Multicriteria Flow Shop Scheduling Problem - Enterprise Information Systems Design, Implementation and Management: Organizational Applications - page 225

Talbi et al. (2001) and Arroyo & Armentano
(2005), propose Multi-Objective Local Search
(MOLS) based on the concept of Pareto domi-
nance. The first one applies MOLS when a sim-
plification of Genetic Local Search stops. The
last one includes preservation of dispersion in
the population, elitism, and use of a parallel bi-
objective local search so as intensify the search
in distinct regions.

Murata et al. (1996) presents a MOGA based
on a weighted sum of objective functions with
variable weights. This algorithm belongs to the
class of MOEA and it has been improved in Ishi-
buchi & Murata (1998) by adding a local search
procedure to the offspring.
Chang et al. (2007) applies subpopulation GA.
Artificial chromosomes are created and introduced
into the evolution process to improve the efficiency
and the quality of the solution.

F//C max ,T max

F//inventory cost function of: earliness, tardi-
ness and work in process

Arroyo & Armentano (2005) applies their
MOLS algorithm to this problem.

Bülbül et al. (2003) applies Dantzig-Wolfe
reformulation and Lagrangian relaxation to an
Integer Programming formulation.

F//f(C max ,T max )

Daniels & Chambers (1990) present an ap-
proximate B&B. comparison and study

F//ΣC i ,C max ,ΣI j Geiger (2007) presents a study of the problem

structure and the effectiveness of local search
neighbourhoods within an evolutionary search
framework.
Minella et al. (2007) and Zitzler & Thiele
(1999) are previous surveys focusing on MOEA
that include computational comparison for the
Pareto approach.

Ho & Chang (1991) and Rajendran (1995),
propose heuristic procedures based on the idea
of minimizing the gaps between the completion
times of jobs on adjacent machines (one of the
improvement techniques used by Mokotoff, 2009,
was inspired by this mechanism).
Yagmahan & Yenisey (2008) applies ACO.

F//many Criteria

concLusion

Dorn et al. (1996) presents a comparison
of four iterative improvement techniques for
flow-shop scheduling problems that differ in
local search methodology. These techniques are
iterative deepening, random search, TS and GA.
The evaluation function is defined according to
the gradual satisfaction of explicitly represented
domain constraints and optimization functions.
The problem is constrained by a greater variety of
antagonistic criteria that are partly contradictory.

In this paper, we have attempted to present a
survey encompassing Multicriteria Flow-Shop
Scheduling problems. Because they have impor-
tant applications in Management Science, these
models have been intensively studied during the
last years. Considerable progress has been made
toward presenting effective solutions to practi-
cal problems, especially with the proliferation
of metaheuristic approaches. Some variants,
e.g. setup depending on the sequence, have been
investigated and well solved by means of meta
and, more recently, hyper heuristic techniques.

F//f(ΣC i ,C max ,ΣT i )