Explore the vast range of titles available.

Nowadays, the intensification of a competitive environment in markets in conjunction with sustainability issues has forced organizations to concentrate on designing sustainable closed-loop supply chains. In this study, a sustainable closed-loop supply chain network is configured under uncertain conditions based on fuzzy theory. The proposed network is a multi-product multi-period problem which is formulated by a bi-objective mixed-integer linear programming model with fuzzy demand and return rate. The objectives are to maximize the supply chain profit and customer satisfaction at the same time. Moreover, the carbon footprint is included in the first objective function in terms of cost (tax) to affect the total profit and treat the environmental aspect. Fuzzy linear programming and Lp-metric method are then applied to deal with the uncertainty and bi-objectiveness of the model, respectively. In order to validate the methodology, a case study problem in the dairy industry is investigated where the proposed Lp-metric is also compared to goal attainment method. The obtained results demonstrate the superiority of Lp-metric against goal attainment method as well as the applicability and efficiency of the proposed methodology to treat a real case study problem. Furthermore, from the management perspective, outsourcing the production during high-demand periods is highly recommended as an efficient solution.

To answer the question of whether the growth of digital inputs can be beneficial for carbon neutrality, we thoroughly explore the impacts of digital inputs on carbon emission reduction in this work. We propose a combined framework of panel regression model and multi-objective optimization model to identify the key digital sectors and obtain their optimal total outputs. First, the results show that digital inputs continue to increase in most countries (regions) from 2000 to 2021, especially in the USA, EU countries and China. Digital equipment inputs in China are the most significant, while digital service inputs in the USA and EU countries are relatively important. Second, the regression results show that digital service inputs have significantly negative influence on carbon emissions, which means that the growth of digital service inputs will decrease carbon emissions. This result indicates that the key point of industrial digitalization for carbon emission reduction may be increasing the digital service inputs. Third, the optimization results show that the digital-input-oriented optimization model, which encourages an increase in digital service inputs, could achieve greater targets of economic growth and carbon emission reduction. The total outputs of Telecommunication Services and Computer Services should increase globally by 10.24% and 8.89%, respectively.

INTRODUCTION: In modern power systems, the optimization of intelligent equipment scheduling for transmission lines is a key task. OBJECTIVES: To improve the effectiveness of scheduling optimization, this study introduces an intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm. METHODS: This model achieves global and local search capabilities through an improved Bacterial Foraging Optimization algorithm, maintaining the diversity of equipment states and effectively improving the optimization level of scheduling results. RESULTS: At 3000 iterations, the model was able to reach its optimal state, and its optimization results showed excellent performance in terms of convergence and uniformity, which was very close to the optimal solution. In practical applications, the performance of the intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm is also excellent. The average line usage rate of the scheduling scheme proposed by the model reached 70.69%, while the average line usage rate of the manual scheduling scheme was only 64.63%. In addition, the optimal relative error percentage of this model is less than 2.1%, while the BRE of other algorithms reaches around 10%. CONCLUSION: The intelligent equipment scheduling optimization model for transmission lines on the ground of improved Bacterial Foraging Optimization algorithm has important practical significance for improving the operational efficiency of the power system, reducing operating costs, and making sure the stable and reliable operation of the power system.

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.

For interval-valued intuitionistic fuzzy sets featuring complementary symmetry in evaluation relations, this paper proposes a novel, complete fuzzy multi-attribute group decision-making (MAGDM) method that optimizes both expert weights and attribute weights. First, an optimization model is constructed to determine expert weights by minimizing the cumulative difference between individual evaluations and the overall consistent evaluations derived from all experts. Second, based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the improved closeness index for evaluating each alternative is obtained. Finally, leveraging entropy theory, a concise and interpretable optimization model is established to determine the attribute weight. This weight is then incorporated into the closeness index to enable the ranking of alternatives. Integrating these features, the complete fuzzy MAGDM algorithm is formulated, effectively combining the strengths of subjective and objective weighting approaches. To conclude, the feasibility and effectiveness of the proposed method are thoroughly verified and compared through detailed examination of two real-world cases.

Multi-objective optimization problems can be solved through Simulation-Optimization (S-O) techniques where the pareto front gives the optimal solutions in the problem domains. During the selection of different modelling methods, optimization techniques and management scenarios, several pareto fronts can be generated. In the present work, an attempt has been made by performing intensive comparisons between different pareto fronts to compare the efficiency and convergence of different S-O models. In this process, groundwater models were developed to simulate the River-Aquifer (R-A) exchanges for the study area as groundwater pumping influences the rate of R-A exchanges and alters the flow dynamics. The developed models were coupled with optimization models and were executed to solve the multi-objective optimization problems based on the maximization of discharge through pumping wells and maximization of groundwater input into the river through R-A exchanges. The distinctive features of the paper include a pareto front comparison where fronts developed by different S-O models were compared and analysed based on various parameters. The results show the dominance of Multi-Objective Particle Swarm Optimization (MOPSO) over other optimization algorithms and concluded that the maximization of pumping rate significantly changes after considering the R-A exchanges-based objective functions. This study concludes that the model domain also alters the output of simulation–optimization. Therefore, model domain and corresponding boundary conditions should be selected carefully for the field application of management models. The artificial neural network (ANN) models have been also developed to deal with the computationally expensive simulation models by reducing the processing time and found efficient.

