reviewer consistency was monitored throughout using iterative feedback sessions, ensuring methodological coherence and minimizing misclassification. These steps were guided by principles from the PRISMA framework and aligned with best practices for systematic reviews, thereby reinforcing the validity and reproducibility of the study’s insights.

3. Thematic Literature Review

3.1. Optimization Techniques in Waste Collection and Transportation

The optimization of waste collection and transportation is a crucial element in achieving efficient and sustainable municipal solid waste management (MSWM). VRP, Capacitated VRP (CVRP), and other metaheuristic algorithms have been widely employed to optimize fleet operations, reduce operational costs, and minimize environmental impacts. This section highlights recent developments and findings from studies that specifically address optimization techniques for waste collection fleets.

VRP has been extensively studied to address the challenge of minimizing travel distances, fuel consumption, and greenhouse gas (GHG) emissions in waste collection. A Coordinated Solid Waste Management (CSWM) framework was proposed in [12], incorporating financial, environmental, and social objectives to achieve sustainable solutions for heterogeneous vehicle fleets operating across multi-echelon logistics networks. Their novel Adaptive Memory Social Engineering Optimizer (AMSEO) outperformed other metaheuristics like Simulated Annealing (SA), providing practical and efficient routing strategies. Similarly, a dynamic approach was introduced to address the multi-compartment VRP, integrating IoT-enabled Threshold Waste Level (TWL) monitoring with hybrid Genetic and Particle Swarm Optimization algorithms. This method reduced total costs up to 42% compared to static routing systems, demonstrating significant operational benefits [13].

The optimization of waste collection processes using pure electric vehicles is gaining significant attention, as fleet electrification has been shown to enhance both operational efficiency and environmental sustainability in waste collection [14]. A smart waste management system incorporating stochastic optimization was proposed in [15] to deal with uncertainties in waste generation and separation rates. They optimized transportation costs and maximized recycled revenue by employing chance-constrained programming and multiple metaheuristic algorithms. On the other hand, another work addressed a real-world roll-on–roll-off VRP for bulky recyclable waste, focusing on minimizing fleet size and route durations while overcoming logistical constraints such as container availability [16].

CVRP models, which incorporate vehicle capacity constraints, have been widely applied to optimize waste collection routes. For instance, in [17], a Priority-Based Green Vehicle Routing Problem (PCGVRP) was introduced, integrating GHG emission costs and conventional operational expenses. Using a Hybrid Local Search Algorithm (LSHA), their model achieved a 42.3% reduction in negative environmental effects, with optimized waste fill levels ranging between 60% and 80%. Moreover, a modified Backtracking Search Algorithm (BSA) was introduced to a CVRP model, which optimized travel distances while maintaining waste bin fill levels at an ideal threshold. Their results indicated a 36.8% reduction in collection route distances and a 50% decrease in fuel consumption [18].

Dynamic approaches to CVRP have also been explored to accommodate real-time changes in waste generation. Study [19] proposed a two-echelon VRP model, integrating IoT devices to monitor bin levels and optimize collection routes. By combining heuristic and metaheuristic algorithms, their study minimized operational costs and environmental impacts. Similarly, Ref. [20] developed a multi-trip VRP with time windows to prioritize critical collection points, such as hospitals. Their model, validated through a real-world case study, achieved a 13.3% reduction in total costs.

Recent studies have increasingly adopted metaheuristic and hybrid algorithms to solve VRP and CVRP models efficiently. One study proposed a modified Particle Swarm Optimization (PSO) algorithm to optimize scheduled waste collection routes. The results showed optimal performance at a waste threshold level of 70–75%, significantly improving collection efficiency and reducing fuel costs [21]. Similarly, another work applied a hybrid Genetic Algorithm (GA) to solve the dynamic multi-compartment VRP, integrating real-time bin-level monitoring in smart cities [22].

Hybrid algorithms such as Particle Swarm Optimization combined with Tabu Search (PSO-TS) have been successfully implemented for sustainable MSWM. Researchers demonstrated the potential of PSO-TS to minimize economic, environmental, and social costs associated with capacitated vehicle routing for municipal solid waste [23]. Another study utilized the SA algorithm to optimize CVRP for waste collection systems, achieving significant reductions in travel distances and costs [24].

