Optimising end-of-life system dismantling strategy

Abstract

The management of end-of-life systems is becoming a major concern for systems manufacturers as the negative impact of these systems on the environment is a matter of increasing public awareness and their appropriate treatment offers economic opportunities. In this context, the disassembly of these systems in order to recycle their components is a possible and sound option that can make it possible to sustain economical progress while respecting environment requirements. The work undertaken in this paper considers modelling and optimizing issues of such disassembly activities. An integrated approach is proposed to model and optimize the selection of valuable components of end-of-life systems, their recycling options and the way to obtain them. Because the framework of such problems is highly uncertain, we propose the use of Bayesian networks and their extension in terms of influence diagrams as mathematical tools for structuring and managing uncertainties. This approach allows taking into account uncertainties rising from different sources on one hand and as a support for optimization on the other hand.

A decision tool for disassembly process planning under end-of-life product quality

“…In [23], the sequence dependent disassembly process planning is studied along with the disassembly line balancing problem. [24] and [25] studied the problem of disassembly process planning under uncertainty of product parts quality. The quality of a product part is considered as a discrete state.…”

“…In [24], two quality classes are considered for each product part: repairable or worn out. In [25], several quality classes are defined as degradation levels. In addition, the revenue generated from each state, in both papers, is a predetermined constant value.…”

Disposal and Recycle Economic Assessment for Aircraft and Engine End of Life Solution Evaluation

“…Moreover, methods are provided to generate appropriate EoL solutions. The following techniques are often used for strategy selection: alternative strategy modeling [16,28], network flow simulation [29], cost-benefit optimization [15], complex system modeling [28], reverse logistics analysis [17,30], and sustainability considering economic, social, and environmental balances [16,28,31].…”

Design freeze sequencing using Bayesian network framework

“…Since BN is a useful tool for modeling uncertainty, most of them use the formalism of BN in design decision making under uncertainty. For example, Matthieu et al (2012) uses BN in formulating optimal disassembly strategy considering both product architecture and quality uncertainty. Moullec et al (2013) and Shahan and Seepersad (2012) use the formalism of BN in representing probabilistic relationship between design parameters and predict the probability distribution of product performance.…”