Design process and data models to support the design of sustainable remanufactured products

The need for increased levels of reuse and remanufacturing is driving the need for sustainable design of remanufactured products. It is acknowledged that design for remanufacturing benefits the environment. However, it seldom integrates eco-design approaches as it requires ‘life cycle thinking’ with closed-loop life cycles. The objective of this paper is to support eco-design of remanufactured products with design process and data models structuring the activities to be performed and providing the support required. We consider the development of two activities in particular: definition of the target, and environmental analysis. The first activity exploits the concept of RPP (Remanufacturable Product Profile), building on the criteria that are crucial for a successful remanufactured product. The second one exploits the life cycle brick concept that associates each product component with a specific life cycle and related environmental impacts. The models proposed are exploited in a case study concerning the design of remanufactured truck gearboxes.     

Formalization of interrelations between operators and data within the framework of an extended algebra of algorithms

A system of algorithmic algebras is considered whose basic concepts are newly interpreted to formalize the interrelation between operators and data of such a system. A modified formal instrument is constructed that extends the possibilities of design and transformation of regular schemes of algorithms. Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 170–182, November–December 2008.     

Possibilistic and probabilistic fuzzy clustering: unification within the framework of the non-extensive thermostatistics

Fuzzy clustering algorithms are becoming the major technique in cluster analysis. In this paper, we consider the fuzzy clustering based on objective functions. They can be divided into two categories: possibilistic and probabilistic approaches leading to two different function families depending on the conditions required to state that fuzzy clusters are a fuzzy c-partition of the input data. Recently, we have presented in Menard and Eboueya (Fuzzy Sets and Systems, 27, to be published) an axiomatic derivation of the Possibilistic and Maximum Entropy Inference (MEI) clustering approaches, based upon an unifying principle of physics, that of extreme physical information (EPI) defined by Frieden (Physics from Fisher information, A unification, Cambridge University Press, Cambridge, 1999). Here, using the same formalism, we explicitly give a new criterion in order to provide a theoretical justification of the objective functions, constraint terms, membership functions and weighting exponent m used in the probabilistic and possibilistic fuzzy clustering. Moreover, we propose an unified framework including the two procedures. This approach is inspired by the work of Frieden and Plastino and Plastino and Miller (Physics A 235, 577) extending the principle of extremal information in the framework of the non-extensive thermostatistics. Then, we show how, with the help of EPI, one can propose extensions of the FcM and Possibilistic algorithms.     

Comments on the erratum to “The spatial and temporal consolidation of returned products in a closed-loop supply chain network” [Comput. Ind. Eng. 51 (2006) 309–320]

A genetic algorithm based mixed-integer; non-linear programming model proposed by Min et al. [Min, H., Ko, C. S., & Ko, H. J. (2006). The spatial and temporal consolidation of returned products in a closed-loop supply chain network. Computers & Industrial Engineering, 51, 309–320] was recently published in Computers & Industrial Engineering journal to solve the reverse logistics problem involving both spatial and temporal consolidation of returned products. In this work, comments and suggestions are given to the recently published model of Min et al. (2006). The comments like centralized return center location, maximum allowable distance between customers and the initial collection point, calculation of transportation cost and finally, the modification of the objective function are suggested in this work.     

A semantic framework and software design to enable the transparent integration,reorganization and discovery of natural systems knowledge

I present a conceptualization that attempts to unify diverse representations of natural knowledge while providing a workable computational framework, based on current semantic web theory, for developing, communicating, and running integrated simulation models. The approach is based on a long-standing principle of scientific investigation: the separation of the ontological character of the object of study from the semantics of the observation context, the latter including location in space and time and other observation-related aspects. I will show how current Knowledge Representation theories coupled with the object-oriented paradigm allow an efficient integration through the abstract model of a domain, which relates to the idea of aspect in software engineering. This conceptualization allows us to factor out two fundamental causes of complexity and awkwardness in the representation of knowledge about natural system: (a) the distinction between data and models, both seen here as generic knowledge sources; (b) the multiplicity of states in data sources, handled through the hierarchical composition of independently defined domain objects, each accounting for all states in one well-known observational dimension. This simplification leaves modelers free to work with the bare conceptual bones of the problem, encapsulating complexities connected to data format, and scale. I will then describe the design of a software system that implements the approach, referring to explicit ontologies to unambiguously characterize the semantics of the objects of study, and allowing the independent definition of a global observation context that can be redefined as required. I will briefly discuss applications to multi-scale, multi-paradigm modeling, intelligent database design, and web-based collaboration.