Balancing the tradeoff between economy, energy, environment, water resources and carbon emissions has become the major challenge for sustainable development. In this study, a fuzzy multi-objective method is developed ...
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Balancing the tradeoff between economy, energy, environment, water resources and carbon emissions has become the major challenge for sustainable development. In this study, a fuzzy multi-objective method is developed by integrating multi-objective programming, fuzzy linear programming and multiple scenarios. The developed approach can tackle multiple uncertainties and complexities existing in economy-energy-environment system, random carbon dioxide emission and water consumption control policies mix. Results disclose that (1) policy orientation and carbon emission control policy can achieve both carbon and water resource control goals, and water resource control policies are relatively unnecessary;(2) the tertiary sector will become the leading industry, accounting for more than three quarters of the total output value, the construction industry will also overgrow, and the proportion of the manufacturing industry will drop significantly;(3) coal still dominates in the energy production and total quantity consumed, and petroleum consumption will fall, coal accounts for about 75 percent of total energy consumption and electricity for about 15 percent;(4) according to different sources, carbon dioxide emissions are mainly from coal and electricity utilization. Coal's contribution gradually rose to 65 percent, while electricity dropped from 35 percent to 30 percent. For each industry, the mining industry, the electricity industry and the tertiary sector are the primary sources. (5) the sulfur dioxide control policy and carbon dioxide control policy has a strong correlation, and nitrogen oxide control policies have a specific correlation with them. The water resource control policy and particulate matter control policy are relatively independent. (C) 2022 Published by Elsevier Ltd.
. Tourists going out for a trip encounter various uncertainties, such as weather conditions, road conditions, the tourists' consumption budget, travel time, and other uncertainties. Tourists focus on the goals of ...
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. Tourists going out for a trip encounter various uncertainties, such as weather conditions, road conditions, the tourists' consumption budget, travel time, and other uncertainties. Tourists focus on the goals of minimizing travel time and consumption costs while maximizing personal satisfaction with the route;therefore, the multi-objective programming model for an uncertain tourism route problem is established based on uncertainty theory. The objectives of the model are to minimize the travel time and consumption cost and to maximize the tourists' satisfaction with the route. According to inverse uncertainty distribution, the model can be transformed into a traditional programming model and solved by the ant colony algorithm (ACO). Finally, in order to solve the uncertain tourism route programming problem, a numerical example is given to show the application of the model.
Interior-point methods, particularly the cutting-plane strategy, have emerged as interesting techniques in optimization. The main idea of the cutting-plane method is to cut off parts of the feasible set and shrink it ...
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Interior-point methods, particularly the cutting-plane strategy, have emerged as interesting techniques in optimization. The main idea of the cutting-plane method is to cut off parts of the feasible set and shrink it by removing the points where optimality is surely not attained. The aim of this study is to improve the convergence speed of a newly developed modified cutting-plane method for multi-objective optimization problems. The effectiveness of the proposed approach is emphasized by its ability rapidly to shrink the feasible space, strengthen the optimal solution search method, and at the same time minimize the computational overhead. Additionally, it is demonstrated that the method is capable of achieving well-distributed efficient solutions on the efficient frontier by purposefully assigning weight vectors. The experimental analysis carried out confirms the effectiveness of the suggested method and offers a new promising perspective for addressing multi-objective optimization challenges.
As the issue of global climate change becomes increasingly severe, governments worldwide have implemented carbon reduction policies, such as carbon taxes and industrial low-carbon transitions, to effectively control t...
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As the issue of global climate change becomes increasingly severe, governments worldwide have implemented carbon reduction policies, such as carbon taxes and industrial low-carbon transitions, to effectively control total carbon emissions. This study applies a multi-objective programming approach and uses the plastic raw material manufacturing process in the petrochemical industry as an example to explore how companies can balance profit maximization with minimizing production-related carbon emissions. By integrating Activity-Based Costing (ABC) and the Theory of Constraints (TOC), this study develops a production decision-making model and employs the epsilon-constraint method to impose carbon emission constraints, analyzing the resulting changes in corporate profitability. The model considers three different policy scenarios: basic carbon tax costs (including the use of renewable energy), continuous incremental progressive carbon tax costs, and discontinuous incremental progressive carbon tax costs. The results indicate that adopting renewable energy effectively reduces carbon emissions during production, while the discontinuous incremental carbon tax model provides better control over emissions. Under different carbon emission constraints, significant variations in optimal profits and production volumes are observed across the models, offering valuable insights for governments and enterprises in formulating carbon reduction strategies.
In this paper, the behavior of the solutions of a multi-objective optimization problem, whose the objective functions are perturbed by adding a small linear term, is analyzed. In this regard, a new notion of Lipschitz...
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In this paper, the behavior of the solutions of a multi-objective optimization problem, whose the objective functions are perturbed by adding a small linear term, is analyzed. In this regard, a new notion of Lipschitzian stability, by means of the Aubin property of the solution set, is defined. Lipschitz stable locally efficient solutions, as generalization of tilt/full stable solutions, are introduced and characterized by modern variational analysis tools. Applying the weighted sum method, the relationships between these solutions and full-stable local optimal solutions of the scalarized problem are investigated. The key tools in deriving our results come from the first- and second-order variational analysis.
This academic paper delves into the intricacies of dining out and dietary management, aiming to provide an effective solution to the multifaceted challenges faced by regular out-diners in Taiwan. The research centers ...