Geographic Information System (GIS)-based approaches have emerged as effective tools for route optimization. One analysis demonstrated the use of GIS-based network analysis to optimize waste collection routes in Khulna, Bangladesh, achieving a 9.4% reduction in travel distances and significant fuel cost savings [25]. Similarly, another proposed a GIS-integrated smart collection system for dynamic waste routing. The simulation results revealed cost reductions of up to 19% and carbon emission reductions ranging between 5% and 22% [26].

A further study proposed an optimal route recommendation system that integrated prediction algorithms with optimization techniques to prioritize waste collection routes based on bin overflow potential. The system, applied to Jeju Island in South Korea, significantly enhanced operational efficiency [27]. Additionally, researchers developed a spatial GIS-based Genetic Algorithm (SGA) to optimize solid waste collection routes, achieving substantial improvements over traditional methods [28]. Further details of the literature on waste collection techniques are provided in Table A1.

3.2. EV Integration in Waste Collection and Transportation

EV integration into waste management systems represents a transformative step toward modernizing logistics while delivering both environmental and economic benefits. The primary impact of EV adoption lies in reducing greenhouse gas (GHG) emissions and improving urban air quality. However, EVs can also contribute to cost reduction, though the most significant savings are achieved when EV integration is coupled with optimization strategies such as improved routing and scheduling. While both EV adoption and optimization processes independently contribute to cost efficiency, their combined implementation maximizes operational performance, energy efficiency, and overall financial savings. The following subsections explore various studies that examine the role of EVs in waste collection, routing optimization, and technological advancements in Electric Vehicle Routing Problems (EVRPs).

The use of plug-in hybrid electric refuse vehicles in waste collection has been explored as a viable solution to combine the benefits of multiple energy sources. In this regard, a previous study introduced the Hybrid Waste Collection Problem (HWCP), where vehicles are powered by both electricity and Compressed Natural Gas (CNG). These vehicles operate with realistic fuel consumption models that consider road gradients and paths between nodes to estimate energy use accurately. The authors develop a Hybrid Threshold Acceptance (HTA) algorithm to optimize routing and recharging decisions. Computational results demonstrate the effectiveness of hybrid vehicles in reducing operational costs and travel distances when compared to conventional single-energy vehicles [29].

The optimization of waste collection processes using pure electric vehicles is gaining significant attention. Study [30] investigates the Electric Waste Collection Problem (EWCP), where heterogeneous fleets of EVs are tasked with collecting waste from geographically dispersed bins. This study introduces multiple complexities, including time windows, multi-compartment vehicles, split deliveries, and waste–bin compatibility constraints. An Adaptive Variable Neighborhood Search (AVNS) algorithm is proposed to optimize the routes, minimizing total travel costs and emissions. Results indicate that using an EV fleet in waste collection reduces both costs and emissions, with an average 1.18% improvement in the objective function value when applying the AVNS heuristic algorithm. Additionally, sensitivity analyses show that optimizing waste bin fill levels can improve route efficiency by 25 km on average, reinforcing that cost savings stem primarily from optimized scheduling and routing rather than EV adoption alone, while EVs contribute significantly through harmful exhaust emission reductions.

In medical waste management, one study proposed an adaptive multi-objective algorithm for managing the transportation of hazardous waste using EVs. A multi-objective model balances energy consumption, load-dependent discharge, and infection risk while considering time windows and partial recharge policies. Their algorithm, MOEA/D-ALNS (Multi-Objective Evolutionary Algorithm based on Decomposition with Adaptive Large Neighborhood Search), outperforms benchmark methods, demonstrating that EVs can efficiently manage complex waste transportation tasks [31].

The heterogeneous nature of waste often necessitates specialized routing solutions. Researchers explored the Heterogeneous Electric Vehicle Routing Problem with Multiple Compartments and Multiple Trips (HEVRP-MCMT) for classified waste collection. Here, vehicles have multiple compartments for different waste types and are permitted to recharge during their route. A hybrid ACO algorithm combined with VNS was proposed to optimize the fleet’s operations. Experiments on both simulated and real-world instances highlight the significant cost savings achieved by employing multi-compartment vehicles [32].

