**Meet the editor**

Javier (Javi) Del Ser is the technology manager of the OPTIMA (Optimization, Modelling and Analytics) business area at TECNALIA RESEARCH & INNOVATION (www.tecnalia.com). He achieved his Ph.D. degree (cum laude) in Electrical Engineering from the University of Navarra (Spain) in 2006, and will get his second PhD in Information Technology from the University of Alcalá

(Spain) in early 2013. He has held different positions in the University of Navarra and University of Mondragon (Spain), as well as visiting stays at the University of Delaware (USA) and VTT (Finland). His research interests lie on data analytics and heuristic algorithms for wireless sensor networks, economics, operational research and resource allocation. He has published more than 120 contributions to journals and conferences, edited 2 books, invented 4 patents, and led several research projects. Dr. Del Ser is a senior member of the IEEE, and has recently been awarded the "Talent of Bizkaia" prize for his outstanding professional curriculum.

Contents

**Preface VII**

Martins

**Garment Company 1**

**Efficient Work Teams 25**

**in Robotics 53** Alejandra Cruz-Bernal

Fernando Sandoya and Ricardo Aceves

**Multilevel Lot-Sizing Problems 77** Ikou Kaku, Yiyong Xiao and Yi Han

Stéphane Le Ménec and Antonios Tsourdos

**Assignment Problem 109**

Chapter 1 **Using Multiobjective Genetic Algorithm and Multicriteria**

Chapter 2 **Grasp and Path Relinking to Solve the Problem of Selecting**

Chapter 3 **Meta-Heuristic Optimization Techniques and Its Applications**

Chapter 4 **A Comparative Study on Meta Heuristic Algorithms for Solving**

Chapter 5 **A Two-Step Optimisation Method for Dynamic Weapon Target**

Cédric Leboucher, Hyo-Sang Shin, Patrick Siarry, Rachid Chelouah,

**Analysis for the Production Scheduling of a Brazilian**

Dalessandro Soares Vianna, Igor Carlos Pulini and Carlos Bazilio

## Contents

#### **Preface XI**

Chapter 1 **Using Multiobjective Genetic Algorithm and Multicriteria Analysis for the Production Scheduling of a Brazilian Garment Company 1** Dalessandro Soares Vianna, Igor Carlos Pulini and Carlos Bazilio Martins


Preface

many others.

The last decade has witnessed a sharp increase in the dimensionality of different underlying optimization paradigms stemming from a variety of fields and scenarios. Examples abound not only in what relates to purely technological sectors, but also in other multiple disci‐ plines, ranging from bioinformatics to finance, economics, operational research, logistics, so‐ cial and food sciences, among many others. Indeed, almost every single aspect driving this increased dimensionality has grown exponentially as exemplified by the upsurge of com‐ munication terminals for the optimization of cellular network planning or the rising need

As a result, the computational complexity derived from solving all such paradigms in an optimal fashion has augmented accordingly, to the extent of igniting an active research trend towards near-optimal yet cost-efficient heuristic solvers. Broadly speaking, heuristics resort to experience-based approximate techniques for solving problems when enumerative alternatives (e.g. exhaustive search) are not efficient due to the high computational complex‐ ity derived therefrom. In particular, meta-heuristics have lately gained momentum, con‐ ceived as heuristics springing from the mimicking of intelligent learning procedures and behaviours observed in Nature, arts and social sciences. As such, from the advent of geneti‐ cally-inspired search algorithms in mid 70s, a wide portfolio of evolutionary meta-heuristics and techniques based on the so-called swarm intelligence has been applied to distinct opti‐ mization paradigms: to mention a few, harmony search, memetic algorithms, differential search, ant colony optimization, particle swarm optimization, cuckoo search, gravitational search, intelligent water drops, coral reef optimization and simulated annealing, among

This flurry of activity around meta-heuristics and their application to real scenarios is the raison d'être of this booklet: to provide the reader with an insightful report on advances in meta-heuristic techniques in certain exemplifying scenarios. On this purpose, the booklet comprises 5 chapters, each presenting the application of different meta-heuristics to differ‐ ent scenarios. The first chapter addresses the application of multi-objective genetic algo‐ rithms for optimizing the task scheduling of garment companies. The approach takes three conflicting objectives into account: to minimize the total production time, to maximize the percentage of use of corporate production centers and to minimize the internal production centers downtime. Next, the second chapter proposes to hybridize the so-called greedy randomized adaptive search procedure (GRASP) with path relinking for optimally selecting work teams under maximum diversity criteria, with clear applications to operational re‐ search, academia and politics. The third chapter delves into a thorough review on meta-heu‐ ristics applied to the route finding problem in robotics, with an emphasis on the combination of genetic algorithms and ant colony optimization as an outperforming scheme

for sequence alignment, analysis, and annotation in genomics.

