Supply chain network design is one of the most important strategic decisions that need to be optimized for long-term efficiency. Critical decisions include facility location, inventory, and transportation issues. This...
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Supply chain network design is one of the most important strategic decisions that need to be optimized for long-term efficiency. Critical decisions include facility location, inventory, and transportation issues. This study proposes that with a dual-channel supply chain network design model, the traditional location-inventory problem should be extended to consider the vast amount of online customers at the strategic level, since the problem usually involves multiple and conflicting objectives. Therefore, a multi-objective dual-channel supply chain network model involving three conflicting objectives is initially proposed to allow a comprehensive trade-off evaluation. In addition to the typical costs associated with facility operation and transportation, we explicitly consider the pivotal online customer service rate between the distribution centers (DCs) and their assigned customers. This study proposes a heuristic solution scheme to resolve this multi-objective programming problem, by integrating genetic algorithms, a clustering analysis, a Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach. A scenario analysis is conducted to understand the model's performance.
Water companies traditionally deal with the problem of rehabilitation and redesign of municipal water supply system due to the either of aging the existed water supply network and housing development. However, such pr...
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Water companies traditionally deal with the problem of rehabilitation and redesign of municipal water supply system due to the either of aging the existed water supply network and housing development. However, such problems are hemmed in by uncertainty due to their long planning horizons and the fact that the exact prediction of future is impossible. Uncertain data and parameters which are likely to undermine the effectiveness of our decisions in this area, are exacerbated if they are be correlated. Unfortunately, correlated uncertain parameters are the things typically such problems are entangled with. Thus, along with considering all of decision objectives, it is incumbent upon water supply system redesign and rehabilitation decision model to develop methods able to appropriately confront with the correlated uncertainty. This paper introduces a bi-objective robust optimization model capable of handling correlated uncertain parameters in municipal water supply system redesign and rehabilitation problem. This robust optimization framework is able to adjust the level of conservatism, a factor which contributes to the reliability of the system. The proposed mathematical framework is applied for a water supply system inspired from Tehran potable water supply system and then various levels of conservatism and reliability are compared. Numerical results show neglecting uncertainty can lead to significant increase in the total cost and amount of unsatisfied demand.
Most traditional engineered systems are designed with a passive and fixed reliability capability and just required to achieve a possibly low level of failure occurrence. However, as the complexity at spatial-temporal ...
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Most traditional engineered systems are designed with a passive and fixed reliability capability and just required to achieve a possibly low level of failure occurrence. However, as the complexity at spatial-temporal scales and integrations increases, modern complex engineered systems (CESs) are facing new challenges of inherent risk and bottleneck for a successful and safe operation through the system life cycle when potential expected or unexpected disruptive events happen. As a prototype for ensuring the successful operation of inherently risky systems, resilience has demonstrated itself to be a promising concept to address the above-mentioned challenges. A standard multi-dimensional resilience triangle model is first presented based on the concept of the three-phase system resilience cycle, which can provide a theoretical foundation for indicating the utility objectives of resilience design. Then, the resilience design problem for CESs is proposed as a multi-objective optimization model, in which the three objectives are to maximize the survival probability, to maximize the reactive timeliness and to minimize the total budgeted cost. Furthermore, the proposed multi-objective optimization programming is solved based on the efficient multi-objective evolutionary algorithm NSGA-II. Finally, the effectiveness of the proposed models and solving procedure is illustrated with an engineered electro-hydrostatic aircraft control actuator resilience design problem, a comparative analysis on the case study is also carried out with respect to previous works. This work can provide an effective tradeoff foundation to improve the resilience of CESs.
In this paper, a class of generalized invex functions, called (a,.,.)-invex functions, is introduced, and some examples are presented to illustrate their existence. Then we consider the relationships of solutions betw...
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In this paper, a class of generalized invex functions, called (a,.,.)-invex functions, is introduced, and some examples are presented to illustrate their existence. Then we consider the relationships of solutions between two types of vector variational-like inequalities and multi-objective programming problem. Finally, the existence results for the discussed variational-like inequalities are proposed by using the KKM-Fan theorem.
This paper develops a multi-objective Mixed Integer programming model for a closed-loop network design problem. In addition to the overall costs, the model optimizes overall carbon emissions and the responsiveness of ...
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This paper develops a multi-objective Mixed Integer programming model for a closed-loop network design problem. In addition to the overall costs, the model optimizes overall carbon emissions and the responsiveness of the network. An improved genetic algorithm based on the framework of NSGA II is developed to solve the problem and obtain Pareto-optimal solutions. An example with 95 cities in China is presented to illustrate the approach. Through randomly generated examples with different sizes;the computational performance of the proposed algorithm is also compared with former genetic algorithms in the literature employing the weight-sum technique as a fitness evaluation strategy. Computational results indicate that the proposed algorithm can obtain superior Pareto-optimal solutions. (C) 2016 Elsevier Inc. All rights reserved.
