Arrival target setting using HDF

Description

Employs dm.hdf over time to estimate the arrival of known specifications.

Usage

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target.arrival.hdf(xdata, ydata, date, t, rts="crs", 
                   wd=NULL, sg="ssm", ftype="d", cv="convex")

Arguments

xdata

Input(s) vector (n by m)

ydata

Output(s) vector (n by s)

date

Production date (n by 1)

t

A vantage point from which the RoC is captured

rts

Returns to scale assumption
"crs" Constant RTS (default)
"vrs" Variable RTS
"irs" Increasing RTS
"drs" Decreasing RTS

wd

Weak disposability vector indicating (an) undesirable output(s) (1 by s)

sg

Employs second-stage optimization
"ssm" Slack-sum maximization (default)
"max" Date-sum maximization
"min" Date-sum minimization

ftype

Frontier type
"d" Dynamic frontier (default)
"s" Static frontier

cv

Convexity assumption
"convex" Convexity holds (default)
"fdh" Free disposal hull (this will override rts)

Value

$eff_t

Efficiency at t

$lambda_t

Intensity vector at t

$eft_date

Effective date

$roc_avg

Average RoC

$roc_local

Local RoC

$roc_ind

Individualized RoC

$arrival_avg

Estimated arrival using roc_avg

$arrival_seg

Estimated arrival using roc_ind

Author(s)

Dong-Joon Lim, PhD

References

Lim, Dong-Joon, et al. "Comparing technological advancement of hybrid electric vehicles (HEV) in different market segments." Technological Forecasting and Social Change 97 (2015): 140~153.

Lim, Dong-Joon, and Timothy R. Anderson. Time series benchmarking analysis for new product scheduling: who are the competitors and how fast are they moving forward?. Advances in DEA Theory and Applications: with Examples in Forecasting Models. Wiley (forthcoming), 2016.

See Also

dm.hdf Distance measure using HDF
roc.hdf RoC calculation using HDF
map.soa.hdf SOA mapping using HDF
target.arrival.hdf Arrival target setting using HDF

Examples

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# Estimate arrivals of MY2015 SC/TC 8 cylinder engines
  # Load engine dataset
    df <- dataset.engine.2015
  
  # Subset for SC/TC 8 cylinder engines
    stc.8 <- subset(df, grepl("^.C..", df[, 8]) & df[, 3] == 8)
  
  # Parameters
    x <- subset(stc.8, select = 4)
    y <- subset(stc.8, select = 5:7)
    d <- subset(stc.8, select = 2)
  
  # Generate an SOA map
    target.arrival.hdf(x, y, d, 2014, "vrs")