• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Optimal Buffer Allocation in Multi-Product Repairable Production Lines Based on Multi-State Reliability and Structural Complexity

Vol. 14, No. 4, April 30, 2020
10.3837/tiis.2020.04.010, Download Paper (Free):

Abstract

In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system’s multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.


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Cite this article

[IEEE Style]
J. Duan1, N. Xie and L. Li, "Optimal Buffer Allocation in Multi-Product Repairable Production Lines Based on Multi-State Reliability and Structural Complexity," KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1579-1602, 2020. DOI: 10.3837/tiis.2020.04.010.

[ACM Style]
Jianguo Duan1, Nan Xie, and Lianhui Li. 2020. Optimal Buffer Allocation in Multi-Product Repairable Production Lines Based on Multi-State Reliability and Structural Complexity. KSII Transactions on Internet and Information Systems, 14, 4, (2020), 1579-1602. DOI: 10.3837/tiis.2020.04.010.