Adaptive Search and the Management of Logistics Systems

Transcription

1 Adaptive Search and the Management of Logistics Systems

2 OPERATIONS RESEARCH/COMPUTER SCIENCE INTERFACES SERIES Series Editors Professor Ramesh Sharda Oklahoma State University Prof. Dr. Stefan VoB Technische Universitdt Braunschweig Other published titles in the series: Brown, Donald/Scherer, William T. Intelligent Scheduling Systems Nash, Stephen G'/Sofer, Ariela The Impact of Emerging Technologies on Computer Science and Operations Research Barth, Peter Logic-Based 0-1 Constraint Programming Jones, Christopher V. Visualization and Optimization Barr, Richard S./ Helgason, Richard V./ Kennington, Jeffery L. Interfaces in Computer Science and Operations Research: Advances in Metaheuristics. Optimization, and Stochastic Modeling Technologies Ellacott, Stephen W./ Mason, John C./ Anderson, lain 1. Mathematics of Neural Networks: Models, Algorithms & Applications Woodruff, David L. Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search Klein, Robert Scheduling of Resource-Constrained Projects

3 ADAPTIVE SEARCH AND THE MANAGEMENT OF LOGISTICS SYSTEMS Base Models for Learning Agents CHRISTIAN BIERWIRTH University of Bremen, Germany " ~. Springer Science+Business Media, LLC

4 Library of Congress Cataloging-in-Publication Bierwirth, Christian. Adaptive search and the management of logistics systems : base mode1s for 1eaming agents / Christian Bierwirth. p. cm. -- (Operations research/computer science interfaces series / ORCS 11) Inc1udes bibliographica1 references and index. ISBN ISBN (ebook) DOI / Production management--mathematica1 mode1s. 2. Genetic a1gorithms. 3. Business logistics. 1. Title. II. Series. TS155.B '1--dc Copyright 2000 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 2000 Softcover reprint ofthe hardcover lst edition 2000 AII rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher, K1uwer Academic Publishers, 101 Philip Drive, Assinippi Park, Norwell, Massachusetts Printed on acid-free paper.

5 Contents Preface Acknowledgments ix xiii Part I Fundamentals of Evolutionary Adaptive Systems 1. FROM ARTIFICIAL TO COMPUTATIONAL INTELLIGENCE 1. Background 1.1 Rule-Based Systems 1.2 Fuzzy Logic 1.3 Classifier Systems 1.4 Adaptation and Learning 2. Learning by Computational Intelligence 2.1 Neural Computation 2.2 Evolutionary Computation 2.3 Combined Approaches 3. Overview of the book 2. PRINCIPLES OF SYSTEMS 1. System Structures 1.1 General Systems 1.2 Adaptive Systems 1.3 System Interventions 2. Com plex Systems 2.1 Decomposition and Decentralization 2.2 A Framework for Coordination 2.3 Anticipation 3. Decision Making Systems 3.1 Hierarchical Aggregations 3.2 Coordination Principles 3.3 Discussion 4. Summary

6 VI ADAPTIVE SEARCH IN LOGISTICS SYSTEMS 3. GENETIC ALGORITHMS Basic Principles Population Management Selection Mechanism Reprod uction Mechanism Modifications and Extensions Alternative Population Management Selection Techniques Encoding and Genetic Operators Constraint Handling Decoding Hybridization Parameter Tuning Theoretical Considerations Statistical Models Population Genetics Summary ADAPTATION TO STATIC ENVIRONMENTS Convergence in Evolutionary Algorithms Measuring Convergence Convergence Control Approaches A Control Model of Local Recombination Structure of Search Spaces Fitness Landscapes The NK-Model Statistical Measures The Landscape of the TSP Adaptation and Local Search Iterative Improvement Algorithms A Local Search Tem plate Inferences from Fitness Landscapes Summary ADAPTIVE AGENTS Evolutionary Algorithms as Agents Agent Definitions and Properties Sensing and Acting Communication Adaptation to Changing Environments Previous Approaches Machine Learning Learning by Evolution The Architecture of Adaptive Agents 103

7 Contents vii Part II Applications of Evolutionary Adaptive Systems 6. PROBLEM REPRESENTATION IN LOGISTICS SYSTEMS Combinatorial Base Problems Traveling Salesman Quadratic Assignment Bin Packing Vehicle Routing Sequencing with Time Windows Encoding-Decoding Systems Encoding Scheme Decoding Procedures Redundancy and Forcing Learning Operators Ordering Categories Mutation Techniques Crossover Techniques Summary ADAPTIVE SCHEDULING Classical Job Shop Scheduling Problem Description Disjunctive Graph Formulation An Illustrating Example Benchmark Problems The Fitness Landscape Local-Search Algorithms Neighborhood Search Adaptive Search Genetic Local-Search Computational Study Discussion Summary TOWARDS REAL WORLD SCHEDULING SYSTEMS Sched uling Scenarios Limitations of the Disjunctive Graph Model Benchmark Problems Probabilistic Scheduling Robust Adaptive Scheduling Improving Adaptive Scheduling Rescheduling Changes to the Model Adaptive Memory Access Adaptive Rescheduling Comparison with other Approaches Summary 177

8 viii ADAPTIVE SEARCH IN LOGISTICS SYSTEMS 9. ADAPTIVE AGENTS AT WORK 1. A Case Study from Industry 1.1 The Production Environment 1.2 An Automated Scheduling System 1.3 Design of Adaptive Agents 1.4 Simulation Study 1.5 Discussion 2. On-Line Systems 2.1 Production Control 2.2 Model of Manufacturing Systems 2.3 Design of Adaptive Agents 2.4 Simulation Study 2.5 Discussion 3. Summary Epilogue References Index 217

