Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Container Relocation Problem: Approximation, Asymptotic, and Incomplete
  Information   The Container Relocation Problem (CRP) is concerned with finding a sequence
of moves of containers that minimizes the number of relocations needed to
retrieve all containers respecting a given order of retrieval. While the
problem is known to be NP-hard, certain algorithms such as the A* search and
heuristics perform reasonably well on many instances of the problem. In this
paper, we first focus on the A* search algorithm, and analyze lower and upper
bounds that are easy to compute and can be used to prune nodes. Our analysis
sheds light on which bounds result in fast computation within a given
approximation gap. We present extensive simulation results that improve upon
our theoretical analysis, and further show that our method finds the optimum
solution on most instances of medium-size bays. On "hard" instances, our method
finds an approximate solution with a small gap and within a time frame that is
fast for practical applications. We also study the average-case asymptotic
behavior of the CRP where the number of columns grows. We calculate the
expected number of relocations in the limit, and show that the optimum number
of relocations converges to a simple and intuitive lower-bound. We further
study the CRP with incomplete information by relaxing the assumption that the
order of retrieval of all containers are initially known. This assumption is
particularly unrealistic in ports without an appointment system. We assume that
the retrieval order of a subset of containers is known initially and the
retrieval order of the remaining containers is observed later at a given
specific time. Before this time, we assume a probabilistic distribution on the
retrieval order of unknown containers. We combine the A* algorithm with
sampling technique to solve this two-stage stochastic optimization problem. We
show that our algorithm is fast and the error due to sampling and pruning is
reasonably small.

15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess