Energy evaluation method and its optimization models for process planning with stochastic characteristics: A case study in disassembly decision-making

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.

► A novel energy method is proposed to evaluate a disassembly process.
► Three energy models are proposed to solve disassembly planning problem.
► Proposed models are solved by stochastic programming and maximum entropy principle.