No abstract available.

Disassembly, as the process of systematic removal of desirable constituent parts from an assembly, is of growing importance due to the increasing environmental and economic pressure. Although disassembly in practice is manual and labor intensive, little attention has been paid to the human intervention in the disassembly process. This paper addresses this deficiency by developing a fuzzy attributed Petri net (FAPN) model to mathematically represent uncertainty in disassembly due to a large amount of human intervention. An algorithm based upon this model is further proposed for optimal disassembly planning with a view to making the technique more applicable to real industry settings. The benefit of the proposed model and algorithm is illustrated through the disassembly of a personal computer (PC) in a prototypical disassembly system.

Autonomous mobile vehicles rely on sensors as a primary perception mechanism. Multiple sensors are used to tolerate certain types of errors and inaccuracy due to system faults and uncertain environment. An autonomous vehicle should be able to detect the dependability of various sensor data and choose the best control strategy based on its internal state and the external environment. This paper proposes a fuzzy rule based control strategy selection approach for mobile navigation in a dynamic real world environment. Fuzzy reasoning Petri nets are used for system modeling and parallel reasoning. The control strategy combining the most dependable sensor data can be chosen quickly based on the proposed approach. Case studies are provided to illustrate and verify the approach.

Practical disassembly process planning is extremely important for efficient material recycling and components reuse. The research work for the process planning in literature focuses on the generation of optimal sequences based on the predictive information of products. The used products, unfortunately, exhibit high uncertainty since products may experience very different conditions during their use stage. The indeterminate characteristics associated to used products often makes the predetermined plan unrealistic. Their disassembly process has to be decided dynamically adaptive to the products' specific status. To be able to deal with uncertainty in a dynamic decision making process, this paper presents a fuzzy reasoning Petri net (FRPN) model to represent related decision making rules in disassembly process. Using the proposed fuzzy reasoning algorithm based on the FRPN model, the multicriterion disassembly rules can be considered in the parallel way to make the decision automatically and quickly. Instead of producing the disassembly sequences before disassembling a whole product, the proposed method makes intelligent decisions based on dynamically updated status of components in the product at each disassembly step. Therefore, it is adaptive to the changes that arise during the process. Finally, an example is used to illustrate the application of the proposed methodology.

Increasing global population and consumption are causing declining natural and social systems. Sustainable development addresses these issues by integrating strategies for economic successes, environmental quality, and social equity. A Sustainability Target Method (STM) provides a practical sustainability target for individual businesses and products through determining the relative indicator resource productivity for environmental performance and the absolute indicator Eco-Efficiency. Based on the indicators in STM, this paper formulates the component selection problem such that a company can improve sustainability of its products most effectively through improving components' performance. The approach is illustrated through an example.

Fuzzy rule-based technique, combining fuzzy logic and expert system methodology, not only is capable to deal with uncertainty in intrusion detection but also allows the most flexible reasoning about the widest variety of information possible. It can be used in both anomaly and misuse detections. This paper presents a method for detecting intrusion based on fuzzy rule-based technique. Fuzzy Reasoning Petri Nets (FRPN) model is used to represent fuzzy rule base and to derive the final detection decision as an inference engine. FRPN have parallel reasoning ability and are readily used into real time detection. An illustration example of TCP SYN flooding attack detection is provided.

This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. The issues of how to represent and reason about rules containing negative literals are addressed in the proposed PN model. The execution rules based on the model are defined formally using the operators in max-algebra. Then, a fuzzy reasoning algorithm is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional PNs and allows one to exploit the maximum parallel reasoning potential embedded in the model. The legitimacy and feasibility of the proposed approach are proved and validated through a turbine fault diagnosis expert system.

An algorithm is developed to determine disassembly levels and bins to maximize the profit from a used product through balancing the resource invested in disassembly processes, the return realized from them and the environmental impact. The algorithm utilizes the information from a computer aided design (CAD) system and requires minimal user input with the computational complexity of O(n), where n is the number of parts and final subassemblies in a product. It has been successfully used in the development of a demanufacturing stage of a multilifecycle assessment and analysis (MLCA) tool. The benefit of the proposed algorithm is illustrated through an example.

As an integral part of a product life cycle, demanufacturing is a process of disassembling a product and assigning collection bins for the resultant subassemblies and parts, which are then reused, remanufactured, reengineered, or disposed of. This paper proposes an algorithm to determine disassembly levels and bins in an integrated way to maximize the profit from a used product through balancing the resource invested in disassembly processes, the return, and the environmental impact caused by them. The algorithm utilizes the information from a computer-aided design system and requires minimal user input with the computational complexity of O(n/sup 2/+mn), where n is the number of parts and final subassemblies in a product, and m is the number of collection bins. It has been successfully used in the development of a demanufacturing stage of a multi-life cycle assessment and analysis tool. The benefit of the proposed algorithm is illustrated through a laptop computer example.

A Multi-Lifecycle Assessment and Analysis (MLCA) Tool is developed to apply the MLCA methodology on electronic products and help designers evaluate environmental performance of their products. The design and development of a demanufacturing stage, one of the main stages in the MLCA tool, is emphasized. The work includes disassembly planning, bin specification, bin process definition and analysis report. The developed algorithm for bin assignment requires minimal user input. An improved algorithm, which requires more data information, is proposed to achieve more benefits from a used product. Based on the data associated with each process for bins, the reports about cost, environmental burden, and energy consumption can be obtained so that users cart make the best decisions on the end-of-life management of a product by balancing economical and environmental needs.

Efficient reasoning for a complex fuzzy production rule-based system is a very important research issue and has many applications in unstructured environments with incomplete/imprecise information only. This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. Based on the model, a formal reasoning algorithm using the operators in max-algebra is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional Petri nets and allows one to exploit maximum parallel reasoning potential embedded in the model. Its legitimacy and feasibility are proved and validated through an example.