Search Results (504)

Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative to truck haulage, as it demonstrates a higher degree of economic efficiency. In a theoretical and practical sense, due to its direct impact on the extraction plan, defining the optimal position of the crusher and consequently the system of conveyors is often the most challenging problem of this methodology. This paper introduces an innovative approach to determining the optimum waste haulage configuration by comparing conventional truck-based transport with IPCC systems. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem, explicitly incorporating spatial dimensions and the relocation costs of semi-mobile crushers. The model situates the crusher in a way that reduces transfer costs throughout production periods and it has been tested on a hypothetical open pit. Full article

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

Efficient berth allocation remains a critical challenge in bulk port operations due to the stochastic nature of vessel arrivals and the complex interaction among loading resources. This study proposes an integrated optimisation–simulation framework to minimise total demurrage costs under uncertainty. The mathematical model was formulated as a mixed-integer linear program (MILP) to determine the optimal assignment and sequencing of vessels to berths and shiploaders, subject to time-window and capacity constraints. The MILP was solved using a Simulated Annealing (SA) metaheuristic to improve computational efficiency for large-scale instances. Subsequently, the optimised berth plans were evaluated in FlexSim, a discrete-event simulation environment, to assess the operational variability arising from stochastic factors, including vessel arrival times, service durations, and loader availability. System performance was measured through vessel waiting time, berth utilisation rate, and demurrage cost variability across multiple replications. Results indicate that the proposed SA–FlexSim framework reduced average demurrage costs by 28.7% compared to the deterministic MILP and by 21.3% relative to standalone SA, confirming its effectiveness and robustness under uncertain operating conditions. The hybrid methodology provides a practical decision-support tool for terminal operators seeking to enhance scheduling reliability and cost efficiency in bulk port environments. Full article

With the rapid advancement of battery technology, new energy hybrid tugboats have been progressively adopted. In order to align with the trend of electrifying tugboat fleets, a mixed-integer linear programming (MILP) model for the joint scheduling of new energy hybrid tugboats and berths has been established. The model incorporates the constraint imposed by the limited number of tugboat charging connectors. The objective is to minimize the total cost over the scheduling horizon, including ship waiting, delayed-departure costs, and the operating costs of both conventional diesel and hybrid tugboats. In light of the characteristics inherent to the problem, a hybrid solution approach combining CPLEX with a heuristic-enhanced whale optimization algorithm (WOA) is employed to solve the model. A case study was conducted using data on the energy consumption of tugboats at Xiamen Port. The effectiveness of the model and algorithm was then verified through a series of small-scale instance experiments. Finally, a comprehensive sensitivity analysis of key parameters is finally conducted, including the number of tugboat charging connectors, battery capacity, and charging rate. This analysis provides valuable guidance for port tugboat operations. Full article

To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.6–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article

Uncontrolled charging of large electric bus fleets can strain constrained power grids, such as South Africa’s. This study develops and evaluates a demand-oriented charging strategy for Golden Arrow Bus Services using a Mixed-Integer Linear Programming (MILP) model calibrated with real operating data. The model schedules fleet charging over an off-peak window to minimise the highest total demand charge (Notified Maximum Demand, NMD) while respecting arrival state of charge (SOC), Time-of-Use (ToU) tariffs, and ensuring all vehicles are fully charged before dispatch. Compared to the unmanaged baseline, the optimised schedules reduce the peak demand charge by 17%, keeping total depot demand below 1 MW and ensuring full fleet readiness. The strategy also eliminates all energy consumption during expensive peak-tariff windows in both winter and summer. Further analysis shows that raising the minimum arrival SOC reduces the required optimum per-bus demand approximately linearly (≈1.5 kW per +5% SOC), whereas widening the SOC arrival range increases demand variability. This MILP framework demonstrates that exploiting SOC diversity and modest charge capacity capping can significantly lower peak demand and operational costs, offering a validated model for depots in other capacity-constrained power systems. Full article

The growing demand for renewable energy positions biorefineries as key to enhancing biofuel competitiveness. This study proposes a novel MILP superstructure integrating resource seasonality, process selection, and heat integration to optimize biomethanol production in a sugarcane biorefinery. A multi-objective optimization balancing net present value (NPV) and avoided CO₂ emissions reveals that energy integration improves environmental performance with limited economic impact. The model estimates the production of up to 66.85 kg of biomethanol/ton sugarcane from bagasse gasification, 40.7 kg e-methanol/ton sugarcane via CO₂ hydrogenation, and 3.68 kg of biomethane/ton sugarcane from biogas upgrading. Hydrogen production through biomethane reforming and photovoltaic-powered electrolysis increases methanol output without raising emissions. The integrated system achieves energy efficiencies of up to 57.3% and enables the avoidance of up to 493 kg of CO₂/ton sugarcane over the planning horizon. When thermal integration is excluded, efficiency drops by 8% and net energy production per area falls by 11%, due to the need to divert bagasse to cogeneration. Although economic challenges remain, CO₂ remuneration ranging from USD 3.27 to USD 129.79 per ton could ensure project viability. These findings highlight the role of integrated energy systems in enabling sustainable and economically feasible sugarcane biorefineries. Full article