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This academic paper delves into the intricacies of dining out and dietary management, aiming to provide an effective solution to the multifaceted challenges faced by regular out-diners in Taiwan. The research centers on the development and application of an innovative hybrid model that combines the analytic hierarchy process (AHP) and multi-objective programming (MOP) to facilitate dietary planning, thereby accommodating individual preferences, expert insights, and the recommended daily intake levels for six categories of essential nutrients. The model is designed to enable a dynamic adjustment of preference coefficients when decision-makers provide their preferences, resulting in personalized dietary recommendations. However, it is noted that solutions adhering to predefined budget constraints may sometimes fall short of identifying entirely satisfactory dietary combinations. Furthermore, a significant challenge identified in this study pertains to the availability of food products at chain restaurants and stores. These products often exhibit deficiencies in essential nutrients while offering an excess of dietary energy. The research reveals that when individuals adhere to recommended dietary combinations, they can attain nutrient intake levels that closely approximate suggested values. In this study, the AHP-MOP model demonstrates enhanced stability and superior adherence to the principles of healthy dietary planning, ultimately yielding dietary combinations associated with a higher perceived value within the same budgetary constraints than the MOP-only model. With the regional limitation of model, the study underscores the potential for enhancing the model's practicality by expanding the product database, thereby contributing to the improved dietary well-being of regular out-diners.
Water shortages and supply pressures due to the special geographical environment pose significant challenges to the social and economic development of the Minjiang River Basin in Fujian Province, China. This study int...
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Water shortages and supply pressures due to the special geographical environment pose significant challenges to the social and economic development of the Minjiang River Basin in Fujian Province, China. This study introduced a semi-infinite interval type-2 fuzzy multi-objective programming (SIIT2F-MOP) model to optimize the water resource system under conditions of uncertainty. Additionally, a novel multi-criteria decision analysis (MCDA) method, integrating the interval-based technique for order of preference by similarity to ideal solution (ITOPSIS), was developed to analyze and evaluate water allocation results using interval data instead of specific values. The SIIT2F-MOP model combines semi-infinite programming, multi-objective optimization, and interval type-2 fuzzy sets to address uncertainties and resolve conflicts among multiple decision-makers in water resource management. The innovations and contributions of this study are as follows: (i) prioritizing the restructuring of secondary industries while advancing manufacturing and modern service sectors can promote economic development and mitigate water shortages, and net system benefits increased by 7.2 %;(ii) evaluation of water allocation options using the ITOPSIS method demonstrates that industrial benefits account for 53.8 % of the optimal scenario;and (iii) the proposed model facilitates dynamic analysis of economic efficiency, equity in water distribution, and water scarcity within the water resource system. By integrating advanced optimization and decision-making techniques, this study contributes to sustainable resource management and supports risk control in the face of growing water demand.
With the rapid development of shared bicycles in recent years, it not only facilitates urban residents, but also brings several problems to urban traffic, mainly in disorderly parking. In order to solve the disorderly...
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ISBN:
(纸本)9781510674479
With the rapid development of shared bicycles in recent years, it not only facilitates urban residents, but also brings several problems to urban traffic, mainly in disorderly parking. In order to solve the disorderly parking of shared bicycles, this paper takes the Yangguang Campus of Wuhan Textile University for example and considers the trip demand of teachers and students. We choose the cosine distribution time satisfactory function as the service quality level function to quantify the quality of service, which can reflect the distance between demanding points and parking sites to users' satisfaction. Then we establish a multi- objective integer planning location-selection model based on gradual coverage method, which aims at maximizing service quality and minimizing the number of parking sites. We use LINGO to calculate the model with actual survey data. The results show that the solution of the location-selection model is close to the reality and of certain practical significance.
Selective maintenance refers to the problem of decision-making on how to maintain a multi-component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likeliho...
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Selective maintenance refers to the problem of decision-making on how to maintain a multi-component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi-objective decision-making model for maximizing the system reliability of a multi-component systems. The objective functions represent the reliability of the sub-systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi-objective selective maintenance model, we have proposed four-valued neutrosophic technique. The proposed solution methodology combines the concept of four-valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi-component system of an aircraft gas turbine engine is studied.
Selection of appropriate suppliers and allocation the orders among them have become the two key strategic decisions regarding purchasing. In this study, a two-phase integrated approach is proposed for solving supplier...
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Selection of appropriate suppliers and allocation the orders among them have become the two key strategic decisions regarding purchasing. In this study, a two-phase integrated approach is proposed for solving supplier selection and order allocation problems. Phase 1 contains four techniques from statistics and Machine Learning (ML), including Auto-Regressive Integrated Moving Average, Random Forest, Gradient Boosting Regression, and Long Short-term Memory for forecasting the demands, using large amounts of real historical data. In Phase 2, suppliers' qualitative weights are determined by a fuzzy logic model. Then, a new multi-objective programming model is designed, considering multiple periods and products. In this phase, the results of Phase 1 and the results of the fuzzy model are utilized as inputs for the multi-objective model. The weighted-sum method is applied for solving the multi-objective model. The results show Random Forest model leads to more accurate predictions than the other examined models in this study. In addition, based on the results, the selection of the forecasting techniques and different weights of suppliers affect both supplier selection and the related orders.
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