Similarly, another study addresses the Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem (HOTDMDEVRPBRS), where solid waste is collected using EVs from multiple depots. This study incorporates battery recharging and swapping stations and utilizes a VNS algorithm to optimize routing under time-dependent travel conditions. The approach shows substantial reductions in solution times and operational costs compared to traditional methods [33].

The development of charging strategies for EVs in waste management is critical to ensuring operational feasibility. For example, one work considers time-dependent waiting times at recharging stations in EV routing. The study shows that queuing delays at charging stations significantly impact routing decisions and operational efficiency. By incorporating adaptive charging schedules, EV routes can be optimized to reduce overall travel time and energy costs [34]. The circular economy promotes resource recovery and environmental sustainability. Another study proposed a two-echelon reverse logistics network using electric vehicles, reducing transportation costs and carbon emissions while improving the collection of recyclables [7]. Similarly, dynamic collection services for Waste Electrical and Electronic Equipment (WEEE) have been shown to benefit from a hub-and-spoke configuration integrated with electric vehicles, proving to be both economically and environmentally superior [35].

Further studies extend this discussion by integrating Intelligent Transportation Systems (ITSs) and Deep Neural Networks (DNNs) into the routing of Garbage Disposal Electric Vehicles (GDEVs). IoT devices are employed to monitor garbage payloads and climatic conditions, enabling dynamic traffic management and energy-efficient route optimization. The findings highlight how real-time data and advanced algorithms can enhance EV-based waste management systems in smart cities [36].

Hybrid energy replenishment strategies are also becoming a focus in the EVRP literature. One analysis addresses the Time-Dependent Open EV Routing Problem with Hybrid Energy Replenishment Strategies (TDOEVRP-HERS). The study considers both battery charging and swapping to optimize waste collection routes, leveraging a Hybrid Adaptive Large Neighborhood Search (HALNS) algorithm. Results indicate significant cost savings and enhanced fleet energy efficiency when hybrid replenishment strategies are applied [37]. Further details of the literature in waste collection EV integration are provided in Table A2.

3.3. Smart Technologies in Waste Management

Smart technologies have transformed the landscape of waste management systems, providing innovative and efficient solutions to address the growing challenges in urban environments. The convergence of IoT, smart sensors, machine learning, and GIS has made waste management more dynamic, data-driven, and optimized. This section presents a detailed overview of smart technologies applied to waste management systems.

IoT has emerged as a game-changing technology for waste management by enabling real-time data collection, monitoring, and optimization. It integrates sensors, communication protocols, and cloud platforms to automate and streamline waste collection processes.

One study introduced a smart waste management system using IoT, highlighting strategies such as reduction, recycling, composting, and land application. IoT enables monitoring of waste levels in bins, identification of hazardous waste, and prevention of environmental contamination [38]. Similarly, another work developed an IoT prototype for smart cities, integrating sensors to measure waste levels in bins and optimize collection routes, leading to improved operational efficiency [39].

Researchers proposed a machine learning-based IoT system to optimize waste collection using graph theory and Low-Range (LoRa) communication. This system demonstrated high efficiency in predicting waste bin levels and minimizing collection routes at a university campus [40]. Further studies extended IoT applications to ITS for dynamic routing of waste collection trucks, ensuring Quality of Service (QoS) in inaccessible areas [41].

An innovative cloud-monitored IoT waste management system was presented, incorporating gas sensors and ultrasonic-level sensors to detect waste levels and hazardous gases. The system sends real-time alerts to municipal authorities via cloud-based servers for immediate action, reducing overflow and improving efficiency [42].

IoT systems also integrate advanced analytics. One system combined IoT and deep learning for waste classification and monitoring, achieving 95.3% classification accuracy using convolutional neural networks (CNNs) [43]. Another architecture for waste volume monitoring and dynamic routing demonstrated reductions in the carbon footprint associated with waste collection [44].

The integration of smart sensors has significantly improved waste collection systems by enabling real-time monitoring and reducing inefficiencies. One study designed a smart garbage bin with sensors that trigger alerts when the bin is full, automating biogas production from biodegradable waste and contributing to renewable energy generation [45]. Another demonstrated the benefits of smart waste collection systems with location intelligence, where IoT-enabled sensors transmit waste-level data processed using graph theory to optimize collection routes. The system improved efficiency by reducing overflow incidents and enhancing resource allocation [46]. Smart waste collection systems have emerged as effective solutions for sustainable urban waste management. An advanced routing optimization model integrating differentiated waste collection showed improvements in cost efficiency and environmental outcomes [8]. Other research emphasized green technologies and circular economy frameworks to address challenges like plastic and electronic waste, advocating for sustainable design and responsible recycling [47].