## Preface

The last decade has witnessed a sharp increase in the dimensionality of different underlying optimization paradigms stemming from a variety of fields and scenarios. Examples abound not only in what relates to purely technological sectors, but also in other multiple disci‐ plines, ranging from bioinformatics to finance, economics, operational research, logistics, so‐ cial and food sciences, among many others. Indeed, almost every single aspect driving this increased dimensionality has grown exponentially as exemplified by the upsurge of com‐ munication terminals for the optimization of cellular network planning or the rising need for sequence alignment, analysis, and annotation in genomics.

As a result, the computational complexity derived from solving all such paradigms in an optimal fashion has augmented accordingly, to the extent of igniting an active research trend towards near-optimal yet cost-efficient heuristic solvers. Broadly speaking, heuristics resort to experience-based approximate techniques for solving problems when enumerative alternatives (e.g. exhaustive search) are not efficient due to the high computational complex‐ ity derived therefrom. In particular, meta-heuristics have lately gained momentum, con‐ ceived as heuristics springing from the mimicking of intelligent learning procedures and behaviours observed in Nature, arts and social sciences. As such, from the advent of geneti‐ cally-inspired search algorithms in mid 70s, a wide portfolio of evolutionary meta-heuristics and techniques based on the so-called swarm intelligence has been applied to distinct opti‐ mization paradigms: to mention a few, harmony search, memetic algorithms, differential search, ant colony optimization, particle swarm optimization, cuckoo search, gravitational search, intelligent water drops, coral reef optimization and simulated annealing, among many others.

This flurry of activity around meta-heuristics and their application to real scenarios is the raison d'être of this booklet: to provide the reader with an insightful report on advances in meta-heuristic techniques in certain exemplifying scenarios. On this purpose, the booklet comprises 5 chapters, each presenting the application of different meta-heuristics to differ‐ ent scenarios. The first chapter addresses the application of multi-objective genetic algo‐ rithms for optimizing the task scheduling of garment companies. The approach takes three conflicting objectives into account: to minimize the total production time, to maximize the percentage of use of corporate production centers and to minimize the internal production centers downtime. Next, the second chapter proposes to hybridize the so-called greedy randomized adaptive search procedure (GRASP) with path relinking for optimally selecting work teams under maximum diversity criteria, with clear applications to operational re‐ search, academia and politics. The third chapter delves into a thorough review on meta-heu‐ ristics applied to the route finding problem in robotics, with an emphasis on the combination of genetic algorithms and ant colony optimization as an outperforming scheme

with respect to other existing approaches. On the other hand, the fourth chapter investigates different meta-heuristic algorithms in the context of multilevel lot-sizing problems, which hinge on determining the lot sizes for producing/procuring multiple items at different levels with quantitative interdependencies, so as to minimize the total production costs in the planning horizon. This chapter also introduces a special variable neighborhood based algo‐ rithm shown to perform satisfactorily for several simulated benchmark instances under di‐ verse scales. Finally, the fifth chapter ends the booklet by outlining a two-step optimization method for dynamic weapon target assignment problem, a military-driven application where an allocation plan is to be found to assigning the available weapons in an area to in‐ coming targets. Specifically, the proposed scheme combines different optimization ap‐ proaches such as graph theory, evolutionary game theory, and particle swarm optimization.

The editor would like to eagerly thank the authors for their contribution to this book, and especially the editorial assistance provided by the InTech publishing process manager, Ms. Natalia Reinic. Last but not least, the editor's gratitude extends to the anonymous manu‐ script processing team for their arduous formatting work.

> **Dr. Javier Del Ser** Technology Manager, OPTIMA Business Area TECNALIA RESEARCH & INNOVATION Zamudio, Spain

**Chapter 1**

. Most of a product lead time –

**Using Multiobjective Genetic Algorithm and**

**of a Brazilian Garment Company**

Additional information is available at the end of the chapter

tion has been called "mass customization" [1].

critical to acquire a good performance.

1 Tasks: set of operations taken on the same production phase.

allocation must regard the distinct production centers2

Carlos Bazilio Martins

http://dx.doi.org/10.5772/53701

**1. Introduction**

task1

specific tasks.

Dalessandro Soares Vianna, Igor Carlos Pulini and

**Multicriteria Analysis for the Production Scheduling**

The Brazilian garment industry has been forced to review its production processes due to the competition against Asiatic countries like China. These countries subsidize the produc‐ tion in order to generate employment, which reduces the production cost. This competition has changed the way a product is made and the kind of production. The industry has fo‐ cused on customized products rather than the ones large-scale produced. This transforma‐

In this scenario the Brazilian garment industry has been forced to recreate its production process to provide a huge diversity of good quality and cheaper products. These must be made in shorter periods and under demand. These features require the use of chronoanaly‐ sis to analyze the production load balance. Since the production time becomes crucial, the

processing time from the beginning to the end of the process – is spent waiting for resour‐ ces. In the worse case, it can reach 80% of the total time [2]. So the production load balance is

It is hard to accomplish production load balance among distinct production centers. This balance must regards the available resources and respect the objectives of the production.

2 Production centers: internal or external production cell composed by a set of individuals which are able to execute

© 2013 Vianna et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 Vianna et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.
