Search Results (41)

The rapid accumulation of Waste Electrical and Electronic Equipment (WEEE) presents severe environmental and resource challenges in high-density metropolises. Traditional reverse logistics (RL) network designs often overlook urban morphological constraints and treat recovery rates as static parameters. To address these gaps, this study proposes a GIS-integrated low-carbon WEEE RL framework. A Spatial Multi-Criteria Decision Analysis (MCDA) workflow first deduces optimal facility layouts avoiding ecological exclusion zones. Subsequently, a Fuzzy Mixed-Integer Linear Programming (FMILP) model endogenizes the dynamic recovery rate and enforces discrete vehicle dispatching, solved via an advanced Geospatially Constrained Multiple-Priority Genetic Algorithm (MPGA). Validated in Jinan, China, the framework consistently outperforms contemporary benchmarks. Crucially, it reveals that traditional continuous models underestimate urban carbon footprints by 34.6%. By adopting the optimal spatial compromise, policymakers can achieve a 19.9% carbon reduction at a marginal 12.7% profit sacrifice, effectively harmonizing decarbonization with commercial viability. Full article

The ongoing digitalization of building operation processes creates new opportunities to improve maintenance and renovation decision-making. Despite the increasing use of BIM, renovation decisions in residential buildings are still often based on fragmented data, heterogeneous documentation, and subjective expert assessments. This challenge is particularly relevant for large-panel housing in Central and Eastern Europe, where aging building stock requires systematic long-term modernization strategies. This paper presents a Renovation Decision Support System (RDSS) integrating a simplified BIM model, technical documentation, diagnostic data, and machine learning methods to support renovation planning. The system consists of five modules: the Building Information Model Module (BIMM), Geometric and Technical Documentation Module (GTDM), Building Condition Assessment Module (BCAM), Building Performance and Condition Prediction Module (BPCM), and Renovation Decision Optimization Module (RDOM). Data exchange is managed through a Common Data Environment (CDE). The system combines multi-criteria building condition assessment with fuzzy inference to determine renovation urgency and long-term optimization using Mixed-Integer Linear Programming (MILP). Budget constraints, activity sequences, time horizons, and user preferences are considered to generate alternative renovation scenarios. The proposed approach supports sustainable management of existing buildings, improves decision transparency, and enables data-driven renovation planning consistent with life-cycle management principles. Full article

Agri-food supply chain management (ASCM) involves hierarchical structures in which actors make autonomous decisions and pursue objectives that may conflict with one another, thereby hindering coordination and limiting the understanding of how these decisions affect overall chain performance. This study proposes a decentralized bilevel mixed-integer linear programming model (BLDPP) for ASCM, solved using an interactive fuzzy decision-making approach that integrates membership functions with multi-objective programming. The model was validated through a case study conducted on an agri-food supply chain in Colombia. The results show that the interactive fuzzy approach enabled the development of a planning scheme that achieved a 94% satisfaction level among all decision-makers, demonstrating its effectiveness in harmonizing potentially conflicting interests. Additionally, the resulting planning incorporated up to 99% of the total productive capacity of small producers into the purchasing plan, supporting their inclusion in the chain. These findings indicate that both the proposed management model and its solution approach offer a robust alternative for advancing toward socially sustainable management of agri-food supply chains. Full article

This study develops a fuzzy linear multi-objective programming (FLMOP) model to optimize Taiwan’s online food delivery (OFD) systems by jointly considering time, cost, quality, and carbon emissions (TCQCE) under strict Hazard Analysis and Critical Control Point (HACCP) safety constraints. By integrating fuzzy set theory with triangular fuzzy numbers (TFN) and employing centroid defuzzification, this model effectively addresses uncertainties in delivery time, cost, and quality. Empirical results demonstrate that controlled delivery-time extension and order batching reduce carbon emissions by 20%, maintain food quality at 89.3%, and lower delivery costs by 15% under large-scale operations. Statistical validation (p = 0.002) and sensitivity analysis confirm robustness and low variability. Comparative benchmarking highlights FLMOP’s superiority over mixed-integer linear programming (MILP) and genetic algorithms/non-dominated sorting genetic algorithm II (GA/NSGA-II), achieving higher hypervolume (0.904 vs. 0.836 and 0.743) and near-optimal solutions within 11 s, making it suitable for real-time decision-making. This study establishes a benchmark for sustainable last-mile OFD and offers practical guidelines for Taiwan’s OFD platforms. Full article

