This paper deals with a recent approach that tackles the product and the supply chain design issues at the same time and focuses more precisely on the supplier selection problem. The product specificities and the cons...
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ISBN:
(纸本)9781467385718
This paper deals with a recent approach that tackles the product and the supply chain design issues at the same time and focuses more precisely on the supplier selection problem. The product specificities and the constraints of suppliers are both integrated into product design phase. Usually, the supplier selection problem involves multiple and conflicting objectives. In this work, a multiobjective model is formulated to minimize supplying and holding costs of product components and quality rejected items. We study the case of an existent product redesign and we address the problem of supplier selection. Design team proposes several alternatives to improve the product and the aim is to select the best product design with its optimal set of components' suppliers. Thus, a multi-objective programming model for supplier selection is developed. Picking a set of Pareto front for multi-objective optimization problems require robust and efficient methods that can search an entire space. To solve the model, we used a non dominant sorting genetic algorithm (NSGA-II). A numerical experiment is provided at the end to show the applicability of the methodology and to compare it with the simple weighed sum method.
In this paper, we propose and compare single- and multi-objective programming ( MOP) approaches to the language model (LM) adaptation that require the optimization of a number of competing objectives. In LM adaptation...
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In this paper, we propose and compare single- and multi-objective programming ( MOP) approaches to the language model (LM) adaptation that require the optimization of a number of competing objectives. In LM adaptation, an adapted LM is found so that it is as close as possible to two independently trained LMs. The LM adaptation approach developed in this paper is based on reformulating the training objective of a maximum a posteriori ( MAP) method as an MOP problem. We extract the individual at least partially conflicting objective functions, which yields a problem with four objectives for a bigram LM: The first two objectives are concerned with the best fit to the adaptation data while the remaining two objectives are concerned with the best prior information obtained from a general domain corpus. Solving this problem in an iterative manner such that each objective is optimized one after another with constraints on the rest, we obtain a target LM that is a log-linear interpolation of the component LMs. The LM weights are found such that all the ( at least partially conflicting) objectives are optimized simultaneously. We compare the performance of the SOP- and MOP-based solutions. Our experimental results demonstrate that the ICO method achieves a better balance among the design objectives. Furthermore, the ICO method gives an improved system performance.
Most of the real world decision making problems involve uncertainty, which arise due to incomplete information or linguistic information on data. Stochastic programming and fuzzy programming are two powerful technique...
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Most of the real world decision making problems involve uncertainty, which arise due to incomplete information or linguistic information on data. Stochastic programming and fuzzy programming are two powerful techniques to solve such type of problems. Fuzzy stochastic programming is concerned with optimization problems in which some or all parameters are treated as fuzzy random variables in order to capture randomness and fuzziness under one roof. A method for solving multi-objective fuzzy probabilistic programming problem is proposed in this paper. The uncertain parameters are considered as fuzzy log-normal random variables. Since the existing methods are not enough to solve fuzzy probabilistic programming problem directly, therefore the mathematical programming model is transformed to an equivalent multi-objective crisp model. Finally, a fuzzy programming technique is used to solve the multi-objective crisp model. The resulting model is then solved by standard non-linear programming methods. In order to illustrate the methodology a numerical example is provided.
A simple augmented epsilon-constraint (SAUGMECON) method is put forward to generate all non-dominated solutions of multi-objective integer programming (MOIP) problems. The SAUGMECON method is a variant of the augmente...
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A simple augmented epsilon-constraint (SAUGMECON) method is put forward to generate all non-dominated solutions of multi-objective integer programming (MOIP) problems. The SAUGMECON method is a variant of the augmented epsilon-constraint (AUGMECON) method proposed in 2009 and improved in 2013 by Mavrotas et al. However, with the SAUGMECON method, all non-dominated solutions can be found much more efficiently thanks to our innovations to algorithm acceleration. These innovative acceleration mechanisms include: (1) an extension to the acceleration algorithm with early exit and (2) an addition of an acceleration algorithm with bouncing steps. The same numerical example in Lokman and Koksalan (2012) is used to illustrate workings of the method. Then comparisons of computational performance among the method proposed by (Ozlen and Azizoglu (2009), Ozlen et al. (2012), the method developed by Lokman and Koksalan (2012) and the SAUGMECON method are made by solving randomly generated general MOIP problem instances as well as special MOIP problem instances such as the MOKP and MOSP problem instances presented in Table 4 in Lolcman and Koksalan (2012). The experimental results show that the SAUGMECON method performs the best among these methods. More importantly, the advantage of the SAUGMECON method over the method proposed by Lokman and Koksalan (2012) turns out to be increasingly more prominent as the number of objectives increases. (C) 2013 Elsevier B.V. All rights reserved.
The capacitated multi-facility location problem is a complex and imprecise decision-making problem which contains both quantitative and qualitative factors. In the literature, many objectives for optimizing many types...
