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.

[1]  Ruud H. Teunter,et al.  Determining optimal disassembly and recovery strategies , 2004 .

[2]  F. Hank Grant,et al.  A Lexicographic Nelder-Mead simulation optimization method to solve multi-criteria problems , 2011, Comput. Ind. Eng..

[3]  Mehmet Ali Ilgin,et al.  Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. , 2010, Journal of environmental management.

[4]  A. Charnes,et al.  Chance-Constrained Programming , 1959 .

[5]  Ehud Kroll,et al.  Disassembly analysis through time estimation and other metrics , 1999 .

[6]  Arthur C. Sanderson,et al.  A correct and complete algorithm for the generation of mechanical assembly sequences , 1991, IEEE Trans. Robotics Autom..

[7]  Alfred J.D. Lambert.  Optimal disassembly of complex products , 1994 .

[8]  Guangdong Tian,et al.  Evaluation model and algorithm of product disassembly process with stochastic feature , 2012, Clean Technologies and Environmental Policy.

[9]  Surendra M. Gupta,et al.  A petri net approach to disassembly process planning , 1998 .

[10]  Sebastián Lozano,et al.  Disassembly sequence planning in a disassembly cell context , 2007 .

[11]  Venkat Allada,et al.  Hierarchical fuzzy neural network‐based serviceability evaluation , 2000 .

[12]  John Ruggiero,et al.  Technical efficiency estimation with multiple inputs and multiple outputs using regression analysis , 2011, Eur. J. Oper. Res..

[13]  Jing-Rong Li,et al.  An object-oriented intelligent disassembly sequence planner for maintenance , 2005, Comput. Ind..

[14]  W. Eversheim,et al.  A Key Issue in Product Life Cycle: Disassembly , 1993 .

[15]  Baoding Liu.  Dependent-Chance Programming: A Class of Stochastic Optimization , 1997 .

[16]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[17]  Dinesh Kumar Sharma,et al.  Finding the shortest path in stochastic networks , 2007, Comput. Math. Appl..

[18]  Surendra M. Gupta,et al.  An evaluation methodology for disassembly processes , 1997 .

[19]  Chichang Jou,et al.  The decision model of task allocation for constrained stochastic distributed systems , 2010, Comput. Ind. Eng..

[20]  Nizan Salomonski,et al.  On-line predictive model for disassembly process planning adaptation , 1999 .

[21]  MengChu Zhou,et al.  Fuzzy-Petri-net-based disassembly planning considering human factors , 2006, IEEE Trans. Syst. Man Cybern. Part A.

[22]  MengChu Zhou,et al.  Disassembly modeling, planning, and application , 2002 .

[23]  Dan Geiger,et al.  Probabilistic Reactive Disassembly Planning , 1996 .

[24]  Spyros Reveliotis,et al.  Uncertainty management in optimal disassembly planning through learning-based strategies , 2007 .

[25]  Sinan Akpinar,et al.  Wind energy analysis based on maximum entropy principle (MEP)-type distribution function , 2007 .

[26]  Ajd Fred Lambert.  Exact methods in optimum disassembly sequence search for problems subject to sequence dependent costs , 2006 .

[27]  Anil Mital,et al.  Evaluation of disassemblability to enable design for disassembly in mass production , 2003 .

[28]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[29]  Manoj Kumar Tiwari,et al.  Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA) , 2009 .

[30]  E Iakovou,et al.  On the optimal design of the disassembly and recovery processes. , 2009, Waste management.

[31]  Baoding Liu,et al.  Theory and Practice of Uncertain Programming , 2003, Studies in Fuzziness and Soft Computing.

[32]  Hua Ke,et al.  Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem , 2009, Appl. Math. Comput..

[33]  Daniel J. Slottje,et al.  Estimating the density of unemployment duration based on contaminated samples or small samples , 2000 .

[34]  Surendra M. Gupta,et al.  Operations Planning Issues in an Assembly/Disassembly Environment , 1994 .

[35]  Franco Failli,et al.  Optimization of Disassembly Sequences for Recycling of End-of-Life Products by Using a Colony of Ant-Like Agents , 2001, IEA/AIE.

[36]  Hsin-Hao Huang,et al.  Disassembly sequence generation using a neural network approach , 2000 .