Title :
Assembly Strategies for Remanufacturing Systems With Variable Quality Returns
Author :
Jin, Xiaoning ; Hu, S. Jack ; Ni, Jun ; Xiao, Guoxian
Author_Institution :
Dept. of Mech. Eng., Univ. of Michigan, Ann Arbor, MI, USA
Abstract :
This paper studies optimal policy for modular product reassembly within a remanufacturing setting where a firm receives product returns with variable quality and reassembles products of multiple classes to customer orders. High-quality modules are allowed to substitute for low-quality modules during reassembly to provide the remanufacturing system with flexibility such that shortage in lower quality modules can be smoothed out by higher quality module inventories. We formulate the problem as a Markov decision process and characterize the structure of the optimal control policy. In particular, we show that the optimal reassembly and substitution follow a state-dependent threshold-based control policy. We also establish the structural properties of the thresholds. Using numerical experimentation, we study how system performance is influenced by key cost parameters including unit holding cost, unit assembly cost and shortage penalty cost. Finally, we compare the optimal policy with an exhaustive reassembly policy and show that there is great benefit in module substitution and threshold-based assembly control.
Keywords :
Markov processes; assembling; costing; flexible manufacturing systems; optimal control; quality control; recycling; Markov decision process; assembly strategies; cost parameters; customer orders; exhaustive reassembly policy; flexible remanufacturing system; high-quality modules; inventories; low-quality modules; modular product reassembly; module substitution; optimal control policy; product returns; shortage penalty cost; state-dependent threshold-based control policy; threshold-based assembly control; unit assembly cost; unit holding cost; variable quality returns; Assembly; Decision making; Markov processes; Optimal control; Sorting; Uncertainty; Assembly systems; Markov decision process; manufacturing planning; optimal control;
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
DOI :
10.1109/TASE.2012.2217741