Effective Age of Remanufactured Products: An Entropy Approach

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Vijitashwa Pandey ◽  
Deborah Thurston

Product take-back and remanufacturing systems are difficult to implement cost effectively. One contributing factor is the complex nature of the inter-relationships among components of a product. Modeling of these relationships helps determine the product’s overall performance as a function of the performances of individual components. Reliability, a commonly used measure of performance, is a good measure of the physical failure rate, but it does not always reflect value degradation as experienced by customers or experts. As a result, it is difficult to define the effective performance of remanufactured products when some components are reused while others are not. Legislated take-back mandates across the world increasingly make it necessary to understand this perceived performance. In this paper we propose a method for combining customers’/experts’ assessments of value degradation using the maximum entropy principle. This value degradation information is then coupled with the components’ failure rate information. A method for modeling performance of a product that is comprised of components of different ages is presented. Overall performance is measured in units of time (effective age) by aligning with that of a product that has never been disassembled. We demonstrate the approach using a personal computer as example.

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.


Author(s):  
KAI YAO ◽  
JINWU GAO ◽  
WEI DAI

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.


Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.


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