In the present paper, a multi-objective goal optimization mechanism is developed by trading off between cost and variance. Both are adversaries to each other while allocating a sample size even in stratified sampling ...
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In the present paper, a multi-objective goal optimization mechanism is developed by trading off between cost and variance. Both are adversaries to each other while allocating a sample size even in stratified sampling design. Discussion section shows how these adversaries put their influence on optimal selection. This is a dual optimization procedure in which variance or mean square error is optimized in the first step and then considering some compromise on variance, cost is optimized. The process is applied to both individual and multi-objective programming models.
This paper investigates the distribution centre location problem with inaccurate information, and a general model based on a rough feasible region is established. By means of synthesizing the believable degree of the ...
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This paper investigates the distribution centre location problem with inaccurate information, and a general model based on a rough feasible region is established. By means of synthesizing the believable degree of the rough feasible region and objective functions, a solution model termed as rough multi-objective synthesis effect (RMOSE) model is developed;this constitutes a series of crisp multi-objective programming models that reflect different decision consciousness for each decision maker. The optimal solutions of the RMOSE model can be obtained by using the genetic algorithm, and it is demonstrated that the solution of the RMOSE model in proper parameters is same as that of existing model with fuzzy model information. So the proposed RMOSE model is actually an extension of a crisp multi-objective programming model. Two cases of experiments for the distribution centre location problems show that the proposed method can be directly applied to real world practices and it is better than existing methods with fuzzy model information.
This paper establishes the income and risk model in financial investment based on multi-objective programming theory, aiming to analyze the relationship between risk and return in financial investment and discuss the ...
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This paper establishes the income and risk model in financial investment based on multi-objective programming theory, aiming to analyze the relationship between risk and return in financial investment and discuss the relationship between the risk the investor shall bear and decentralization degree of investment project. MATLAB software is used to analyze the investor’s optimized return under fixed risk level and the minimized risk with defined benefit. In addition, it chooses the optimal portfolio under such risk level with respect to the bearing capacity of different risks. This paper performs sensitivity analysis of risk in income model using LINGO software, and puts forward the optimal portfolio for the investor without special preference. Calculations show that the model established is satisfactory in determining the optimal portfolio.
Recently, cloud services and computing have revolutionized both academic research and industrial practices. Numerous hardware and software providers have joined the intensely competitive cloud services market. At the ...
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Recently, cloud services and computing have revolutionized both academic research and industrial practices. Numerous hardware and software providers have joined the intensely competitive cloud services market. At the same time, a corresponding focus on evaluation of the industry is growing apace. Investigations are currently under way into how to incorporate the dynamic and intermediate processes in cloud service companies' business models. This paper intends to develop some alternative models of network data envelopment analysis (NDEA) for evaluating cloud service businesses. By considering various internal functions and processes of the services in multi-period settings, we design three evaluation models: (1) dynamic black-box data envelopment analysis (DBDEA);(2) static network data envelopment analysis (SNDEA);and (3) dynamic network data envelopment analysis (DNDEA). Using multi-objective programming (MOP) techniques, the three NDEA models are formulated and solved for the cloud service industry. An empirical study is conducted to evaluate the performance of the cloud service industry. (C) 2017 Elsevier Inc. All rights reserved.
Triangular Atanassov's intuitionistic fuzzy number (TAIFN) has better ability to model fuzzy ill-defined quantity. The information aggregation of TAIFNs is of great importance in multi-attribute group decision-mak...
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Triangular Atanassov's intuitionistic fuzzy number (TAIFN) has better ability to model fuzzy ill-defined quantity. The information aggregation of TAIFNs is of great importance in multi-attribute group decision-making (MAGDM). In this paper, some arithmetic aggregation operators for TAIFNs are defined, with the triangular Atanassov's intuitionistic fuzzy weighted average (TAIFWA) operator, ordered weighted average (TAIFOWA) operator and hybrid weighted average (TAIFHWA) operator included. Then we further investigate the Atanassov's triangular intuitionistic fuzzy generalized ordered weighted average (TAIFGOWA) operator and generalized hybrid weighted average (TAIFGHWA) operator. Some desirable and useful properties of these operators, such as idempotence, monotonicity and boundedness, are also discussed. For the MAGDM with TAIFNs and incomplete attribute weight information, a multi-objective programming model is constructed by minimizing total deviation between all alternatives and fuzzy positive ideal solution, which is transformed into a linear goal programming. Consequently, the attribute weights are objectively derived. Thereby, an innovated MAGDM method is proposed on the basis of the TAIFWA and TAIFGHWA operators. Finally, a green supplier selection example is provided to illuminate the practicability of the proposed method in this paper.
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