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The capacitated multi-facility location problem is a complex and imprecise decision-making problem which contains both quantitative and qualitative factors. In the literature, many objectives for optimizing many types of logistics networks are described: (i) minimization objectives such as cost, inventory, transportation time, environmental impact, financial risk and (ii) maximization objectives such as profit, customer satisfaction, and flexibility and robustness. However, only a few papers have considered quantitative and qualitative factors together with imprecise methodologies. Unlike traditional cost-based optimization techniques, the approach proposed here evaluates these factors together while considering various viewpoints. Decision-makers must deal both factors together to model complex structure of real-world applications. In this paper, a two-phase possibilistic linear programming approach and a fuzzy analytical hierarchical process approach have been combined to optimize two objective functions ("minimum cost" and "maximum qualitative factors benefit") in a four-stage (suppliers, plants, distribution centers, customers) supply chain network in the presence of vagueness. The results and findings of this method are illustrated with a numerical example, and the advantages of this methodology are discussed in the conclusion. (C) 2014 Elsevier Inc. All rights reserved.
In this paper, a hybrid artificial intelligent approach based on the clonal selection principle of artificial immune system (AIS) and neural networks is proposed to solve multi-objective programming problems. Due to t...
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In this paper, a hybrid artificial intelligent approach based on the clonal selection principle of artificial immune system (AIS) and neural networks is proposed to solve multi-objective programming problems. Due to the sensitivity to the initial values of initial population of antibodies (Ab's), neural networks is used to initialize the boundary of the antibodies for AIS to guarantee that all the initial population of Ab's is feasible. The proposed approach uses dominance principle and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the "not so good" antibodies. A secondary (or external) population that stores the nondominated solutions found along the search process is used. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the Pareto front. (C) 2010 Faculty of Computers and Information, Cairo University. Production and hosting by Elsevier B.V. All rights reserved.
Energy is one of the most basic elements for raising social welfare, playing a fascinating role in economic and social progress of the countries and thus increasing the competitiveness of the countries in globalized w...
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Energy is one of the most basic elements for raising social welfare, playing a fascinating role in economic and social progress of the countries and thus increasing the competitiveness of the countries in globalized world. Therefore, carrying out the sustainable energy policies which are based on social, economic and environmental factors and in this context, finding the local, sustainable, environmentally-friendly and economic resources and the optimal distribution of them have become a necessity in order to achieve the sustainable development thrusts. In this study, a multi-objective mixed integer linear programming (MOMILP) model which reflects the Turkey's realities and necessities and optimizes simultaneously the objectives of total cost minimization, CO2 emission minimization, energy import minimization, fossil resource usage minimization, employment maximization and social acceptance maximization is proposed. This model is solved by Minimum Deviation Method (MDM) considering the most basic energy resources (solar, wind, coal, natural gas, hydroelectric, nuclear etc.) used for the electricity generation all over the world and a 11-years electricity generation plan is obtained on the basis of resources for Turkey.
Under the background of frequent occurrence of extreme drought as well as increasing attention to water shortage, regional water right distribution has become a vital approach for local government to advocate water ri...
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ISBN:
(纸本)9781424481194
Under the background of frequent occurrence of extreme drought as well as increasing attention to water shortage, regional water right distribution has become a vital approach for local government to advocate water right and manage water resource. The connotation along with subject and object of regional water right are investigated in this article. The manifestations of regional water right include life water right, ecological water right and productive water right. multi-objective programming model of regional water right distribution in the fields of life, ecology and production is established taking social, ecological and economic targets into account. In accordance with the principle that basic water right superiors than public water right and competitive water right, the distribution priority is confirmed by questionnaires design and AHP method. Finally, empirical research is conducted by data from Ningxia and results are obtained under the condition of objects equilibrium. 0.3188 billion m(3) water should be distributed to life activities and 0.1109 billion m(3) water should be distributed to ecological activities, 7.3331 billion m(3) to production activities according to the abovementioned empirical research.
In this study, we examine an integrated problem involving fleet replacement and pilot training scheduling for airborne service helicopters. Two objectives, minimizing the total cost and the number of batches of pilot ...
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In this study, we examine an integrated problem involving fleet replacement and pilot training scheduling for airborne service helicopters. Two objectives, minimizing the total cost and the number of batches of pilot training, are considered based on the practical requirements. multi-objective programming combined with a time-space network technique is applied to solve the problem. Numerical tests, in reference to the operations of a Taiwanese organization, are carried out to demonstrate the performance of the proposed model. A comparison of the proposed model and two single-objective models corresponding to the two objectives is also presented.
This paper estimates the macroeconornic costs of CO2 emission reduction in China employing the input-output analysis with the multi-objective programming approach. The results show that the effect of reducing CO2 emis...
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This paper estimates the macroeconornic costs of CO2 emission reduction in China employing the input-output analysis with the multi-objective programming approach. The results show that the effect of reducing CO2 emissions on China＇s economy is significant. Under the present conditions, the estimated macroeconomic costs of CO2 emission reduction in 2010 for China are approximately 3,100-4,024 RMB t-1. The stronger the abatement actions, the higher the macroeconomic costs of per unit emission reduction would be. Excavation industry, oil industry, chemical industry, and metal smelting industry have high potential to abate their CO2 emissions.
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