A Dynamic Replacement Economy Decision Model

Oakford, R. V.; Lohmann, Jack R.; Salazar, Arturo

doi:10.1080/07408178408974669

Cited by 56 publications

(34 citation statements)

References 2 publications

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“…Dreyfus [13] modeled technological change in revenue, maintenance, and replacement costs using bounded exponential functions. The Dynamic Replacement Economy Decision Model (DREDM) developed in [31] is a generalization of Wagner's dynamic programming model that allows for multiple challengers and timevarying parameters.…”

Section: Replacement Analysismentioning

confidence: 99%

“…The general single asset replacement problem is widely studied in the literature [2], [13], [29], [31]. It may be de¯ned as follows: a n´a sset in use at time period n N´number of time periods that service is required A n´n umber of challenging assets at time period n…”

Section: General Single Asset Replacementmentioning

confidence: 99%

“…The Possibilistic Model for Single Asset Replacement via dynamic programming (PMSAR) [20] is a generalization of SREDM in [31] to allow for fuzzy parameters such as aggregate cash°ows, in°ation, or technological change. SREDM used the \best", \worst", and \most likely" estimates of the parameters, as in PERT analysis, to create triangular probability distributions.…”

Section: General Single Asset Replacementmentioning

confidence: 99%

“…Let us now consider an adaptation of a replacement problem discussed by Lohmann [31] in which we speci¯cally incorporate fuzzy uncertainty and use it as a vehicle for illustrating our point.…”

Section: An Example Problemmentioning

confidence: 99%

“…If, for example, a moderate in°a-tion increase of [1%; 2%; 3%] per year were expected while the other parameters remained the same, the optimal replacement policy ¦ becomes: Contrast this with the traditional stochastic model SREDM where the solution is derived using Monte-Carlo simulation, a technique that generates a large number of realizations of the uncertain (and assumed random) variables and solves each set of them deterministically [31]. From this large sample, the probability that each alternative current asset is the optimal¯rst choice can be estimated (see Figure 4) as well as the corresponding cumulative probability distributions of (1) the economic life of each current asset, (2) the NPV of the optimal sequence of challengers for a¯nite horizon (see Figure 5, and (3) the equivalent¯nite horizon time for in¯nite horizon problems.…”

Section: An Example Problemmentioning

confidence: 99%

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On replacement models via a fuzzy set theoretic framework

Esogbue

Hearnes

1998

IEEE Trans. Syst., Man, Cybern. C

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Abstract| Uncertainty is present in virtually all replacement decisions due to unknown future events, such as revenue streams, maintenance costs, and in°ation. Fuzzy sets provide a mathematical framework for explicitly incorporating imprecision into the decision making model, especially when the system involves human subjectivity. This paper illustrates the use of fuzzy sets and possibility theory to explicitly model uncertainty in replacement decisions via fuzzy variables and fuzzy numbers. In particular, a fuzzy set approach to economic life of an asset calculation as well as a¯-nite horizon single asset replacement problem with multiple challengers is discussed. Because the use of triangular fuzzy numbers provides a compromise between computational efciency and realistic modeling of the uncertainty, this discussion emphasizes fuzzy numbers. The algorithms used to determine the optimal replacement policy incorporate fuzzy arithmetic, dynamic programming with fuzzy rewards, the vertex method, and various ranking methods for fuzzy numbers. A brief history of replacement analysis, current conventional techniques, the basic concepts of fuzzy sets and possibility theory, and the advantages of the fuzzy generalization are also discussed.

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Section: Replacement Analysismentioning

confidence: 99%

Section: General Single Asset Replacementmentioning

confidence: 99%

Section: General Single Asset Replacementmentioning

confidence: 99%

Section: An Example Problemmentioning

confidence: 99%

Section: An Example Problemmentioning

confidence: 99%

See 3 more Smart Citations

On replacement models via a fuzzy set theoretic framework

Esogbue

Hearnes

1998

IEEE Trans. Syst., Man, Cybern. C

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Solution algorithms for the parallel replacement problem under economy of scale

Chen

1998

Naval Research Logistics

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The parallel replacement problem with demand and capital budgeting constraints

Hartman

2000

Naval Research Logistics

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A generalized parallel replacement problem is considered with both fixed and variable replacement costs, capital budgeting, and demand constraints. The demand constraints specify that a number of assets, which may vary over time, are required each period over a finite horizon. A deterministic, integer programming formulation is presented as replacement decisions must be integer. However, the linear programming relaxation is shown to have integer extreme points if the economies of scale binary variables are fixed. This allows for the efficient computation of large parallel replacement problems as only a limited number of 0–1 variables are required. Examples are presented to provide insight into replacement rules, such as the “no‐splitting‐rule” from previous research, under various demand scenarios. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 40–56, 2000

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A Dynamic Replacement Economy Decision Model

Cited by 56 publications

References 2 publications

On replacement models via a fuzzy set theoretic framework

On replacement models via a fuzzy set theoretic framework

Solution algorithms for the parallel replacement problem under economy of scale

The parallel replacement problem with demand and capital budgeting constraints

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