Algorithms for asset replacement under limited technological forecast

Yatsenko, Yuri; Hritonenko, Natali

doi:10.1016/j.ijpe.2014.08.020

Cited by 18 publications

(2 citation statements)

References 24 publications

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“…Hagspiel, Huisman, and Nunes (2015) focused upon uncertain timing of future technology when the instant rate of new arrivals changes after a fixed or an uncertainty period time. Yatsenko and Hritonenko (2015) considered different optimal replacement methods of assets under changing purchase and operation -maintenance cost caused by technology development; and deduced two modified methods using limited technological forecast data but deliver the solutions close to the infinite horizon replacement. However, these above articles only use the cost flow to characterise the asset deterioration state and do not consider the maintenance option for extension of the asset life in order to wait a better technology.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach that focuses on the strategic investment goals is mainly found in the management science literature. In this context, the maintenance issue takes even greater importance and may be considered as an economic investment policy as it allows delaying replacement with existing technology in order to await new technology (Bethuyne 2002;Büyüktahtakın and Hartmanb 2015;Hagspiel, Huisman, and Nunes 2015;Hopp and Nair 1994;Mauer and Ott 1995;Yatsenko and Hritonenko 2015). These models take into account the uncertainty in unknown future technology.…”

Section: Introductionmentioning

confidence: 99%

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Acquisition of new technology information for maintenance and replacement policies

Nguyen

Yeung

Castanier

2016

International Journal of Production Research

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In this paper, we propose the first model that considers the option to acquire information on the profitability of a new technology that is not yet available on the market for asset maintenance and replacement decisions. We consider the uncertainty of future asset characteristics by incorporating information acquisition decisions into a non-stationary Markov decision process framework. Using this framework, we optimise asset maintenance and replacement decisions as well as the optimal timing of new technology adoption. Through mathematical analyses, the monotone properties and convexity of the value function and optimal policy are deduced. Deeper numerical analyses highlight the importance of considering the acquisition of information on future technology when formulating a maintenance and replacement policy for the asset. We also deduce a non-intuitive result: an increase in the arrival probability of new technology does not necessarily make the acquisition of additional information more attractive.

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Section: Introductionmentioning

confidence: 99%