Optimal Allocation Model Research Articles

Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.

Research on a cloud model intelligent computing platform for water resource management

ABSTRACT As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references and insights for intelligent water management and refined allocation of water resources in the Weihe River Basin.

How to optimize government R&D budget allocation? A review of recent studies and research gaps

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Pipe sharing: A capacity allocation method for incorporating economy and fairness in the multiproduct pipeline

To guarantee fair access to pipeline facilities, pipe companies around the world have promoted market-based reform measures that are subject to governmental regulation. The pipe company should allocate transport capacity to shippers based on their nominations and then make schedules that align with the operational process. The reasonable mechanism is crucial for allocation results, affecting the carrier's revenue and shippers' fairness. Aiming at this issue, this paper puts forward an allocation mechanism of combining priority rules with historical data based on summarizing existing mechanisms. A multi-objective optimization model for capacity allocation that simultaneously considers economic and fairness metrics is developed. Finally, the efficient frontiers describing the economy-fairness trade-off are generated through using ε-constraint method, which can be selected by the pipe company based on its preference. Using data from a real pipeline as a case study, the solutions under different mechanisms are quantitatively evaluated. The results show that different mechanisms are applicable differently. Compared with other existing mechanisms, the proposed mechanism brings the pipe company the maximum cost of 1665.98 × 104CNY. On the basis of realizing the fair allocation, the proposed method ensures the pipe company to obtain the maximum transport cost to a certain extent as well.

Stochastic optimal allocation of grid-side independent energy storage considering energy storage participating in multi-market trading operation

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective.

A dynamic multi-objective optimization model for inner-industry carbon responsibility allocation based on carbon tax and carbon offsets accounting

The setting of Intergovernmental Panel on Climate Change (IPCC) raises concerns on curbing the excessive carbon emissions, and European Union (EU) has been proactively attempting “green” initiatives striving to achieve carbon neutrality. The current Carbon Border Adjustment Mechanism (CBAM) only works on short-term carbon reduction without much “greenness” investment incentives so far, and few studies focus on the long-standing inner-industrial carbon reduction coordination. To address these issues, this paper creatively presents a dynamic multi-objective carbon responsibility allocation model (CRAM) for EU carbon market, allows the carbon trade with a taxation based on carbon offsets accounting, and considers the final targets of sustainable “greenness” investment incentives. To find the optimal results of the designed dynamic CRAM, an improved KT-NSGA-II algorithm is proposed to detect the Pareto frontiers of each successive periods. Data from EU Cement and Aluminium industries are then selected for empirical analysis to compare the proposed CRAM to conventional CBAM model. The findings demonstrate that the superiority of CRAM in carbon emissions reduction, economic benefits improvement and green invests encouragement. With the adjustment of the inner-industrial carbon allowance trade, total carbon emissions decreased by 28.03% and the “greenness” investment initiative increased by 39.24% in contrast to the CBAM. The sensitivity analysis of the model also provides suggestions on the settings of carbon quota and tax with different industrial production process, and proposes policy recommendations for CRAM implementation.

Optimised Two-Layer Configuration of SESS-CCHP System Considering Wind and Light Output Correlation and Load Sensitivity

With the gradual depletion of fossil energy sources and the diversification of users’ energy demand, combined cooling, heating and power (CCHP) microgrids have become a hot technology to improve energy efficiency and promote efficient and synergistic energy operation. However, the uncertainty and correlation of wind power and photovoltaic (PV) outputs have posed a great challenge to the reliability of CCHP system operation, so CCHP systems are often equipped with energy storage devices to improve system flexibility to ensure the reliability of energy supply. However, system-owned reserves still have shortcomings such as high investment O&M costs and large space requirements. As an emerging model, “shared energy storage” can reduce the investment pressure of users and open up new ways for the economic and stable operation of CCHP systems. Therefore, based on the scenario of wind and solar power correlation and considering different types of load flexibility, this paper proposes to construct a shared energy storage station (SESS)-CCHP double-layer synergistic optimal allocation model. The model incorporates the consideration of the actual operation strategy of the CCHP system in the planning stage of energy storage. An example analysis shows that SESS reduces the total operating cost of the CCHP system by 25.96% and improves the new energy consumption rate by 10.46% compared with no energy storage. Compared with the system independently configured with energy storage, the cost saving is 2.14%, thus validating the effectiveness of the proposed model.