The transition to sustainable energy systems increasingly relies on advanced optimization methods to address the challenges of designing and operating them efficiently. Seasonal storage systems play a pivotal role in aligning renewable energy generation with fluctuating energy demand, with ice storage emerging as a promising solution for seasonal energy storage. This paper presents a novel optimization framework for the operation of seasonal ice-storage systems, leveraging Mixed-Integer Linear Programming (MILP) with time-series aggregation (TSA) techniques. The proposed model accurately captures the physical behavior of ice storage, incorporating both latent and sensible heat storage phases, discrete temperature levels, and charging/discharging efficiency curves. A key feature of this framework is its ability to address computational challenges in large-scale optimization, while maintaining high detail. Using a business park in Germany as a case study, the results demonstrate a significant reduction in computational time of up to 80% for 110 typical periods, with only a 2.5% deviation in the objective value and 9% in the Seasonal Energy Efficiency Ratio (SEER), although this efficiency gain depends on the number of typical periods used. This work addresses key gaps in seasonal ice-storage optimization models and provides a robust tool for designing and optimizing sustainable energy systems. Full article

Air transportation has accelerated international trade, and the efficient use of cargo aircraft capacity supports logistics operations, reduces expenses, and benefits the environment. In this study, we formulate a mathematical programming model to solve the cargo aircraft capacity optimization problem and propose simplified approaches for practical applications. We investigate Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA), and Large Neighborhood Search (LNS) techniques. MILP yields optimal solutions for small instances but cannot handle large-scale, real-world problems due to excessive computation time; therefore, we combine the GA and LNS. The GA provides acceptable solutions rapidly, and LNS refines them by exploring larger solution spaces. Thus, this hybrid approach leverages the GA’s exploration capability and LNS’s exploitation ability to produce high-quality solutions efficiently. Our experimental results show that the hybrid GA-LNS method outperforms the MILP and single approaches in terms of capacity usage, loading duration, and computational time. This study provides an applicable model with practical constraints and guidelines for air cargo and cost reduction, operational efficiency, and safety. Full article

The high electricity and water consumption in industrial textile dyeing processes represents an environmental and economic challenge, requiring optimization strategies to reduce costs and impacts toward cleaner production. This work proposes an optimization model to minimize costs associated with water and electricity consumption in industrial textile dyeing processes. The model has a Mixed Integer Linear Programming (MILP) formulation. The objective function to be minimized is the total process costs. The constraints consider production capacity, daily production limits, and specific costs per material. A case study was conducted in a real industrial process for three types of tissue: cotton, polyester, and polyamide. The model was coded in GAMS and the CPLEX solver was used to solve the problem. The results showed that water consumption accounted for 78.2% of the total cost in the optimal solution. Using the same model, an alternative simulation was performed, replacing four smaller-capacity machines with a single larger-capacity machine, resulting in a marginal reduction in total costs. Simulations were also performed to replace the current machines with highly efficient automated HT (High Temperature) machines, indicating a potential 71.39% reduction in water consumption costs. The conclusion is that the proposed model is effective for optimizing textile dyeing processes, balancing operational efficiency and sustainability, and is applicable in complex industrial scenarios. Full article

The estimated increase in urban solid waste generation in the near future worldwide may negatively impact the environment and public health, and produce a significant economic impact on solid waste management. Recycling is crucial in mitigating this solid waste generation growth by diverting materials from landfills, reducing greenhouse gas emissions and pollution, conserving resources, and extending end-of-life strategies. In this study, we address the bin location–allocation problem for the collection of recyclable household waste, a key challenge in the context of the circular economy and efforts to mitigate the sustained growth of household waste generation. To tackle this problem, this study generalizes a previous mixed-integer linear programming (MILP) model to address different types of waste, particularly recyclable household waste, while minimizing total bin costs and ensuring that each generation point is assigned to the nearest collection site within a given threshold travel distance. Additionally, the model compares single and multi-stream collection strategies. For each case, we evaluate the options of locating recycling bins at road intersections and in open public spaces. Real-world data from the commune of Quinta Normal in Santiago, Chile is used to test our approach. This study also reports results of a sensitivity analysis of key parameters, including the generated household recyclable waste and the maximum distances users are willing to travel to dispose of their recyclable waste. Finally, managerial implications that emerge from this study are discussed, which may help authorities improve recyclable household waste collection, and outline future research directions. Full article