IoT-based waste management solutions have also adopted citizen-centric approaches. For example, one system incorporated smart bins to monitor waste levels and share data with citizens via mobile applications, ensuring transparency and improving accuracy in waste collection [48]. Another implemented a Long-Range Wide Area Network (LoRaWAN)-enabled waste collection system with route optimization, achieving significant reductions in fuel consumption, operational costs, and emissions [49]. A low-power IoT sensor node architecture for smart cities focused on energy-efficient data transmission using LoRa technology, demonstrating extended battery life and reduced operational costs [50]. Further advancements integrated deep learning with IoT to classify waste into multiple categories with high accuracy while also optimizing collection routes [51].

Machine learning (ML) and AI have revolutionized waste management through accurate waste classification, predictive analysis, and optimization. One system used IoT and ML to segregate household waste into biodegradable and non-biodegradable categories, achieving high classification accuracy and promoting compost production [52]. Another applied ML to forecast waste generation trends and optimize resource allocation, resulting in increased operational efficiency [3]. Research on reducing the carbon footprint of waste collection vehicles highlighted the impact of switching from diesel to CNG and biogas, achieving substantial emissions reductions [4].

Innovative approaches also combined LoRa communication with real-time object detection for smart bins, segregating waste into categories like metal, plastic, and paper, thereby optimizing collection and reducing costs [53]. Another system utilized deep learning and IoT with MobileNetV3 CNN, demonstrating high accuracy in waste classification and cost-effective waste management [51].

GIS-based technologies have proven effective in optimizing waste collection routes, locating facilities, and analyzing spatial data. For instance, one study applied a GIS-based model to quantify demolition waste flows, identifying recycling potential and reducing landfill demand [54]. Another used GIS to identify suitable landfill sites, incorporating environmental and public health considerations [55].

GIS combined with network analysis has been used for solid waste collection optimization, achieving substantial reductions in travel distances and costs [56]. Integrated GIS models have also optimized bin locations and collection routes, significantly reducing crew requirements and travel distances [57]. In another example, an Artificial Neural Network (ANN) was combined with GIS to forecast waste generation and optimize truck collection routes, highlighting the relationship between waste composition and travel distances for effective route planning [58].

The integration of IoT, smart sensors, machine learning, and GIS offers multiple advantages:

• Efficiency: Optimized waste collection routes reduce travel distance, fuel consumption, and cost [39,46,56].
• Real-Time Monitoring: IoT and smart sensors provide instant updates on waste levels, enabling timely intervention [42,48].
• Environmental Benefits: Reduced emissions and energy consumption through route optimization and biogas production [41,45,49].
• Citizen Participation: IoT platforms and mobile applications involve citizens in waste management, improving transparency and accountability [48].
• Predictive Analysis: Machine learning models forecast waste levels and optimize resource allocation [40,51,53].
• Spatial Optimization: GIS-based tools identify ideal landfill locations and optimize facility placement [54,55,57].

Figure 4 represents the distribution of utilized techniques, and further details of the literature on waste collection techniques are provided in Table A3.

4. Discussion

In this section, we examine the findings from the literature on waste management, analyzing studies that focus on optimization, EV integration, and smart technologies. A key aspect of this evaluation is the examination of KPIs. Therefore, the following part presents a detailed explanation of each KPI, emphasizing its role in shaping waste management strategies:

• Operation/travel cost—Assess the financial impact of waste collection operations, including fuel, maintenance, transportation, and labor costs.
• Waste saving—Evaluate landfill demand and overflow reduction.
• Emission reduction—Measure the effectiveness of carbon reduction strategies, including vehicle electrification.
• Waste collection/bin efficiency—Examine the efficiency of waste and bin collection.
• Travel distance—Analyze the total distance covered by waste collection vehicles, including factors affecting route optimization.
• Fuel consumption/cost saving—Evaluate fuel consumption patterns, including savings achieved through alternative fuels and optimized routing.
• Operation/Travel time—Measure the time taken for waste collection operations, including improvements in workforce efficiency.
• Energy/Electricity consumption—Assess energy consumption in waste collection systems.
• Vehicle utilization—Examine fleet efficiency, including fleet size, and number of vehicles for the operations.
• Charging station—Analyze charging station infrastructure, including the number and utilization of charging stations.
• Quality/Solution improvement—Evaluate advancements in waste collection, including forecast accuracy, risk, and decision-making.
• Classification—Categorize waste collection separation and classification for converting waste to energy.

