Optimal and intelligent decision making in sustainable development of electronic products

Increasing global population and consumption are causing declining natural and social systems. Multi-lifecycle engineering and sustainable development address these issues by integrating strategies for economic successes, environmental quality, and social equity. Based on multi-lifecycle engineering and sustainable development concepts, this doctoral dissertation aims to provide decision making approaches to growing a strong industrial economy while maintaining a clean, healthy environment. The research develops a methodology to complete both the disassembly leveling and bin assignment decisions in demanufacturing through balancing the disassembly efforts, value returns, and environmental impacts. The proposed method is successfully implemented into a demanufacturing module of a Multi-LifeCycle Assessment and Analysis tool. The methodology is illustrated by a computer product example. Since products during the use stage may experience very different conditions, their external and internal status can vary significantly. These products, when coming to a demanufacturing facility, are often associated with incomplete/imprecise information, which complicates demanufacturing process decision making. In order to deal with uncertain information, this research proposes Fuzzy Reasoning Petri nets to model and reason knowledge-based systems and successfully applies them to demanufacturing process decision making to obtain the maximal End-of-Life (BOL) value from discarded products. Besides the BOL management of products by means of product/material recovery to decrease environmental impacts, the concepts of design for environment and sustainable development are investigated. Based on Sustainability Target Method, a sensitivity analysis decision-making method is proposed. It provides a company with suggestions to improve its product\u27s sustainability in the most cost-effective manner

A review of applications of fuzzy sets to safety and reliability engineering

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller