An evaluation methodology for disassembly processes
Proceedings of the 21st international conference on Computers and industrial engineering
A petri net approach to disassembly process planning
Proceedings of the 23rd international conference on on Computers and industrial engineering
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Finding the shortest path in stochastic networks
Computers & Mathematics with Applications
Disassembly sequence planning in a disassembly cell context
Robotics and Computer-Integrated Manufacturing
Robotics and Computer-Integrated Manufacturing
An object-oriented intelligent disassembly sequence planner for maintenance
Computers in Industry
The decision model of task allocation for constrained stochastic distributed systems
Computers and Industrial Engineering
A Lexicographic Nelder-Mead simulation optimization method to solve multi-criteria problems
Computers and Industrial Engineering
Fuzzy-Petri-net-based disassembly planning considering human factors
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
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.