Planning of sewer systems typically involves limitations and problems, regardless of whether traditional planning methods or optimization models are used. Such problems include non-quantifiability, fuzzy objectives, a...
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Planning of sewer systems typically involves limitations and problems, regardless of whether traditional planning methods or optimization models are used. Such problems include non-quantifiability, fuzzy objectives, and uncertainties in decision-making variables which are commonly applied in the planning of any process. Particularly, uncertainties have prevented the inclusion of these variables in models. Consequently, the theoretical optional solution of the mathematical models is not the true optimum solution to practical problems. In this study, to solve the above problems for regional sewer system planning, multi-objective programming (MOP), nonlinear programming, mixed-integer programming, and compromise fuzzy programming were used. The objectives of this study were two-fold: (1) determination of the necessary decision-making variables or parameters, such as the optimum number of plants, piping layout, size of the plant, and extent of treatment;(2) establishment of a framework and methodology for optimal planning for designing a regional sewer system, matching demanded targets with the lowest cost, which would achieve the aim of lower space and energy requirements as well as consumption and high treatment efficiency for the purpose of meeting effluent standards. The findings of this study revealed that individual regional sewage treatment plants could be merged to form a centralized system. Land acquisition was difficult;thus, reducing the number of plants was required. Therefore, the compromise-fuzzy-based MOP method could effectively be used to build a regional sewer system plan, and the amount of in-plant establishment reached its maximized value with a minimized cost.
Intermittent sources of energy represent a challenge for electrical networks, particularly regarding demand satisfaction at peak times. Energy management tools such as load shaving or storage systems can be used to mi...
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Intermittent sources of energy represent a challenge for electrical networks, particularly regarding demand satisfaction at peak times. Energy management tools such as load shaving or storage systems can be used to mitigate intermittency. In this work, the value of different mechanisms to move energy through time is examined through a multi-objective programming approach, that aims at minimizing operating costs as well as carbon emissions. Among main achievements, we mention a sensitivity result that provides a novel three-dimensional Pareto front. The new tool complements the traditional curves of indifference costs with a quantitative measure for assessing the relative value of the conflicting objectives. The methodology is assessed on three instances representing typical configurations in Brazil, Germany and France, respectively corresponding to a system that is hydro-dominated, thermo-dominated, and with a balanced mix of hydro and thermal power. The corresponding Pareto fronts show that lowering carbon emissions can also reduce generation costs if the power mix is sufficiently diversified.
With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in spatial planning. Due to unreasonable location and regional s...
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With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in spatial planning. Due to unreasonable location and regional scale, the satisfaction of residents has been seriously affected. This paper develops a bi-level multi-objective programming (BLMOP) to optimize both facility location and size. Three major problems have been addressed: (1) solving the contradiction between supply and demand;(2) keeping a balance of social, economic, and environmental benefits;and (3) designing a multi-objective particle swarm optimization (MOPSO) algorithm by modifying the parameters and learning strategies. To obtain feasible solutions, a combination of optimistic and pessimistic approaches is adopted. Taking the rural areas of Southwest China as an example, the results find that the proposed model enables to provide objective-oriented optimization schemes depending on the decision-maker's (DM) preferences. Furthermore, the MOPSO algorithm can solve the BLMOP and provide Pareto-optimal solutions separately.
The focus of this paper is to suggest a solution methodology for a fully fuzzy multi-objective quadratic programming problem. Solving the provided mathematical model using known classical methods is extremely difficul...
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The focus of this paper is to suggest a solution methodology for a fully fuzzy multi-objective quadratic programming problem. Solving the provided mathematical model using known classical methods is extremely difficult. To solve the present mathematical programming problem, three major approaches are suggested. In first step, we used arithmetic operations between two fuzzy parameters and variables. The importance is given in the next step to handle fuzzy part of objective functions by ranking functions and after the completion of the second step, the fuzzy part of the fuzzy constraints tackled by the inequality property of between two triangular fuzzy numbers. Finally, the transformed multi-objective quadratic mathematical programming problem is solved using a weighted fuzzy goal programming approach. The final solution of the suggested model is derived using existing methodology and softwares. The working procedures of the proposed method is further discussed using numerical example.
This study introduces a multi-objective programming model for identifying a cropping pattern to evaluate the feasibility of increasing net profit, reducing water use, and diminishing the environmental impacts, simulta...