4.1. Optimization and Routing

The optimization of waste collection and routing plays a fundamental role in improving efficiency, reducing costs, and minimizing environmental impacts. As described in this section, 32 articles have been investigated in the literature on waste collection optimization techniques and routing, which collectively contribute to a detailed understanding of traditional routing methods, advanced optimization models, case studies, and utilized datasets. These studies demonstrate the transformative potential of optimization techniques and smart systems in waste management. In this regard, Figure 5 shows the frequency and classification of utilized methods in optimization.

Despite significant advancements, an important observation is that none of these articles utilize publicly available datasets. This limitation highlights the challenge of data accessibility in waste management research and underscores the need for open data initiatives to advance the field.

In addition, Figure 6 compares the frequency of focus and result range (minimum in red and maximum in blue) for waste collection objectives in the literature. Emission reduction stands out with the highest result range (92%) due to the share of replaced vehicles, with significant frequency highlighting its priority. Travel distance and fuel consumption also show high result ranges with moderate frequencies, emphasizing their importance. Conversely, waste saving and energy consumption have lower frequencies and result ranges, indicating underexplored opportunities in the literature.

4.2. Integration of EVs

Electric vehicles have gained traction in waste management systems due to their potential to mitigate environmental impact and enhance operational efficiency.

Table 1. Comparative analysis on diesel and electric vehicle implementation in the literature.

Parameter	Diesel Vehicles	Electric Vehicles	References
Carbon Emissions (kg CO2/km)	1.2–1.8	0.2–0.4	[29,31,59]
Fuel Cost (USD/km)	0.8–1.2	0.5–0.7	[30,60]
Maintenance Costs (USD/year)	5000–7000	2000–3500	[60,61]
Noise Levels (dB)	75–90	40–60	[31,62]

provides evidence of implementation and benefits.

In this work, 27 recent EV studies were analyzed, including 20 that utilized open access (public) datasets. Figure 7 illustrates the estimated frequency of EV integration methodologies applied in waste management, showcasing their contribution to achieving sustainability goals and predominance of energy optimization techniques, emission reduction strategies, and infrastructure adaptability.

Moreover, Figure 8 compares the frequency of focus and result range (minimum in red and maximum in blue) for various objectives in integrating EVs into waste management. Categories like operation/travel cost and emission reduction show high frequencies (12 and 8 articles, respectively) and notable result ranges (52.77% and 89.69%, respectively), reflecting their central importance. Travel distance and energy/electricity consumption also display moderate frequencies with high result ranges, highlighting their relevance to EV integration. Conversely, waste collection/bin efficiency shows low frequency and minimal result range, indicating gaps in research focus.

4.3. Smart Technologies and IoT Applications

The integration of smart technologies such as IoT, smart sensors, and GIS has revolutionized waste management practices. This review provides a comprehensive overview of how these technologies enhance efficiency, sustainability, and decision-making in waste systems. In this section, findings are synthesized, and comparative insights are offered to provide a comprehensive overview.

The integration of IoT, smart sensors, and GIS technologies revolutionizes waste management by enabling real-time monitoring, dynamic routing, and environmental safeguarding. Together, these tools optimize waste collection processes and resource allocation, achieving significant efficiency and sustainability gains, such as improved diversion rates and reduced emissions. However, adoption faces barriers, including high costs, technical skill requirements, and complex data management challenges, particularly in smaller municipalities with limited resources.

Standardized protocols for IoT deployment are essential to ensure compatibility and scalability. Public–private partnerships can address financial and technological challenges, fostering innovation and broad implementation. Future research should focus on combining IoT with AI to enhance predictive analytics and automation, driving smarter, more adaptable waste management systems.

