Decision Sciences Journal
Volume 31, Number 3
Summer 2000
Sequential Search and Selection Problem Under Uncertainty
Young H. Chun
Department of Information Systems and Decision Sciences, E. J.
Ourso College of Business Administration, Louisiana State University,
Baton Rouge, LA 70803-6316, email: chun@lsu.edu
Abstract. This paper formulates and discusses a series
of sequential decision problems of the following common structure:
A decision alternative of multiple attributes—that is, a
job, an employee, or an investment alternative—is to be
selected within a certain fixed length of time. An unknown number
of alternatives are presented sequentially, either deterministically
or in a random manner. The decision maker can rank all the alternatives
from best to worst without ties, and the decision to accept or
reject an alternative is based solely on the relative ranks of
those alternatives evaluated so far. The nonparametric sequential
decision problem is first studied for a model involving a discrete
time period and then generalized in terms of continuous time.
Also considered is a variant of this problem involving a Bayesian
estimation of (1) the uncertain probability of having an alternative
at a given stage in the discrete-time model and (2) the arrival
rate of alternatives in the continuous-time model. The optimal
selection strategy that maximizes the probability of selecting
the absolute best alternative is illustrated with the job search
problem and the single-machine job assignment problem.
Subject Areas: Decision Analysis: Sequential decision
making; Dynamic Programming: Stochastic model applications; and
Probabilistic Models: Optimal stopping rule. |