A grey genetic algorithm for uncertainty reverse logistics
23 May 2012-pp 885-892
TL;DR: This study presents the uncertainty remanufacturing demand (URD) for green suitcase chain to predict the return demand model of an flexible inventory model to improve the effectiveness of extended producer responsibility for green supply chain management.
Abstract: This study presents the uncertainty remanufacturing demand (URD) for green suitcase chain to predict the return demand model of an flexible inventory model. In this research, we proposed reverse production design by green prediction model for the division of green cost responsibility. This philosophy have become a popular topic that improved the effectiveness of extended producer responsibility for green supply chain management (GSCM). It is different from the traditional division of green cost responsibility processes that we proposed a novel measurement by the uncertainty index from green prediction model evolutions. A grey genetic algorithm (GGA) was designed by adaptive designs for URD optimization. These designs provided a novel evaluation index by varying all variables to achieve the global optimization of green cost responsibility. The new demand prediction design derived from the crossover and mutation rate of an adjusted GA search optimization. This research verified these methodologies in a practical case. The experiment is simulated by a GGA to reach an optimal solution.
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TL;DR: The previously published papers on reverse logistics are reviewed and classified on the basis of the meta-heuristic approaches adopted and the problem context of the reverse supply chain to discuss the power and flexibility of these methods for solving a set of RL problems.
35 citations
TL;DR: This research summarizes the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics and discusses the potential future research trends.
Abstract: In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.
8 citations
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TL;DR: A classification of different approaches based on a number of complementary features is provided, and special attention is paid to setting parameters on-the-fly, which has the potential of adjusting the algorithm to the problem while solving the problem.
Abstract: The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters. © Springer-Verlag Berlin Heidelberg 2007.
1,307 citations
TL;DR: The purpose of this paper is to introduce the area of Green Logistics and to describe some of the problems that arise in this subject which can be formulated as combinatorial optimization problems.
Abstract: The purpose of this paper is to introduce the area of Green Logistics and to describe some of the problems that arise in this subject which can be formulated as combinatorial optimization problems. The paper particularly considers the topics of reverse logistics, waste management and vehicle routing and scheduling.
266 citations
"A grey genetic algorithm for uncert..." refers background or methods in this paper
...Many researchers introduced the new concepts of green logistics, and classified the situations raised in the prediction model efficiencies of green improvements [21]....
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...GGA was applied to select green prediction models in URD management, however, the curr ent situations are very difficult to match the environmental regulations of WEEE and RoHS [21] [22]....
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Posted Content•
TL;DR: In this paper, the authors introduce the area of green logistics and describe some of the problems that arise in this subject which can be formulated as combinatorial optimization problems, particularly considering the topics of reverse logistics, waste management and vehicle routing and scheduling.
Abstract: The purpose of this paper is to introduce the area of Green Logistics and to describe some of the problems that arise in this subject which can be formulated as combinatorial optimization problems. The paper particularly considers the topics of reverse logistics, waste management and vehicle routing and scheduling.
240 citations
TL;DR: An improved method to forecast the output and trends of high technology industry in Taiwan is presented, using the combined use of grey theory and genetic algorithms.
156 citations
"A grey genetic algorithm for uncert..." refers background in this paper
...The exploitation is able to be prompted by selected suitable pressure components and their parameters such as population size, crossover rate and mutation rate [10] [11] [25]....
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...X(l�O) (n)) Procedure 3: Presented the background model (AGO) = (I I T �I X (l�O)( n ) , I n T �I X ( I �O)( n» ) [25]....
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01 Apr 2007
TL;DR: This paper proposes a multi-objective genetic programming approach to developing such alternative Pareto optimal decision trees and allows the decision maker to specify partial preferences on the conflicting objectives to further reduce the number of alternative solutions.
Abstract: Classification is a frequently encountered data mining problem. Decision tree techniques have been widely used to build classification models as such models closely resemble human reasoning and are easy to understand. Many real-world classification problems are cost-sensitive, meaning that different types of misclassification errors are not equally costly. Since different decision trees may excel under different cost settings, a set of non-dominated decision trees should be developed and presented to the decision maker for consideration, if the costs of different types of misclassification errors are not precisely determined. This paper proposes a multi-objective genetic programming approach to developing such alternative Pareto optimal decision trees. It also allows the decision maker to specify partial preferences on the conflicting objectives, such as false negative vs. false positive, sensitivity vs. specificity, and recall vs. precision, to further reduce the number of alternative solutions. A diabetes prediction problem and a credit card application approval problem are used to illustrate the application of the proposed approach.
138 citations