This study presents a practical framework for the maximal covering location problem (MCLP) under uncertainty. The approach combines possibility theory with chance-constrained programming to represent both imprecision and randomness in demand. Demand is modeled as fuzzy random variables. Using the Zadeh extension principle, both the fuzzy and fuzzy random formulations are transformed into equivalent deterministic mixed-integer programs. Clear linearization steps are provided for the objective function and constraints. Two specifications are examined to reflect different attitudes toward risk. The first specification uses possibility measures, reflecting an optimistic stance, while the second uses necessity measures and represents a conservative approach. Computational experiments conducted in an urban facility context show that increasing the possibility or probability level results in more conservative solutions and a smaller amount of covered demand. In contrast, lower thresholds lead to more exhaustive coverage with greater exposure to uncertainty. In the deterministic scenario, full coverage becomes attainable as the number of facilities increases. Under uncertainty, the models balance coverage with robustness based on the chosen risk tolerance levels. The proposed framework serves as a flexible decision support tool, enabling planners to align facility location choices with their risk tolerance while maintaining tractability with standard optimization solvers. Full article

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

Driven by the rapid expansion of e-commerce in China, there is a growing demand for high-efficiency, sustainability-oriented logistics solutions in rural regions, particularly for the time-sensitive distribution of perishable agricultural commodities. Traditional logistics systems face considerable challenges in these geographically complex regions due to limited infrastructure and extended travel distances. To address these issues, this study proposes an intelligent cooperative delivery system that integrates automated drones with conventional trucks, aiming to enhance both operational efficiency and environmental sustainability. A mixed-integer linear programming (MILP) model is developed to account for the diverse terrain characteristics of rural China, including forest, lake, and mountain regions. To optimize distribution strategies, the model incorporates an improved Fuzzy C-Means (FCM) algorithm combined with a hybrid genetic simulated annealing algorithm. The performance of three transportation modes, namely truck-only, drone-only, and truck–drone integrated delivery, was evaluated and compared. Sustainability-related externalities, such as carbon emission costs and delivery delay penalties, are quantitatively integrated into the total transportation cost objective function. Simulation results indicate that the cooperative delivery model is especially effective in lake regions, significantly reducing overall costs while improving environmental performance and service quality. This research offers practical insights into the development of sustainable intelligent transportation systems tailored to the unique challenges of rural logistics. Full article

The increasing emphasis on integrating sustainability into corporate operations has prompted supply chain managers to incorporate not only economic objectives but also environmental and social considerations into their network designs. This study presents a structured six-stage methodology to develop a fuzzy multi-objective optimization model for the sustainable design of a multi-level, multi-product forward supply chain network. The model incorporates two conflicting objectives: minimizing total network costs and reducing environmental impact. To quantify environmental performance, a comprehensive life cycle assessment is conducted in accordance with the ISO 14040 standard and the ReCiPe 2016 method, focusing on three impact categories: human health, resources, and ecosystems. To address uncertainty in demand and production costs, fuzzy mixed-integer linear programming is employed. The model is validated and applied to a real-world case study of a dairy small-to-medium enterprise in Ecuador. Using the epsilon-constraint method, a Pareto frontier is generated to illustrate the trade-offs between the economic and environmental objectives. This research provides a robust decision-making tool for uncertain environments and advances knowledge on the integration of life cycle assessment with supply chain optimization and network design methodologies for sustainable development. Full article

The reverse logistics of medical textiles has become a major concern in Morocco today, compelling authorities and professionals to develop a sustainable reverse logistics model. This study proposes a model for designing a sustainable closed-loop supply chain network in a fuzzy environment, using the medical textile life cycle as a case study. The model aims to generate economic gains, increase corporate social responsibility through job creation, and mitigate risks associated with the transportation of end-of-life products. In addition, the uncertainty of the model parameters is considered. The multi-objective model, formulated as a mixed-integer linear program, was solved using an exact approach, enabling strategic and tactical decision-making. Furthermore, the results demonstrate that accounting uncertainty can significantly impact strategic and tactical decisions in network design. Full article

