I am surprised that bplapply() takes a few seconds to detect the number of available cores, while multicoreWorkers() or MulticoreParam() are fast when they run by themselves:

> system.time(BiocParallel::bplapply(1:1e2 , function(x) order(rnorm(n=1e3)), BPPARAM = MulticoreParam()))
   user  system elapsed
  0.060   0.180   4.033

Multicore setup is fast if number of cores is fixed: 

> system.time(BiocParallel::bplapply(1:1e2 , function(x) order(rnorm(n=1e3)), BPPARAM = MulticoreParam(1)))
   user  system elapsed
  0.036   0.004   0.042

Slow if the choice is delegated to multicoreWorkers():

> system.time(BiocParallel::bplapply(1:1e2 , function(x) order(rnorm(n=1e3)), BPPARAM = MulticoreParam(multicoreWorkers())))
   user  system elapsed
  0.056   0.140   4.034

But by itself, multicoreWorkers() is fast!

> system.time(multicoreWorkers())
   user  system elapsed
  0.000   0.032   0.037

I am running BiocParallel 1.10.0.

The 'fast' version is actually because you've set the number of cores to 1; BIocParallel cheats in this circumstance and just does lapply locally. For the other versions the cost is creating the forked processes and sending data to and from the workers. For me the time is <.5s so I'm surprised at how slow your computations seem to be. You could separate out the setup costs (and amortize over uses in a script, for instance, with

param = MulticoreParam()
bpstart(param)

and then separately

bplapply(seq_len(100), function(x) order(rnorm(n=1e3)), BPPARAM=param

with a final clean-up

bpstop(param)

It would be helpful to know your sessionInfo()