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Recently, there has been growing interest in composition of cloud manufacturing resources (CMRs). Composition of CMRs is a feasible innovation to fulfill the user request while single cloud manufacturing resource cannot satisfy the functionality required by the user. In this paper, we propose a new case-based approach for the composition of CMRs. The basic idea of the present approach is to provide a computational framework for the composition of CMRs by imitating the common design method of reviewing past designs to obtain solution concepts for a new composite cloud manufacturing resource (CCMR). A notion of virtual cloud manufacturing resource generators (VCMRGs) is introduced to conceptualize and represent underlying CCMRs contained in existing CCMRs. VCMRGs are derived from previous CCMRs and serve as new conceptual building blocks for the composition of CMRs. Feasible composite CMRs are generated by combining VCMRGs using some adaptation rules. The reuse of prior CCMRs is accomplished via VCMRGs within the framework of case-based reasoning. We demonstrate that the proposed approach yields lower execution time for fulfilling user request and shows good scalability.

A multicriteria decision-making model was proposed in order to acquire the optimum one among different product design schemes. VIKOR method was introduced to compute the ranking value of each scheme. A multiobjective optimization model for criteria weight was established. In this model, projection pursuit method was employed to identify a criteria weight set which could keep classification information of original schemes to the greatest extent, while PROMETHEE II was adopted to keep sorting information. Dominance based multiobjective simulated annealing algorithm (D-MOSA) was introduced to solve the optimization model. Finally, an example was taken to demonstrate the feasibility and efficiency of this model.

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing. [ABSTRACT FROM AUTHOR]