Variation characteristic analysis of regional agricultural water consumption under Budyko-type framework

ABSTRACT Agricultural water consumption security is crucial for maintaining food production stability. Aiming to investigate water consumption efficiency and objectives in agricultural and non-agricultural sectors, we adopted a Budyko-analogous framework to establish an optimal water allocation model based on the maximum water utilization benefits principle. The proposed Budyko framework was verified through its application in Anhui Province, China, and the difference in water utilization efficiencies among different regions was discussed. The research findings can be concluded as follows: (1) during 2011–2020, the provincial agricultural productive loss caused by per-unit water shortage was lower compared to non-agricultural sectors, and this trend was adaptive in the south but totally opposite in the north; (2) the guaranteed level of agricultural water consumption was higher compared to non-agricultural sectors in the north due to the predominance of agricultural production but totally contrary in the south. Overall, the above findings are consistent with the actual industrial structure of Anhui Province.

Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations. The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton. The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios. The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods. This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.

Multi-objective optimal allocation of water resources in Shule River Basin of Northwest China based on climate change scenarios

The optimal allocation of water resources is crucial for addressing regional water scarcity, adapting to climate change, and promoting sustainable development. This study focused on the Shule River Basin and considered three climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5), which is combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). A water resources optimal allocation model including water supply and demand prediction, multi-objective optimization, decision-making and analysis on optimal water allocation schemes was constructed. This model aims to analyze the optimal allocation of water resources in the Shule River Basin over three planning years (2025, 2030, 2035), considering guarantee rates of 50 % and 75 % under various climate scenarios. The obtained results indicate that: (1) An increasing trend in water demand in the planning year. For instance, under the SSP1–2.6 scenario, the basin's water demand is projected to reach 1.920×10^9 m3, 2.037×10^9 m3, and 2.103×10^9 m3 in 2025, 2030, and 2035, respectively. (2) The total water supply with a guarantee rate of 50 % (75 %) in the planning years is projected to be 1.939 (1.483)×10^9, 1.957(1.502) ×10^9, and 1.974 (1.519) ×10^9 m3, respectively. (3) In the three scenarios of SSP1–2.6, SSP2–4.5, and SSP5–8.5, the water demand for domestic and ecology is met in each planning year. There is a slight water shortage in the industrial sector, with a shortage rate ranging from 0 % to 13.41 %. The agricultural sector experiences the highest water shortage, with a rate of 1.93–44.77 %. (4) The water supply structure of each optimal allocation scheme of water resources was optimized. The economic benefits mainly depend on the industrial and ecological sectors. These findings offer valuable insights for optimizing the distribution of regional water resources in response to changing climatic conditions.

Reliability-centered availability collaborative optimization allocation approach for machine tools

The availability of computer numerical control (CNC) machine tools is a comprehensive reflection of reliability and maintainability. To ensure the availability requirements of products, it is necessary to allocate the availability indicators of the entire machine to all units. Traditional methods based on reliability allocation or maintainability allocation ignore the coupling relationships among them, which cannot comprehensively improve the availability level of machine tools. Therefore, in this paper, a availability collaborative optimization allocation model for CNC machine tools is proposed to maximize the availability under the optimal combination of failure rate and repair rate. Firstly, a two-stage reliability allocation model based on different allocation mechanisms is established by taking the Function-Motion-Action (FMA) decomposition tree as the allocation path. In the first stage, the allocation weights are determined based on global sensitivity analysis. In the second stage, the reliability optimization allocation model based on Copula function is established. Then, with reliability as the center, an availability collaborative optimization allocation model constrained by maintainability is constructed, and the non-dominated sorting genetic algorithm II (NSGA-II) algorithm is used to solve the model. Finally, a CNC gear milling machine is taken as an example to verify the rationality and effectiveness of the method on product availability requirements under collaborative allocation.

Flexible resource allocation optimization model considering global K-means load clustering and renewable-energy consumption

Abstract Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy transformation. The investment construction cost and operation cost of various flexible resources are different, and the adjustment ability is different in different timescales. Therefore, the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources. In this paper, the global K-means load clustering model is proposed and the 365-day net load is reduced to eight typical daily net loads by clustering. Secondly, a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed. The flexible resources involved include the flexible transformation of thermal power, hydropower, pumped storage, energy storage, and demand response. The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal. The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy. As the abandonment rate of renewable energy decreases, the optimal capacity of pumped storage, electrochemical energy storage, and hydropower units increases. When the power-abandonment rate of renewable energy is 5%, the optimal allocation capacity of thermal power flexibility transformation, pumped storage, electrochemical energy storage, hydropower unit, and adjustable load in Province A is 5313, 17 090, 5830, 72 113, and 4250 MW, respectively. Under the condition that the renewable-energy abandonment rate is 0, 5%, and 10% respectively, the configured capacity of pumped storage is 20 000, 17 090, and 14 847 MW, respectively.

