The purpose of the current study is to select suppliers and determine their order allocation in a way that the performance of the sustainability of the supply process gets optimized on the whole. In this research, aft...
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The purpose of the current study is to select suppliers and determine their order allocation in a way that the performance of the sustainability of the supply process gets optimized on the whole. In this research, after reviewing the literature and investigating the supply chain of the case study (Iran Khodro's supply chain) through Delphi method, a set of evaluation criteria related to the performance of the suppliers in economic, social, and environmental terms was identified. In the next stage, by using the identified criteria, the multi-objective mathematical integer programming was presented to solve the problems of supplier selection and order allocation. The suggested mathematical programming in this research is designed to be multi-product, single-period, and multiple sourcing. Fuzzy TOPSIS method is applied to calculate the qualitative parameters that are used in the suggested mathematical programming. Ultimately, the mathematical model suggested in the research is solved by two methods, i.e., epsilon constraint method and weighted sum method. Moreover, the Total Value of Sustainable Purchasing (TVSP) is calculated for both cases. The comparison of these two methods indicates that, in this research, the results of the weighted sum method are more efficient than those of the epsilon constraint method. (C) 2019 Sharif University of Technology. All rights reserved.
In this paper, isolated efficient solutions of a given nonsmooth multi-objective Semi-Infinite programming problem (MOSIP) are studied. Two new Data Qualifications (DQs) are introduced and it is shown that these DQs a...
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In this paper, isolated efficient solutions of a given nonsmooth multi-objective Semi-Infinite programming problem (MOSIP) are studied. Two new Data Qualifications (DQs) are introduced and it is shown that these DQs are, to a large extent, weaker than already known Constraint Qualifications (CQs). The relationships between isolated efficiency and some relevant notions existing in the literature, including robustness, are established. Various necessary and sufficient conditions for characterizing isolated efficient solutions of a general problem are derived. It is done invoking the tangent cones, the normal cones, the generalized directional derivatives, and some gap functions. Using these characterizations, the (strongly) perturbed Karush-Kuhn-Tucker (KKT) optimality conditions for MOSIP are analyzed. Furthermore, it is shown that each isolated efficient solution is a Geoffrion properly efficient solution under appropriate assumptions. Moreover, Kuhn-Tucker (KT) and Klinger properly efficient solutions for a nonsmooth MOSIP are defined and it is proved that each isolated efficient solution is a KT properly efficient solution in general, and a Klinger properly efficient solution under a DQ. Finally, in the last section, the largest isolated efficiency constant for a given isolated efficient solution is determined.
multi-objective programming, as an effective mathematical model for dealing with multiple objectives, is widely used in the fields of complex system optimization, management decision analysis and artificial intelligen...
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ISBN:
(纸本)9781538669655
multi-objective programming, as an effective mathematical model for dealing with multiple objectives, is widely used in the fields of complex system optimization, management decision analysis and artificial intelligence. This paper first analyzes the characteristics and deficiencies of the multi-objective planning model based on priority factors, and proposes a multi-objective planning method based on relative deviations. It mainly introduces the relative deviation to describe the degree of deviation from the target value and reflect the satisfactory state of the decision results. It establishes a planning model in which each objective is at the same level, and analyzes the effectiveness of the method in conjunction with a specific case. The comprehensive analysis and the corresponding conclusions show that the solution model established not only ensures the existence of the solution, but also guarantees the reliability of the decision-making result. It has strong explanatory and operability, and to a certain extent, enriches the existing fuzzy decision-making method.
Fuzzy rough bi-level multi-objective nonlinear programming problem (FRBMNPP) moved toward becoming rise normally in various real applications. In this article we develop bi-level multi-objective nonlinear programming ...
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Fuzzy rough bi-level multi-objective nonlinear programming problem (FRBMNPP) moved toward becoming rise normally in various real applications. In this article we develop bi-level multi-objective nonlinear programming problem (BMNPP), in which the objective functions have fuzzy nature and the constraints represented as a rough set. The fuzzy objective functions converted into deterministic ones by utilizing the alpha-cut methodology. Thus the FRBMNPP become a rough BMNPP which is transformed into two problems corresponding to the upper and lower approximation models. The Karush-Kuhn-Tucker (KKT) method and two models of technique of order preferences by similarity to ideal solution (TOPSIS) approach are developed to solve such problem. At last, applicability and efficiency of the two TOPSIS models and KKT method, suggested in this study, are presented through an algorithm and a numerical illustration. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.
The theme park industry has grown intensely in recent times and offer every day a greater number of attractions and activities to visit. The problem is that, when a tourist visits a theme park, he has a short space of...