This study addresses an intermodal routing problem encountered by an intermodal transportation operator fulfilling the food grain transportation order of an agri-food company. To enhance the environmental sustainability of food logistics, carbon tax and trading regulations have been employed to reduce the carbon emissions associated with transportation. Multi-source uncertainties, including the company’s demand for food grains and various parameters related to the intermodal transportation activities, are modeled via trapezoidal fuzzy numbers to optimize the comprehensive reliability of the solution. This work incorporates wastage reduction by lowering the wastage costs and formulating a wastage threshold constraint in intermodal routing. Accordingly, a fuzzy mixed-integer nonlinear programming model for a green and reliable intermodal routing problem for food grain transportation is proposed. To overcome the model’s insolvability and the difficulty in finding the global optimum solution to a nonlinear optimization model, a two-stage solution method is developed, employing chance-constrained programming and linearization technique to reformulate the initial model. A numerical case study is given to verify the feasibility of the proposed methods. Sensitivity analysis reveals the influence of confidence levels and wastage threshold, providing insights for the agri-food company to balance economics, reliability, and wastage reduction in food grain transportation. The numerical case study also analyzes the feasibility of carbon tax and trading regulations in reducing carbon emissions, concluding that carbon tax regulations consistently achieve greater reductions and are universally feasible. In contrast, the feasibility of carbon trading regulations depends on confidence levels and wastage threshold. The findings of this work could provide strong quantitative support for intermodal transportation operators and agri-food companies seeking to implement sustainable food grain transportation. Full article

In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them. Full article

The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities. Full article

Due to global environmental impacts, the electric vehicle (EV) adoption rate is increasing. However, unlike conventional petrol vehicles, EVs take a considerable time to charge. EVs on the road with different battery charging statuses and driving demographics may cause uncertain peak time arrivals at charging stations. Battery-swappable charging stations are a quick and easier way to replace uncharged batteries with charged ones. However, charging due to uncertain EV arrival causes higher charging profiles posing load to the grid, management of charged and discharged batteries, and peak time charging tariffs. These challenges hinder the wide operation of battery-swappable charging stations. Nevertheless, a pre-assessment of peak hours using EV demographics can reduce congestion. In recent literature surveys for battery-swappable charging stations, spot congestion has not been given much attention, which has a direct influence on the sizing and operation of battery-swappable charging stations. This research study is focused on estimating peak time events using a novel integrated techno-economic assessment framework. A fuzzy-based parametric assessment tool is developed that identifies the factors that influence higher congestion events. Based on the peak event assessment, grid, and solar PV-based generation is optimized using mixed integer linear programming. In the final step, an environment analysis of a swappable charging station is performed. Furthermore, the results achieved using the proposed framework for battery-swappable charging stations (BSCSs) were compared with fast-charging (FC) stations. FC can economically perform well if integrated with solar PV systems; however, the capital cost is 80% greater than the BSCSs designed under the proposed framework. The operational cost of BSCSs is 39% higher than FC stations as they use 29% higher grid units than FC stations due to night operations under congestion. Full article

The park-integrated energy system can achieve the optimal allocation, dispatch, and management of energy by integrating various energy resources and intelligent control and monitoring. Flexible load participation in scheduling can reduce peak and valley load, optimize load curves, further improve energy utilization efficiency, and reduce system costs. Based on this, firstly, a flexible power-load model is established considering the translatable load, transferable load, and reducible load; and a thermal flexible load model is established based on the fuzziness of user perception of temperature in this study; then, the mixed integer linear programming method is adopted, and the sum of the carbon transaction cost, operation and maintenance cost, compensation cost, power purchase cost, gas purchase cost, wind and light abandonment penalty cost and investment cost of the system is minimized as the objective function, and the configuration of the integrated energy system is optimized, and the optimal capacity of each equipment and the output of each period are obtained. Finally, taking an industrial park in Liaoning Province of China as an example, the analysis is carried out. The example results show that by scheduling the flexible electrical load and flexibly adjusting the indoor temperature, renewable energy consumption can be promoted, and electricity load and heat-load curves can be optimized to increase the installed capacity of wind turbines, reduce the capacity of gas turbines, batteries, and heat-storage tanks, improve system economy, and improve the penetration rate of renewable energy. Full article

The water environment restoration project portfolio (WERP) selection is discussed in this paper. By complying with the analysis of the project’s multidimensional property and operation mode, this paper develops the chance constraint and the management constraint of the WERP from the perspectives of public service and enterprise operation. In addition, the multi-objective mixed integer linear programming model is constructed by combining the expectation method and the fuzzy chance constraint programming method. The results demonstrate that: (1) Our proposed method successfully circumvents the occurrence of local objective optimization within a specific confidence interval, thereby achieving a balance between economic and water environment restoration objectives; (2) including fuzzy chance constraints in our proposed method significantly diminishes the risk of exceeding the WERP capacity, thereby ensuring the effectiveness of water environment restoration by adopting a market-based approach. However, further examination of the impact of various sub-projects in WERP is necessary, along with the integration of novel evolutionary algorithms to enhance the efficiency of our model. Full article