Staged Resource Allocation Optimization under Heterogeneous Grouping Based on Interval Data: The Case of China’s Forest Carbon Sequestration

In interval data envelopment analysis (DEA), the production possibility set is variable, which causes traditional resource allocation optimization methods to yield results with limited reachability. This study aims to improve existing resource allocation optimization models so that they can produce meaningful results when handling interval data. Addressing this topic can enhance the applicability of existing models and improve decision-making accuracy. We grouped decision-making units (DMUs) based on heterogeneity to form production possibility sets. We then considered the characteristics of the worst and best production possibility sets in the interval DEA to establish multiple benchmark fronts. A staged optimization procedure is proposed; the procedure provides a continuous optimization solution, offering a basis for decision-makers to formulate strategies. To illustrate this, we provide a numerical example analysis and a case study on forest carbon sequestration. Finally, by applying our method to China’s forest carbon sink data, we show that it better meets the practical needs in China. The practical implication of this procedure is that it provides a basis for decision makers to formulate strategies based on interval data. The theoretical implication is that it extends the application of DEA models to interval data.

Integrated optimization of train stop planning and seat allocation for high-speed railways based on active management for dynamic demand

ABSTRACT Proactive demand management methods can achieve a trade-off between supply and demand for high-speed railways. Focusing on multi-type demand configurations, four active demand management methods were designed, two single-scenario and two multi-scenario methods. An optimization model of stop planning and seat allocation was constructed to minimize the difference between demand and supply, number of trains, and train-stop costs. The number of train stops, transport capacity, and train occupancy rates were considered. Particle swarm optimization and CPLEX were combined to handle this significant linear programming problem. A real case study of different demand scenarios based on the Hohhot-Beijing high-speed railway in China verified the feasibility of active demand management methods. The results indicate that integrated optimization can be justified with mixed load patterns. Compared with the original scheme, the optimized scheme reduced the number of trains by 23.4% and increased train occupancy rate by more than 36.7%.

Retraction Note: A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO)

Retraction Note: A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO)

Improving Transparency of Decision Models Through the Application of Decision Analytic Models with Omitted Objects Displayed (DAMWOOD).

The coronavirus disease 2019 (COVID-19) pandemic has increased public awareness of the influence of epidemiological and economic decision models on public policy decisions. Alongside this is an increased scrutiny on the development, analysis, reporting and utilisation of decision models for public policy making. Therefore, it is important that model developers can clearly explain and justify to all stakeholders what is included and excluded from a model developed to support decision-making, to both improve transparency and trust in decision-making. Our aim is to provide tools for improving communication between modellers and decision-makers, leading to improved transparency in decision-making. To do so, we extend the recently described directed acyclic graphs with omitted objects displayed (DAGWOOD) approach from Haber et al. (Ann Epidemiol 68:64-71, 2022) to decision analytic models, giving the decision analytic models with omitted objects displayed (DAMWOOD) approach. DAMWOOD is a framework for the identification of objects omitted from a decision model, as well as for consideration of the effects of omissions on model outcomes. Objects omitted from a decision model are classed as either an exclusion (known and unknown confounders), misdirection (alternative model pathways) or structure (e.g. model type, methods for estimating relationships between objects). DAMWOOD requires model developers to use explicit statements and provide illustration of included and omitted objects, supporting communication with model users and stakeholders, allowing them to provide input and feedback to modellers about which objects to include or omit in a model. In developing DAMWOOD, we considered two challenges we encountered in modelling for pandemic policy response. First, the scope of the decision problem is not always made sufficiently explicit by decision-makers, requiring modellers to intuit which policy options should be considered, and/or which outcomes should be considered in their evaluation. Second, there is rarely sufficient transparency to ensure stakeholders can see what is included in models and why. This limits stakeholders' ability to advocate to decision-makers for the prioritisation of specific outcomes and challenge the model results. To illustrate the application of DAMWOOD, we apply it to a previously published COVID-19 vaccine allocation optimisation model. The DAMWOOD diagrams illustrate the ways in which it is possible to improve the communication of model assumptions. The diagrams make explicit which outcomes are omitted and provide information on the expected impact of the omissions on model results. We discuss the usefulness of DAMWOOD for framing the decision problem, communicating the model structure and results and engaging with those making and affected by the decisions the model is developed to inform.