In the smart technology section, 28 articles have been analyzed including 10 open access datasets. Figure 9 illustrates the relationship between the frequency of research focus and result range (minimum in red and maximum in blue) for smart technologies and IoT applications in waste management. This field shows less focused parameters. Operation/travel cost exhibits the highest frequency (eight articles) with a substantial result range, reflecting its critical importance. Travel distance also shows a moderate frequency and result range. This analysis highlights the need for further exploration of underrepresented areas while maintaining focus on high-impact objectives.

A detailed classification of the most focused KPIs in the waste management literature has been provided in Table A4.

The findings presented in this section illustrate how advancements in optimization, EV integration, and smart technologies can be practically implemented within municipal and private-sector waste management operations. The adoption of GIS-driven route optimization and AI-based fleet management systems can significantly improve efficiency and cost-effectiveness in urban waste collection. Furthermore, the successful deployment of EVs in waste logistics depends on integrating these optimization strategies with real-world infrastructure and policy frameworks. These insights provide a foundation for other decision-makers in municipal waste management agencies and industry leaders to transition toward data-driven, sustainable, and scalable waste management solutions.

5. Gaps and Barriers

The waste management sector has advanced rapidly in recent years, driven by the integration of digital technologies and innovative practices aimed at enhancing efficiency and sustainability and reducing environmental impacts. Key trends include the use of optimization algorithms and GIS to streamline waste collection through optimized routing, while AI and machine learning enable predictive analytics for waste forecasting and real-time decision-making. The adoption of electric vehicles (EVs) has significantly reduced greenhouse gas emissions, though challenges such as battery limitations and inadequate charging infrastructure persist. IoT devices have revolutionized waste monitoring by collecting real-time data on bin fill levels and hazardous materials, supporting dynamic, cloud-based decision-making to enhance efficiency. Additionally, circular economy principles are gaining traction, focusing on waste-to-energy conversion, material recovery, and advanced recycling to minimize landfill dependency and maximize resource utilization. However, implementing these principles at scale is hindered by high costs, regulatory inconsistencies, and technological limitations, requiring strategic investments and policy interventions to ensure their widespread adoption. Together, these advancements are reshaping the trajectory of waste management towards a more sustainable future.

The challenges outlined above present critical considerations for policymakers, urban planners, and industry leaders who aim to develop efficient and sustainable waste management strategies. Addressing these barriers requires coordinated efforts in infrastructure investment, regulatory support, and policy incentives that encourage the adoption of smart waste technologies. The successful deployment of AI, IoT, and EV integration depends on well-defined policy frameworks that facilitate interoperability, public–private partnerships, and financial feasibility. By tackling these gaps, decision-makers can accelerate the transition toward a resilient, data-driven, and environmentally responsible waste management ecosystem.

The review highlights key challenges in waste management, including high implementation costs, infrastructure limitations, and policy gaps. For instance, in Goiás State, Brazil, 93% of municipalities dispose of municipal solid waste inappropriately in unlicensed dumps or landfills, posing significant environmental and health risks. By 2040, the region will require an estimated 59,500 km² for landfill construction, with urgent needs concentrated in metropolitan areas producing up to 6850 t/day of waste [55]. Similarly, in Shenzhen, China, under a worst-case scenario, over 54 million m³ of land would be needed for demolition waste disposal, costing approximately USD 218 billion, while improved recycling scenarios could reduce landfill needs by 80% and increase recycling value by 65% [54]. In another case, construction and demolition waste in Guangzhou could lead to economic losses equivalent to 9.1% of the city’s GDP by 2030 if not properly managed [63]. These examples underscore the financial and infrastructural burden associated with the implementation of circular economy strategies and smart waste technologies. Moreover, while innovations such as dual-compartment waste collection trucks can reduce travel distances by 10–16%, they also increase collection time by up to 19.8% [58], demonstrating trade-offs between cost, efficiency, and sustainability.