Environmental concerns and the increasing scarcity of resources force decision makers in the supply chain to consider reuse and re-production. Closed loop supply chain is a fundamental concept that has attracted the attention of many researchers due to its profitability for businesses as well as its positive environmental and social effects. Closed-loop supply chains and sustainability dimensions are complementary because of their mutual effects. This paper develops a mathematical model to design a sustainable closed-loop supply chain network in the wood–plastic composite industry. Due to the nature of the problem considered, a mixed-integer linear programming method is utilized. The proposed model is a multi-objective model, and the Lp-metric method is used to solve it. The proposed model is applied in a real case in Iran. The proposed model identified 17 optimal provinces for manufacturing centers, 15 for reuse centers, and 9 for reproduction centers. Verification and validation of the proposed model illustrate its capability in real world implications. Full article

Multi-story residential buildings present distinct challenges for demand-side management due to shared infrastructure, diverse occupant behaviors, and complex load profiles. Although demand-side management strategies are well established in industrial sectors, their application in high-density residential communities remains limited. This study proposes a cost-optimized energy management framework for urban multi-story apartment buildings, integrating rooftop solar photovoltaic (PV) generation, shared battery energy storage, and flexible electric vehicle (EV) charging. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. EVs are modeled as flexible common loads under strata ownership, alongside shared facilities such as hot water systems and pool pumps. The optimization framework ensures equitable access to battery storage and prioritizes energy allocation from the most cost-effective source solar, battery, or grid on an hourly basis. Two seasonal scenarios, representing summer (February) and spring (September), are evaluated using location-specific irradiance data from Joondalup, Western Australia. The results demonstrate that flexible EV charging enhances solar utilization, mitigates peak grid demand, and supports fairness in shared energy usage. In the high-solar summer scenario, the total building energy cost was reduced to AUD 29.95/day, while in the spring scenario with lower solar availability, the cost remained moderate at AUD 31.92/day. At the apartment level, energy bills were reduced by approximately 34–38% compared to a grid-only baseline. Additionally, the system achieved solar export revenues of up to AUD 4.19/day. These findings underscore the techno-economic effectiveness of the proposed optimization framework in enabling cost-efficient, low-carbon, and grid-friendly energy management in multi-residential urban settings. Full article

The recent updates to the Single Day-Ahead Coupling (SDAC) framework in the European energy market, along with new rules for providing manual frequency restoration reserve (mFRR) products in the Nordic Energy Activation Market (EAM), have introduced a finer Market Time Unit (MTU) resolution. These developments underscore the growing importance of flexible assets, such as power-to-hydrogen (PtH) facilities, in delivering system flexibility. However, to successfully participate in such markets, well-designed and accurate bidding strategies are essential. To fulfill this aim, this paper proposes a Mixed Integer Linear Programming (MILP) model to determine the optimal bidding strategies for a typical PtH facility, accounting for both the technical characteristics of the involved technologies and the specific participation requirements of the mFRR EAM. The study also explores the economic viability of sourcing electricity from nearby wind turbines (WTs) under a Power Purchase Agreement (PPA). The simulation is conducted using a case study of a planned PtH facility at the Port of Hirtshals, Denmark. Results demonstrate that participation in the mFRR EAM, particularly through the provision of downward regulation, can yield significant economic benefits. Moreover, involvement in the mFRR market reduces power intake from the nearby WTs, as capacity must be reserved for downward services. Finally, the findings highlight the necessity of clearly defined business models for such facilities, considering both technical and economic aspects. Full article

This paper investigates the joint scheduling problem of unmanned aerial vehicles (UAVs) and trucks for community logistics, where UAVs act as service providers for last-mile delivery and trucks serve as mobile storage platforms for drone deployment. To address the complexity of decision variables, this paper proposes a three-stage solution scheme that divides the problem into the following: (1) UAV mission set generation via clustering, (2) truck-drone route planning, and (3) collaborative scheduling via a Mixed-Integer Linear Programming (MILP) model. The MILP model, solved exactly using Gurobi, optimizes truck movements and drone operations to minimize total delivery time, representing the core contribution. In the experimental section, to verify the correctness and effectiveness of the proposed Mixed-Integer Linear Programming (MILP) model, comparative experiments were conducted against a heuristic algorithm based on empirical intuitive decision-making. The solution results of experiments with different scales indicate that the joint scheduling model outperforms the scheduling strategies based on empirical experience across various scenario sizes. Additionally, multiple experiments conducted under different parameter settings within the same scenario successfully demonstrated that the model can stably be solved without deteriorating results when parameters change. Furthermore, this study observed that the relationship between the increase in the number of drones and the decrease in the total consumed time is not a simple linear relationship. This phenomenon is speculated to be due to the periodic patterns exhibited by the drone scheduling sequence, which align with the average duration of individual tasks. Full article