Disassembly is not only a premise of products recycling, but also an important link of products remanufacturing. However, used products suffer from the influence of a variety of uncertainties. The randomness of disassembly process is a significant feature. In this paper, a disassembly network is established, in which lengths of arc are stochastic variables with a specified power subject to specified distributions and denote removal times of parts, the energy evaluation method integrating two or more uncertain variables is proposed. According to different disassembly decision-making criteria, three types of typical stochastic programming models of a disassembly process are developed, namely the minimum expected value model, the maximum energy disassemblability degree model and D′-minimum energy model. The energy probability distributions are determined through the application of stochastic linear programming and maximum entropy principle. Synchronously, based on obtained theoretical probability distributions, the quantitative evaluation and stochastic programming of a disassembly process are realized. The simulation results show that the proposed method is feasible and effective to solve the stochastic programming issue with time-varying stochastic characteristics.
The rapidly growing concern for environmental protection and resource utilization has stimulated many new activities in the industrialized word for coping with urgent environment problems caused by the steadily increasing consumption of industrial products. One of the upsetting problems is a disposal issue of end-of-life products. For example, it is predicted that 9 million refrigerators, 12 million air-conditioners, 11 million washing machines, 58 million televisions and 70 million computers will be scrapped in China in 2010 (Li & Wen, 2006). In order to recycle them, it is necessary that disassembly of end-of-life products should be implemented.
Disassembly is defined by Brennan, Gupta, and Taleb (1994) as “the process of systematic removal of desirable constituent parts from an assembly while ensuring that there is no impairment of the parts due to the process”. Disassembly is conductive and instructive to recycling of products. Only in this way, can we achieve high purity of material recycling and realize good reuse of parts. Two methods are generally used to remove components or materials: destructive and non-destructive disassembly. The most common methods for a destructive disassembly are shredding processes. Shredding processes can damage potential parts. Meanwhile, shredding processes can result in the mixture of materials, which is not conducive to the recovery of high purity of materials (Jovane et al., 1993). Therefore, the current research on disassembly problems mainly focuses on the non-destructive disassembly. Consumed time, cost and energy of a disassembly process are closely related to economic benefits of product recovery, thus, in a non-destructive condition, disassembly evaluation and optimization have become one of the hot ones.
Currently, the research mainly focuses on deterministic disassembly evaluation and planning. However, the disassembly process of actual products has a strong uncertainty due to a variety of unpredictable and uncontrollable factors, such as use and design factors of products. Moreover, most of disassembly operations are performed mainly by hand. The uncertainty of a disassembly process is further increased because of the presence of human factors. Although some authors consider that a removal operation is an event with given certain probability to account for uncertainty (Geiger and Zussman, 1996 and Andres et al., 2007), based on this assumption, the determination of the optimal path of a disassembly process is merely a probabilistic planning problem.
It can be seen that removal power is also varying in a disassembly process when time-varying. Therefore, it is necessary for the introduction of a new energy evaluation method of a disassembly process integrating two or more uncertain variables. What’s more, stochastic optimization of a disassembly process can be realized based on this novel evaluation method.
The rest of the paper is organized as follows: Section 2 discusses the literature review on disassembly. Section 2 introduces some assumptions and basic concepts of stochastic disassembly evaluation. Section 4 defines typical stochastic programming models based on energy evaluation method. Section 5 introduces a calculation method of typical stochastic programming models. Section 6 designs a solution algorithm of it. In Section 7, a numerical example is presented to test its effectiveness. Section 8 presents our discussion. Finally, Section 9 concludes our work and describes our future research steps.
A disassembly planning problem is one of the hot ones. In this paper, based on the uncertainty feature of a disassembly process, a novel energy evaluation method integrating multiple variables is proposed. Secondly, according to disassembly execution, three models for disassembly energy planning are proposed. Finally, the proposed models are solved by stochastic linear programming and maximum entropy principle. The results denote the algorithm can be used to solve the proposed models. In the future, some actual data of removal time needs to be collected and analyzed in order to provide the support for a disassembly decision-making.