These trade-offs highlight the importance of evaluating innovations not solely based on individual performance metrics but through a holistic cost–benefit lens. For instance, while reducing travel distance lowers fuel use and emissions, increased collection time may raise labor costs and operational complexity. Decision-makers must balance short-term operational burdens against long-term environmental and economic gains. This underscores the need for comprehensive life cycle assessments and integrated performance indicators that account for environmental, financial, and social dimensions. Without this systems-level view, even well-intentioned innovations risk becoming impractical at scale, particularly in regions with budgetary or infrastructure constraints. Thus, effective deployment of advanced waste strategies must move beyond technological potential to address operational realities and context-specific priorities. Addressing these challenges will require scalable solutions, interdisciplinary collaboration, and targeted policy interventions. A stronger integration of circular economy principles, waste-to-energy conversion, and material recovery strategies will be essential to enhance sustainability outcomes.

The primary goals of these efforts are to enhance operational efficiency by reducing costs, fuel consumption, and waste through optimized routing and improved segregation. However, many of these advancements—particularly those involving EV adoption, AI-driven waste management, and circular economy models—face high implementation costs, making large-scale deployment challenging, especially in regions with limited financial and infrastructural resources. Additionally, they aim to mitigate environmental impacts by adopting EVs and promoting recycling to lower CO₂ emissions and decrease landfill dependency. Advanced waste segregation and recycling methods focus on maximizing material recovery and supporting circular economy principles.

Future research should focus on developing holistic and scalable waste management systems that integrate advanced technologies, such as AI and IoT, with real-time decision-making frameworks. Emphasis should be placed on designing cost-effective and sustainable solutions applicable to both urban and rural contexts, ensuring adaptability to diverse geographic and economic conditions. The exploration of lifecycle assessments and environmental impact metrics is critical to aligning waste systems with sustainability goals. Furthermore, policy frameworks that standardize practices and encourage public engagement are essential for widespread adoption. Addressing the unique challenges of emerging economies through tailored, decentralized approaches will also be pivotal in advancing global waste management practices.

Finally, this review acknowledges potential limitations:

• The reliance on published literature may exclude innovative practices in gray literature or ongoing projects.
• The focus on three core themes may overlook other emerging areas in waste management.
• Biases in data interpretation were minimized by triangulating findings across studies and involving multiple reviewers in the analysis.

6. Conclusions

This review has explored the key advancements in waste management, emphasizing the role of optimization techniques, EV integration, and smart technologies in transforming collection and transportation systems. The findings highlight the potential of AI, IoT, GIS, and EVs in enhancing operational efficiency, reducing environmental impact, and supporting the transition to more sustainable waste management practices.

A key takeaway is that cost efficiency is primarily driven by optimization strategies, while EV adoption plays a critical role in emissions reduction. Additionally, smart technologies enable real-time monitoring, predictive analytics, and adaptive decision-making, allowing municipalities to optimize resource allocation and improve operational efficiency. Despite these advancements, significant challenges remain, including high implementation costs, infrastructure limitations, and policy gaps. Financial and regulatory barriers continue to hinder the large-scale adoption of circular economy principles, smart waste technologies, and EV-based logistics, particularly in regions with underdeveloped waste management systems. Infrastructure constraints further complicate adoption, requiring substantial investments and policy interventions. Overcoming these challenges demands scalable and cost-effective waste management solutions, interdisciplinary collaboration, and supportive regulatory frameworks. A stronger integration of circular economy principles, waste-to-energy conversion, and material recovery strategies will be essential in achieving long-term sustainability goals.

For effective implementation, tailored and actionable strategies are necessary across stakeholder groups. National governments should introduce fiscal incentives such as tax credits or subsidies for EV fleet adoption and smart bin deployment, while also establishing open-data standards for digital waste monitoring platforms to ensure interoperability and transparency. Local authorities can pilot integrated routing platforms using GIS and AI to optimize collection in high-density areas, supported by public–private partnerships to share infrastructure costs. Businesses and waste management operators should invest in predictive analytics for route planning and participate in government-backed grant schemes to electrify their fleets. Research institutions and policymakers should collaborate to establish cross-sector testbeds that validate scalable models, linking operational efficiency gains to environmental performance benchmarks. Additionally, standardized evaluation frameworks should be developed to assess the cost–benefit trade-offs of emerging technologies under diverse urban and regional conditions.

In conclusion, the transformation of waste management systems requires a combination of technological advancements, regulatory support, and collaborative efforts across industries and governments. By addressing identified challenges and leveraging emerging opportunities, stakeholders can drive a paradigm shift toward more resilient, cost-effective, and environmentally conscious waste management solutions worldwide.