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We investigate the performance of several hyperheuristics applied to a real-world personnel-scheduling problem. A hyperheuristic is a high-level search method which manages the choice of low level heuristics, making it a robust and easy to implement approach for complex real-world problems. We need only to develop new low level heuristics and objective functions to apply a hyperheuristic to an entirely new problem. Although hyperheuristic methods require limited problem-specific information, their performance for a particular problem is determined to a great extent by the quality of low level heuristics used. We address the question of designing the set of low level heuristics for the problem under consideration. We construct a large set of low level heuristics by using a technique which allows us to "multiply" partial low level heuristics. We apply hyperheuristic methods to a trainer scheduling problem using commercial data from a large financial institution. The results of the experiments show that simple hyperheuristic approaches can successfully tackle a complex real-world problem provided that low level heuristics are carefully selected to treat various constraints. We also examine how the choice of different sets of low level heuristics affects solution quality.
Published in:
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on (Volume:2 )
Abstract- This paper investigates the performance of
several hyperheuristics applied to a real-world
personnel scheduling problem. A hyperheuristic is a
high-level search method which manages the choice of
low level heuristics, making it a robust and easy to
implement approach for complex real-world problems.
We need only to develop new low level heuristics and
objective functions to apply a hyperheuristic to an
entirely new problem. Although hyperheuristic methods
require limited problem-specific information, their
performance for a particular problem is determined to a
great extent by the quality of low level heuristics used.
This paper addresses the question of designing the set of
low level heuristics for the problem under consideration.
We construct a large set of low level heuristics by using
a technique which allows us to “multiply” partial low
level heuristics. We apply hyperheuristic methods to a
trainer scheduling problem using commercial data
from a large financial institution. The results of the
experiments show that simple hyperheuristic
approaches can successfully tackle a complex real-
world problem provided that low level heuristics are
carefully selected to treat various constraints. We also
examine how the choice of different sets of low level
heuristics affects solution quality.
1 Introduction
Given its economic importance, there is continuing
research interest in solving personnel scheduling problems
in order to optimise measures such as worker quality of
life, workforce utilisation and service quality. The purpose
of personnel scheduling is to allocate the available
workforce to timeslots and locations and to assign
particular tasks to each member of staff.
However, real-world scheduling problems require
increasingly complex models and computing optimal
solutions may require prohibitive amounts of computer
time. Heuristic methods are often used in practice, which
produce solutions of acceptable quality in reasonable time.
Various metaheuristic approaches have been developed
and successfully applied for different personnel scheduling
problems. Recent examples include fast local search and
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We investigate the performance of several hyperheuristics applied to a real-world personnel-scheduling problem. A hyperheuristic is a high-level search method which manages the choice of low level heuristics, making it a robust and easy to implement approach for complex real-world problems. We need only to develop new low level heuristics and objective functions to apply a hyperheuristic to an entirely new problem. Although hyperheuristic methods require limited problem-specific information, their performance for a particular problem is determined to a great extent by the quality of low level heuristics used. We address the question of designing the set of low level heuristics for the problem under consideration. We construct a large set of low level heuristics by using a technique which allows us to "multiply" partial low level heuristics. We apply hyperheuristic methods to a trainer scheduling problem using commercial data from a large financial institution. The results of the experiments show that simple hyperheuristic approaches can successfully tackle a complex real-world problem provided that low level heuristics are carefully selected to treat various constraints. We also examine how the choice of different sets of low level heuristics affects solution quality.
Published in:
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on (Volume:2 )
Abstract- This paper investigates the performance of
several hyperheuristics applied to a real-world
personnel scheduling problem. A hyperheuristic is a
high-level search method which manages the choice of
low level heuristics, making it a robust and easy to
implement approach for complex real-world problems.
We need only to develop new low level heuristics and
objective functions to apply a hyperheuristic to an
entirely new problem. Although hyperheuristic methods
require limited problem-specific information, their
performance for a particular problem is determined to a
great extent by the quality of low level heuristics used.
This paper addresses the question of designing the set of
low level heuristics for the problem under consideration.
We construct a large set of low level heuristics by using
a technique which allows us to “multiply” partial low
level heuristics. We apply hyperheuristic methods to a
trainer scheduling problem using commercial data
from a large financial institution. The results of the
experiments show that simple hyperheuristic
approaches can successfully tackle a complex real-
world problem provided that low level heuristics are
carefully selected to treat various constraints. We also
examine how the choice of different sets of low level
heuristics affects solution quality.
1 Introduction
Given its economic importance, there is continuing
research interest in solving personnel scheduling problems
in order to optimise measures such as worker quality of
life, workforce utilisation and service quality. The purpose
of personnel scheduling is to allocate the available
workforce to timeslots and locations and to assign
particular tasks to each member of staff.
However, real-world scheduling problems require
increasingly complex models and computing optimal
solutions may require prohibitive amounts of computer
time. Heuristic methods are often used in practice, which
produce solutions of acceptable quality in reasonable time.
Various metaheuristic approaches have been developed
and successfully applied for different personnel scheduling
problems. Recent examples include fast local search and
Link download:
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Thanks nhé :club: