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Logistics management class lectures_78270232-Integrated-Logistics-Management-An-Opportunity-for-Met-a-Heuristics

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Integrated Logistics Management:
An opportunity for Metaheuristics
Helena R. Lourenço
Grup de Recerca en Logistica Empresarial
DEE, Universitat Pompeu Fabra
R. Trias Fargas 25-27
08005 Barcelona, Spain
helena.ramalhinho@econ.upf.es
Date: October 31, 2001
Abstract
In today’s highly competitive and global marketplace the pressure on organizations to
find new ways to create and deliver value to customers grows ever stronger. In the last
two decades, the logistics function has moved to the center stage. There has been a
growing recognition that it is through an effective logistics management throughout the
firm and supply chain that the goal of cost reduction and service enhancement can be
achieved. The key to success in Integrated Logistics Management (ILM) require heavy
emphasis on integration of activities, cooperation, coordination and information sharing
throughout the firm and the entire supply chain, from suppliers to customers. To be able
to respond to the challenge of integration there is the need of sophisticated decision
support systems based on powerful mathematical models and solution techniques,
together with the advances in information and communication technologies. The industry
and the academia have become increasingly interested in ILM to be able to respond to the
problems and issues posed by the changes in the logistics function. We present a brief
discussion on the important issues in ILM. We then argue that metaheuristics can play an
important role in solving complex logistics problems derived by the importance of
designing and managing logistics activities within the supply chain as a single entity. We
will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the
ones, but not limited to, with great potential to be used on solving the ILM related
problems. We will present briefly some successful applications.
Keywords: Integrated Logistics Management, Metaheuristics, Iterated Local Search,
Tabu Search and Scatter Search

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1. Introduction
In today’s highly competitive and global marketplace the pressure on organizations to
find new ways to create and deliver value to customers grows ever stronger. The
increasing need of industry to compete with its products in a global market, across cost,
quality and service dimensions, has driven the need to develop logistic systems more
efficient than those traditionally employed. Therefore, in the last two decades, logistics
has moved from an operational function to the corporate function level. There has been a
growing recognition that it is through an effective logistics management that the goal of
cost reduction and service enhancement can be achieved.
The key to success in Integrated Logistics Management (ILM) require heavy emphasis on
integration of activities, cooperation, coordination and information sharing throughout
the entire supply chain, from suppliers to customers. To be able to respond to the
challenge of integration there is the need of sophisticated decision support systems (DSS)
based on powerful mathematical models and solution techniques, together with the
advances in information and communication technologies. There is no doubt of the
importance of quantitative models and computer based tools for decision making in
today’s business environment. This is especially true in the rapidly growing area of
integrated logistics management. These computer-based logistics systems can make a
significant impact in the decision process on the organizations. That’s why the industry
and the academia have become increasingly interested in ILM and logistics DSS to be
able to respond to the problems and issues posed by the changes in the area.
Many well-known algorithmic advances in optimization have been made, but it turns out
that most have not had the expected impact on the decisions for designing and optimizing
logistics problems. For example, some optimization techniques are of little use because
they are not well suited to solve complex real logistics problems in the short time needed
to make decisions. Also some techniques are highly problem-dependent and need high
expertise. This adds difficulties in the implementations of the decision support systems
which contradicts the tendency to fast implementation in a rapid changing world. In fact,
some of the most popular commercial packages use heuristic methods or rules of thumb.
The area of heuristic techniques has been the object of intensive studies in the last
decades with new and powerful technique proposed to solve hard problems, as many
metaheuristic methods. Therefore, for one side there is the need for sophisticated logistics
DSS to able the organizations to respond quickly to new issues and problems faced on the
ILM, and for the other side there are advances in the area of metaheuristics that can
provide an effective response to complex problems. This provides a fertile ground for
applications of these techniques to ILM and, afterwards, the development of computer-
based systems to help logistics decisions.
The objectives of this work is to give an understanding of the role that metaheuristics can
play in solving complex logistics problem in a integrated business processes environment
such as optimizing routing distribution, supply chain design, production scheduling and
resource allocation.

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Integrated Logistics Management: An opportunity for Metaheuristics Helena R. Lourenço Grup de Recerca en Logistica Empresarial DEE, Universitat Pompeu Fabra R. Trias Fargas 25-27 08005 Barcelona, Spain helena.ramalhinho@econ.upf.es Date: October 31, 2001 Abstract In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, the logistics function has moved to the center stage. There has been a growing recognition that it is through an effective logistics management throughout the firm and supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Integrated Logistics Management (ILM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the firm and the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in ILM to be able to respond to the problems and issues posed by the changes in the logistics function. We present a brief discussion on the important issues in ILM. We then argue that metaheuristics can play an important role in sol ...
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