9 Preface Global competition and growing costumer expectations force industrial enterprises to reorganize their business processes and to support cost-effective customer services. Realizing the potential savings to be gained by exacting customer-delivery processes, logistics is currently subject to incisive changes. This upheaval aims at making competitive advantage from logistic services instead of viewing them simply as business necessity. With respect to this focus logistics management comprises the process of planning, implementing, and controlling the efficient, effective flow and storage of goods and services, and related information from point of origin to point of consumption for the purpose of conforming customer requirements I. This definition implies a holistic view on the logistic network, where the actors are suppliers, manufacturers, stock keepers, shipping agents, distributors, retailers and finally consumers. The flow of goods along the supply chain considers raw-materials, work-in-process parts, intermediate and finished products, and possibly waste. The prevailing management of logistics operation is driven by aggregated forecasting of these material flows. Modern logistics management propagates a disaggregated view of the material flow in order to meet the precise requirements at the interface between actors in the supply chain. Replacing aggregated information by detailed values establishes the prerequisites for an integrated process planning which goes for the shift from anticipatory towards responsebased logistic81. Smaller units of goods are considered at shorter periods for planning, implementing and controlling the material flow. From Icf. the Council of Logistics Management (1995). 2cf. Bowersox (1999).

10 x ADAPTIVE SEARCH IN LOGISTICS SYSTEMS the detached view of an actor this demands a decentralized process replanning in order to adjust its daily logistics operation. From an overall view, bounded anticipation and reactive behavior of actors are locally combined which is assumed to improve the overall operation in the entire network. Due to great advances in communication and transportation technologies, detailed information of material flows can often be exchanged immediately between the actors in a supply chain. This is undoubtedly the most important source contributing to the design of modern logistic systems by opening a way towards collaborative planning along the entire network. Next to advances in communication and transport, computer-based decision-support systems have fundamentally changed the operations management of logistic processes. At their early beginning these systems were mainly used to minimize the transportation and inventory-holding costs. Based on the available infoimation about detailed material flows, these systems can further support the order information management, the warehouse operation and the customer service administration today. Extending the focus of logistic decision support towards this direction eventually pursues the strategic objective of logistics management, namely to reduce the system-wide costs across the whole logistics network. At their core decision-support systems have models and algorithms which carry the variables for planning, implementing and controlling the flow of goods. Often the variables are determined by simple heuristics which provide decisions based on rules of thumb. In advanced decisionsupport systems, tailored methods of Operations Research are applied to optimize the logistic processes. Unfortunately, such algorithms take advantage from aggregate data structures which are in danger to become useless by only slight modifications of goals or constraints noticed under a detailed view of material movements. Reactive decision making based on bounded anticipation is therefore hindered by the current state of methodological support. In order to enable the shift from anticipatory logistics operation towards response-based logistics operation, robust algorithms capable of quickly processing detailed data in frequently changing environments are highly desirable. This work aims to widen the methodological support for managing supply chains into this direction. Our approach focuses on a class of techniques to simulate decision making processes which are based on external feedback. The first descriptions of such adaptive systems came from biology. In that context adaptation designates an evolution process which progressively modifies a structure in order to gain better performance. For more than two decades this principle has

11 Preface xi received tremendous attention in economics, computer sciences, engineering and other fields. Striking problem-solving capacities and versatility coupled with the convenience to simulate an evolution process have quickly spurred the application of so called Evolutionary Algorithms to a wide range of problems. Evolutionary Algorithms essentially are adaptive systems. They are able to change the behavior of a system in respond to changes in their environment. While we can run them as long as we like, they offer a solution at any time. For this reason Evolutionary Algorithms can be efficiently used to support reactive problem-solving of actors in the logistics network. Following modern terminology, we refer to Evolutionary Algorithms as adaptive or learning agents. Different to other software artifacts also designated as agents in the current literature, our adaptive agents do not communicate directly by exchanging messages. They communicate by mutually changing details of the common environment they perceive. The availability of shared information enables this collaborative planning of material movements between adjacent actors in the supply chain. The book is divided into two parts. Part I lays down the fundamentals of evolutionary adaptive search before an architectural model for adaptive agents is developed. Part II is devoted to applications of adaptive agents for planning and scheduling of logistic activities. These processes have often extremely difficult to solve optimization problems at their core. By focusing on elementary yet complex operations, such as grouping, routing, packing and scheduling, it is hoped to glean sufficient details of realistic problems. Christian Bierwirth Bremen, August 1999

12 Acknowledgments This book is the outcome of my research done at the faculty of economics, University of Bremen, Germany, from 1993 to There are several people who accompanied the work over the years. The holder of the chair of logistics at the University of Bremen, Herbert Kopfer, kindly invited me to discuss many of the ideas which are captured in the book. Hans-Dietrich Haasis (University of Bremen), Hermann Gehring (University of Hagen) and Richard Vahrenkamp (University of Kassel) undertake an assessment of the first version of the text. My former colleagues Klaus Schebesch, Thomas Utecht, Ivo Rixen and Annette Blome were involved with me in several research and application-oriented projects, which served as valuable input for some of the chapters in hand. However, to lay down the book took much more time than I ever had planned before. If it is amenable to a broader audience today, this is due to my friend and colleague Dirk Mattfeld who encouraged me to revise my schedule countless times. I would like to thank all people mentioned above very, very much.