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The theme park industry has grown intensely in recent times and offer every day a greater number of attractions and activities to visit. The problem is that, when a tourist visits a theme park, he has a short space of time and he wants to maximize the usefulness of the visit, such as enjoying as many attractions as possible. Visitors face a serious selection problem - they must identify the best alternatives so as to optimally utilize their time and visit to the attractions that most interest them, besides keeping a check on the money spent during the visit. It would be very useful if they had a tool to help them take an optimum decision from among the different alternatives, considering their goals, restrictions and preferences. Therefore, we are going to develop a tool that, through a multi-objective model, helps theme park visitors who want to obtain an ideal route, helping them choose among the various alternatives: what activities to pursue, in what order, at what time, etc. The system can be used from home through a web page or application, or when present in the park, which also allows the tourist to incorporate the suggestions that arise in the park on the fly. This tool could improve the satisfaction of tourists visiting the park as it offers the maximum utility with regard to the activities undertaken within the timeframe available to them, thus also rendering added value to the theme park. One of the challenges facing theme parks management is the management of visitor flow, characterized by the dual objective of ensuring the highest quality experience for the tourists and reducing the risks arising from congestion of the different areas and/or most visited attractions, and we consider that this tool can help them to do it.
In financial investment, risk and benefit coexist. How to balance the benefits and risks and find the optimal investment portfolio is a key issue to be considered by investors. In this research, BP neural network is u...
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In financial investment, risk and benefit coexist. How to balance the benefits and risks and find the optimal investment portfolio is a key issue to be considered by investors. In this research, BP neural network is used to predict the future return on equity(ROE) of asset;a multiobjectiveprogramming model of investment portfolio is established on the basis of Markowitz's portfolio investment theory;to select the optimal investment portfolio, a comparison is made on the benefit-risk ratio of the investment portfolio with the smallest risk and at different income levels;in addition, an empirical analysis is made on the basis of the quarterly ROE data of 5 stocks during 2002-2017.
The design of a portfolio investment strategy for multiple venture capital and a risk-free asset in the market needs to consider two objectives: the overall return is as large as possible and the overall risk is as sm...
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The design of a portfolio investment strategy for multiple venture capital and a risk-free asset in the market needs to consider two objectives: the overall return is as large as possible and the overall risk is as small as possible. However, the two objectives are not mutually reinforcing. In a certain sense, they are opposite. In this paper, the multi-objective decision-making method is used to establish the model, and the investment benefit is the goal. The optimization model is established for the investment problem. Different investment methods have different risks and benefits. According to the principle of the optimization model, the model proposes two criteria, so that the economic benefits are as large as possible and the risks are as small as possible under certain conditions of investment. At the same time, a linear programming model for portfolio investment scheme design is given. The main idea is to integrate two design goals through linear weighting: assuming that the transaction fee function is approximately linearized on the basis of considerable investment scale, and the risk function is solved by decision variables.
Subway last train timetabling focuses more on train coordination to make passengers transfer smoothly. Aiming to find an optimal balanced timetabling, a multi-objectives programming model is proposed with speed profil...
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ISBN:
(纸本)9789811079894;9789811079887
Subway last train timetabling focuses more on train coordination to make passengers transfer smoothly. Aiming to find an optimal balanced timetabling, a multi-objectives programming model is proposed with speed profile control for finding the optimal train velocity on sections and train dwell time at stations. Our work makes two contributions. First, this paper analyzes train movements in a line and provides an energy flow transferring method. Second, based on the analysis of train velocity and energy flow, an optimization model, in which passenger satisfaction, train trip time and network energy consumption are model objectives, is brought into the last train timetabling. Finally, to obtain an approximately optimal scheduling strategy, an artificial intelligence algorithm is proposed in particular to effectively solve the proposed model. The results of numerical experiments demonstrate the efficiency and effectiveness of the proposed method.
In this paper, a new generalised class of higher order (F, alpha, rho, d)-V-type I function is introduced for a non-smooth multi-objective fractional programming problem involving support functions. The newly defined ...
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In this paper, a new generalised class of higher order (F, alpha, rho, d)-V-type I function is introduced for a non-smooth multi-objective fractional programming problem involving support functions. The newly defined class extends several known classes in the literature has been justified through a non-trivial example. In the framework of new concept, we determine conditions under which a fractional function becomes higher order (F, alpha, rho, d)-V-type I function and do some computational work to substantiate the analysis. Further, we establish Karush-Kuhn-Tucker type sufficient optimality conditions and derive various duality results for higher order Mond-Weir type and Schaible type dual programs.
Economic restructuring, energy planning and environmental protection are subject to inherent uncertainties in a compound system with competing decision objectives. Therefore, an inexact multiobjectiveprogramming mod...
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Economic restructuring, energy planning and environmental protection are subject to inherent uncertainties in a compound system with competing decision objectives. Therefore, an inexact multiobjectiveprogramming model for regional economy-energy-environment system management has been developed to obtain absolutely "optimal" solutions. Under two comparative scenarios, three subsystems, six industries, four types of energy, and three kinds of air pollution were considered in an optimization model, and a net system benefit and trade-off analysis between subsystems was conducted. The methods of interval-parameter programming and multi-objective programming were incorporated into the model to tackle the uncertainties and complexities reflected in the case study. The model results indicated that the developed model could provide effective linkages among the economy-energy environment systems and offer decision makers great insight into the reliability tradeoffs for the adjustment of the existing management policy. (C) 2017 Elsevier Ltd. All rights reserved.
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