Incorporating water footprints and virtual water into crop redistribution provides a new approach for efficient water resources utilization and synergistic development of water surplus and scarce regions. In this work, the absolute and comparative advantage of the production‐based blue and gray water footprint (PWFblue and PWFgray), the calorie‐based blue water footprint (CWFblue) and the net benefit‐based blue water footprint (NBWFblue) were used as coefficients to establish a bi‐level crop redistribution model. The mode considers upper‐level decision makers interested in maximizing food security and ecological security and lower‐level decision makers interested in water use efficiency, water use benefits and net benefits. The model was applied in the Hetao Irrigation District (HID), China. The results showed that after optimization, the PWFblue, CWFblue, NBWFblue, and gray water footprint (GWF) of the HID were reduced by 23.32%, 5.60%, 17.40%, and 6.67%, respectively. National benefits were improved, especially when considering synergistic optimization, although the net benefits of HID was affected. The calorie supply increased by 9.6 × 109 kcal, the GWF decreased by 8.29 × 106 m3, and water use efficiency and benefits were improved in China. In contrast, the calorie supply and the net benefits of the HID decreased, while the GWF increased. Moreover, multiple stakeholders were involved in crop redistribution and required national synergies. The bi‐level model proved more suitable than the multi‐objective model. The model proposed in this work considers synergies outside the region in crop redistribution within the region, and can provide new insight for water and soil resources management in arid and semi‐arid regions. Key Points Virtual water flow embedded in optimization model reflecting comparative advantage Absolute advantage and comparative advantage synergize interregional interests Bi‐level optimization model trade‐offs regional authority and sub‐regions

In arid regions, irrigation scheduling optimization is efficient in coping with the shortage of agricultural water resources. This paper developed a simulation–optimization model for irrigation scheduling optimization for the main crop in an arid oasis, aiming to maximize crop yield and minimize crop water consumption. The model integrated the soil water balance simulation model and the optimization model for crop irrigation scheduling. The simulation model was firstly calibrated and validated based on field experiment data for maize in 2012 and 2013, respectively. Then, considering the distribution of soil types and irrigation districts in the study area, the model was used to solve the optimal irrigation schedules for the scenarios of status quo and typical climate years. The results indicated that the model is applicable for reflecting the complexities of simulation–optimization for maize irrigation scheduling. The optimization results showed that the irrigation water-saving potential of the study area was between 97 mm and 240 mm, and the average annual optimal yield of maize was over 7.3 t/ha. The simulation–optimization model of irrigation schedule established in this paper can provide a technical means for the formulation of irrigation schedules to ensure yield optimization and water productivity or water saving.

There is still a huge gap between the emissions pathways of megacities and the pathways to meeting the targets set by the Paris agreement. Compared with technological emission reductions, structural emission reduction can provide cities with more stable and sustainable carbon-peaking solutions. This study constructs a scenario-based input–output optimization model, adopting a novel carbon emission accounting method for purchased electricity that considers shared responsibility, and systematically evaluates the decarbonization paths of megacities and their impacts on economic growth, energy consumption, and carbon emissions. The results show that (a) through industry substitution and manufacturing restructuring, Shenzhen is projected to peak at 57.68 MtCO2 emissions in 2026, with a 10.57% energy and a 19.55% carbon reduction by 2030. (b) Shenzhen can achieve its carbon emission peak target through the energy transition while accepting a loss of 0.97%–3.23% of GDP, requiring the maximum economic concession of 16.45% from the transportation sector (S10) in the early stage of transformation, while 12.24% from the extractive industry (S2) in the later stage. (c) The comprehensive structure adjustment proved to be more effective than other mitigation approaches, capable of achieving high-quality economic growth of 6.4% during the study period while reaching a peak target of 53.55 million tons of CO2 by 2026. (d) The emission reduction effect of the power sector was the most significant among all the scenarios, with emission reduction rates between 6.26% and 35.63%, and the cumulative emission reduction potential reached 38.1–110.6 MtCO2. The priority for emission reduction in the power sector is the coal phase-out plan, which is essential for achieving these significant reductions. This study provides an important reference for megacities facing similar challenges, especially those in developing countries, to achieve a stable and sustainable carbon peak pathway through structural adjustment.

Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale fire outbreaks. This study aims to optimize IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM)—a processed-based model integrating biophysical and human impacts—to South Korea for projecting the pattern and scale of future forest fires. The developments performed in this study include: (1) the optimization of probability algorithms in FLAM based on the national GIS data downscaled to 1 km2 with additional factors introduced for national specific modeling; (2) the improvement of soil moisture computation by adjusting the Fine Fuel Moisture Code (FFMC) to represent vegetation feedbacks by fitting soil moisture to daily remote sensing data; and (3) projection of future forest fire frequency and burned area. Our results show that optimization has considerably improved the modeling of seasonal patterns of forest fire frequency. Pearson’s correlation coefficient between monthly predictions and observations from national statistics over 2016–2022 was improved from 0.171 in the non-optimized to 0.893 in the optimized FLAM. These findings imply that FLAM’s main algorithms for interpreting biophysical and human impacts on forest fire at a global scale are only applicable to South Korea after the optimization of all modules, and climate change is the main driver of the recent increases in forest fires. Projections for forest fire were produced for four periods until 2100 based on the forest management plan, which included three management scenarios (current, ideal, and overprotection). Ideal management led to a reduction of 60–70% of both fire frequency and burned area compared to the overprotection scenario. This study should be followed by research for developing adaptation strategies corresponding to the projected risks of future forest fires.