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This study introduces a multi-objective programming model for identifying a cropping pattern to evaluate the feasibility of increasing net profit, reducing water use, and diminishing the environmental impacts, simultaneously, under life cycle assessment (LCA). The research uses data collected in 2016-2017 through a survey in the east of the Lorestan Province of Iran. Results indicate that the multi-objective cropping pattern reduces environmental indicators, including water consumption by 1%, global warming potential by 14%, and nonrenewable energy use by 14%, with no change in farms' net profit compared to the current pattern in the region. The findings reveal that a designed cropping pattern under the constraints and objectives of LCA not only minimizes the environmental impacts, but also considers the stability of the benefits in the long term. However, the currently applied cropping pattern by farmers only focuses on achieving short-term profit-oriented goals. A new approach to land allocation is necessary to produce crops with a reduction in water consumption, nonrenewable energy use, and greenhouse gas emissions in the region. In this regard, it is essential to consider the policies that reduce available water and non-renewable resources into government decisions. On the other hand, policy incentives or disincentives, developing support packages of crop pricing, insurance and facilities support to prevent the cultivation of crops with high water demand and fertilizer are also essential. This proposed planning model should be used as the foundation for long-term cropping pattern planning policies in other irrigated and rainfed farming systems around the world.
In today's era of fragmented information,good data management is essential for *** data integration and analysis(D&A) is crucial to unleashing the full potential of data *** evaluation was conducted on Interco...
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In today's era of fragmented information,good data management is essential for *** data integration and analysis(D&A) is crucial to unleashing the full potential of data *** evaluation was conducted on Intercontinental Freight Company's(ICM) D&A system,with three key assessments identified:importance,connectivity,and availability *** multi-level fuzzy evaluation metrics,key points that affect the system's development were *** regression lines were used to analyze relationships between people,process,and *** performance was measured via average growth in business profits,and an index of 11 variables was created to measure the system's maturity.A growth phase distribution model was developed utilizing a support vector machine and tested on a sample of 50 US *** optimization model,based on multi-purpose functions,maximizes efficiency and can be seen by comparing factors before and after port *** experiment proves the effectiveness of the D&A system development evaluation model.
Aiming at the problem of resource allocation, this paper establishes a regional water resource allocation model based on a multi-objective programming algorithm. Specifically, we establish a linear programming model t...
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Aiming at the problem of resource allocation, this paper establishes a regional water resource allocation model based on a multi-objective programming algorithm. Specifically, we establish a linear programming model to identify two reservoirs with optimal water supply quantity and optimal power generation volume as objective functions. In addition, we establish a multi-objective programming model and determined the functional expressions of the two objectives of social benefit and economic benefit. The entropy weight method is used to determine the weights of the four major indicators of industry, agriculture, housing and electricity. Finally, we use MATLAB to solve the corresponding water demand, and use SPSS software to analyze the correlation between water, electricity supply and water demand, and obtain the influence of different variables in the model.
In this research, we use the harmonic mean technique to present an interactive strategy for addressing neutrosophic multi-level multi-objective linear programming (NMMLP) problems. The coefficients of the objective fu...
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In this research, we use the harmonic mean technique to present an interactive strategy for addressing neutrosophic multi-level multi-objective linear programming (NMMLP) problems. The coefficients of the objective functions of level decision makers and constraints are represented by neutrosophic numbers. By using the interval programming technique, the NMMLP problem is transformed into two crisp MMLP problems, one of these problems is an MMLP problem with all of its coefficients being upper approximations of neutrosophic numbers, while the other is an MMLP problem with all of its coefficients being lower approximations of neutrosophic numbers. The harmonic mean method is then used to combine the many objectives of each crisp problem into a single objective. Then, a preferred solution for NMMLP problems is obtained by solving the single-objective linear programming problem. An application of our research problem is how to determine the optimality the cost of multi-objective transportation problem with neutrosophic environment. To demonstrate the proposed strategies, numerical examples are solved.
Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper ...
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Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper takes the decision-making objectives as fuzzy events and aims to solve numerical multi-objective programming problems under random environment. We first analyze the effects of randomness on multi-objective decision-making results. With the expectation value and the probability of fuzzy events as quantitative index of randomness, we then establish a two-stage random multi-objective programming model based on reliability (i.e., TS-MOPM). Specifically, we give several probability calculation methods of fuzzy events with common distributions, and further present the corresponding calculation procedures for solving TS-MOPM. Finally, a case study is implemented to test the proposed model TS-MOPM. Theoretical analysis and case study indicate that our model has better interpretability and operability. The research results enrich the existing random multi-objective programming methods to some extent. (C) 2020 The Authors. Published by Atlantis Press SARL.
Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented epsilon-constraint a...
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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented epsilon-constraint algorithm, for solving multi-objective linear programming problems, by drawing from the weaknesses of AUGMECON 2, one of the most widely used improvements of the epsilon-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.
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