Robust portfolio optimization model for electronic coupon allocation

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

Multi-objective double layer water optimal allocation and scheduling framework combing the integrated surface water – groundwater model

Balancing the water consumption of agricultural and ecological is the key point of sustainable social and economic development in an inland river basin. The growth of desert riparian forests in inland river basins mainly depends on a certain phreatic water table depth (PWTD). The main object of this study was to allocate and schedule water resources to regulate the PWTD and satisfy agricultural water demand. Therefore, a multi-objective double layer optimal allocation and scheduling framework based on the computationally efficient integrated surface water-groundwater model (ISGWM), which can simulate the surface water processes, groundwater recharge and discharge processes, and PWTD changes, was constructed and applied to the mainstream of Tarim River Basin (TRB). The top layer model of the framework is an optimal ecological water allocation model, and its optimal allocation results are used as the initial solution of the bottom layer model. The results show that under 5 different inflow frequencies, the agricultural water shortage rate is 0, 17.38 %, 17.41 %, 14.06 %, and 19.94 %, respectively. The PWTD regulation has a great performance. After the optimal scheduling, the proportions of good growth of the control area behind the gate under different inflow frequencies were 98.18 %, 98.18 %, 98.18 %, 90.91 %, and 94.55 %. Agricultural water shortage is mainly due to the non-uniformity distribution of intra-annual inflow and the lack of controlling hydraulic engineering. The regulation of PWTD can guarantee the growth of desert riparian forests on both sides of the mainstream of TRB. Besides, we explored the feasibility of exploiting groundwater to supplement agricultural water consumption. The groundwater exploitation should be controlled within the scope of not causing excessive increase of PWTD (difference between PWTD and target depth <1 m), due to the groundwater exploitation to supplement agricultural water will lead to the increase of PWTD. Overall, this framework, which regulates the PWTD with the change of ecological water supply based on the ISGWM, provides a new idea for the allocation and scheduling of agricultural and ecological water resources in arid inland river basins. It also provides a new method for the coupled cooperative operation of surface water and groundwater.

Principles and Optimization of China’s Unconventional Water Management: From a Brand-New Perspective of Responsibility Allocation

Unconventional water includes reclaimed water, harvested rainwater, desalinated seawater, and mine water. Unconventional water use is considered more of a “mandatory responsibility” in China. The initial allocation of unconventional water emphasizes quantity-centered responsibility allocation while the minimum utilization reflects this responsibility. The unconventional water use responsibility (UWUR) should be tailored to the characteristics of each area, moving away from a ‘more is better’ mindset. However, there is a large research gap in this field. This paper first presented six fundamental principles for unconventional water allocation. Ensuring fairness in allocation involves aligning the allocated amount with urban water usage characteristics. Hence, based on four key features, this paper integrated various socioeconomic and environmental factors to build an initial allocation model. To enhance efficiency, an optimal allocation model was constructed using the zero-sum gains–data envelopment analysis (ZSG-DEA) method. The models were then applied to Jiangsu Province, China, to verify their applicability. The results showed that the projected minimum UWUR allocation (unit: 100 million m3) for each city in 2025 is 1.482 (Nanjing), 1.501 (Wuxi), 0.919 (Xuzhou), 1.029 (Changzhou), 2.977 (Suzhou), 1.497 (Nantong), 0.818 (Lianyungang), 0.766 (Huai’an), 0.875 (Yancheng), 0.920 (Yangzhou), 0.790 (Zhenjiang), 0.858 (Taizhou), and 0.766 (Suqian). The rational and feasible results indicated that the allocation framework proposed in this paper has a certain practicability. Lastly, this paper considered the differences in unconventional water utilization conditions across 13 cities and proposed corresponding measures to improve the utilization. This paper represents a tentative exploration of unconventional water allocation in China and offers theoretical and practical insights for policy-makers to improve territorial spatial planning and sustainable water management.

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Applying artificial intelligence (AI) and machine learning (ML) techniques to optimize waste management strategies, focusing on enhancing economic efficiency and reducing environmental impact, is vital. The study utilized ML models to analyze and forecast waste generation trends, assess the viability of various waste management methods, and develop optimization models for resource allocation and operational efficiency. The research employs the World Bank’s comprehensive waste management dataset. After rigorous data preprocessing, including cleaning and feature selection, a variety of ML techniques, such as regression models, classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and optimization algorithms, including linear programming, are applied. Unlike other research, this study achieved 85 % accuracy on predictive analytics models for forecasting waste generation trends, primarily attributed to integrating more diverse data sets, including socio-economic factors. Also, the optimization resource allocation achieved a 15 % increase in operational efficiency. These findings provide significant insights for policymakers and urban planners, suggesting that integrating ML in waste management can lead to more sustainable and cost-effective practices. This paper demonstrates the transformative potential of ML in optimizing waste management strategies, offering a pathway towards more sustainable and economically viable waste management solutions globally.