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Solid Waste Management Optimization Techniques: A Systematic Review
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DOI：10.1007/s41742-026-01204-3.png)
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Abstract
En 中文
Increasing population, rapid urbanization, sustained economic growth, exponential rise in industrialization, and an overwhelming number of upcoming megacities have increased the volume and complexity of waste generated worldwide. Global waste generation has reached 2.01 billion tons, and urban management planners are facing major challenges in disposing of this huge quantum of waste in a financially viable and environmentally sustainable manner. All of this has made solid waste management (SWM) more complex, thus necessitating advanced and superior optimization strategies that can reduce cost and enhance the efficiency of any SWM model. This review paper comprehensively analyzes the optimization techniques researchers have used for SWM while evaluating their employment, advantages, and limitations. Research papers from 2010 to 2024 have been reviewed, and optimization models have been categorized into deterministic, probabilistic, hybrid, and Internet of Things (IoT)-based models, with the collection and transportation stages of SWM assessed in detail. Models have been compared considering cost minimized, distance reduced, greenhouse gas emission (GHGE) cut down, and social sustainability. Some advances have been made in optimizing SWM through innovative routing procedures, advanced waste bin placements, and adjusting the timing of collection vehicles. The review highlights major research gaps and proffers viable recommendations for future research in optimization techniques in SWM, which can enable urban management planners to adopt methods that can produce efficient SWM models for upcoming mega cities. These directions will assist managers, researchers, and waste management professionals in producing financially viable and environmentally sustainable SWM models. The review synthesizes 66 high quality studies (2010–2014) using PRISMA 2020 guidelines. A comparative analysis indicates that hybrid and IoT integrated models achieve up to 20-40% cost reduction and 15–30% improvement in operational efficiency compared to conventional deterministic approaches.
Keywords:
Solid waste management
Optimization techniques
Optimization models
Route optimization
Internet of things
Journal
I
IF:
3.5
Papers:
318
Citations:
0
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