Springer multi objective management in freight logistics increasing capacity service level and safety with optimization algorithms nov 2008 ISBN 1848003811 pdf

Multi-objective Management in Freight Logistics Massimiliano Caramia • Paolo Dell’Olmo Multi-objective Management in Freight Logistics Increasing Capacity, Service Level and Safety with Optimization Algorithms 13 Massimiliano Caramia, PhD Università di Roma “Tor Vergata” Dipartimento di Ingegneria dell’Impresa Via del Politecnico, 00133 Roma Italy Paolo Dell’Olmo, PhD Università di Roma “La Sapienza” Dipartimento di Statistica, Probabilità e Statistiche Applicate Piazzale Aldo Moro, 00185 Roma Italy ISBN 978-1-84800-381-1 e-ISBN 978-1-84800-382-8 DOI 10.1007/978-1-84800-382-8 British Library Cataloguing in Publication Data Caramia, Massimiliano Multi-objective management in freight logistics : increasing capacity, service level and safety with optimization algorithms Freight and freightage - Mathematical models Freight and freightage - Management Business logistics I Title II Dell'Olmo, Paolo, 1958388'.044'015181 ISBN-13: 9781848003811 Library of Congress Control Number: 2008935034 © 2008 Springer-Verlag London Limited Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency Enquiries concerning reproduction outside those terms should be sent to the publishers The use of registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made Cover design: eStudio Calamar S.L., Girona, Spain Printed on acid-free paper springer.com Preface The content of this book is motivated by the recent changes in global markets and the availability of new transportation services Indeed, the complexity of current supply chains suggests to decision makers in logistics to work with a set of efficient (Paretooptimal) solutions, mainly to capture different economical aspects that, in general, one optimal solution related to a single objective function is not able to capture entirely Motivated by these reasons, we study freight transportation systems with a specific focus on multi-objective modelling The goal is to provide decision makers with new methods and tools to implement multi-objective optimization models in logistics The book combines theoretical aspects with applications, showing the advantages and the drawbacks of adopting scalarization techniques, and when it is worthwhile to reduce the problem to a goal-programming one Also, we show applications where more than one decision maker evaluates the effectiveness of the logistic system and thus a multi-level programming is sought to attain meaningful solutions After presenting the general working framework, we analyze logistic issues in a maritime terminal Next, we study multi-objective route planning, relying on the application of hazardous material transportation Then, we examine freight distribution on a smaller scale, as for the case of goods distribution in metropolitan areas Finally, we present a human-workforce problem arising in logistic platforms The general approach followed in the text is that of presenting mathematics, algorithms and the related experimentations for each problem Rome, May 2008 Massimiliano Caramia Paolo Dell’Olmo v Acknowledgements The authors wish to thank Eugenia Civico, Isabella Lari, Alessandro Massacci and Maria Grazia Mecoli for their contribution in the implementation of the models in Chapter 3; Pasquale Carotenuto, Monica Gentili and Andrea Scozzari for their contribution to the hazmat project to which Chapter refers; Monica Gentili and Pitu Mirchandani as co-authors of previous work related to Chapter vii Contents List of Figures xiii List of Tables xv Introduction 1.1 Freight Distribution Logistic Multi-objective Optimization 11 2.1 Multi-objective Management 11 2.2 Multi-objective Optimization and Pareto-optimal Solutions 12 2.3 Techniques to Solve Multi-objective Optimization Problems 14 2.3.1 The Scalarization Technique 15 2.3.2 ε -constraints Method 18 2.3.3 Goal Programming 21 2.3.4 Multi-level Programming 22 2.4 Multi-objective Optimization Integer Problems 25 2.4.1 Multi-objective Shortest Paths 27 2.4.2 Multi-objective Travelling Salesman Problem 32 2.4.3 Other Work in Multi-objective Combinatorial Optimization Problems 33 2.5 Multi-objective Combinatorial Optimization by Metaheuristics 34 Maritime Freight Logistics 37 3.1 Capacity and Service Level in a Maritime Terminal 37 3.1.1 The Simulation Setting 40 ix x Contents 3.1.2 The Simulation Model 43 3.1.3 Simulation Results Analysis 47 3.2 Final Remarks and Perspectives on Multi-objective Scenarios 50 3.3 Container Allocation in a Maritime Terminal and Scheduling of Inspection Operations 51 3.3.1 Containers Allocation in a Maritime Terminal 52 3.3.2 Formulation of the Allocation Model 54 3.4 Scheduling of Customs Inspections 56 3.5 Experimental Results 60 Hazardous Material Transportation Problems 65 4.1 Introduction 65 4.2 Multi-objective Approaches to Hazmat Transportation 68 4.2.1 The Problem of the Risk Equity 69 4.2.2 The Uncertainty in Hazmat Transportation 70 4.2.3 Some Particular Factors Influencing Hazmat Transportation 71 4.2.4 Technology in Hazmat Transportation 71 4.3 Risk Evaluation in Hazmat Transportation 72 4.3.1 Risk Models 72 4.3.2 The Traditional Definition of Risk 73 4.3.3 Alternative Definition of Risk 75 4.3.4 An Axiomatic Approach to the Risk Definition 77 4.3.5 Quantitative Analysis of the Risk 78 4.4 The Equity and the Search for Dissimilar Paths 80 4.4.1 The Iterative Penalty Method 80 4.4.2 The Gateway Shortest-Paths (GSPs) Method 81 4.4.3 The Minimax Method 83 4.4.4 The p-dispersion Method 84 4.4.5 A Comparison Between a Multi-objective Approach and IPM 87 4.5 The Hazmat Transportation on Congested Networks 89 4.5.1 Multi-commodity Minimum Cost Flow with and Without Congestion 91 4.5.1.1 4.5.2 The Models Formulation 91 Test Problems on Grid Graphs 95 Contents xi 4.5.3 The Linearized Model with Congestion 97 4.6 The Problem of Balancing the Risk 98 4.6.1 Problem Formulation 98 4.7 Bi-level Optimization Approaches to Hazmat Transportation 100 Central Business District Freight Logistic 103 5.1 Introduction 104 5.2 Problem Description 105 5.2.1 Mathematical Formulation 107 5.3 Solution Strategies 111 5.3.1 Experimental Results 112 5.4 Conclusions 119 Heterogeneous Staff Scheduling in Logistic Platforms 121 6.1 Introduction 121 6.2 The Heterogeneous Workforce Scheduling Problem 126 6.3 Constraints and Objective Functions of the CHWSP 126 6.3.1 Constraints 127 6.3.2 Objective Functions 128 6.4 Simulated Annealing: General Presentation 131 6.4.1 The Length of the Markov Chains 133 6.4.2 The Initial Value of the Control Parameter 134 6.4.3 The Final Value of the Control Parameter 135 6.4.4 Decrement of the Control Parameter 135 6.5 Our Simulated Annealing Algorithm 137 6.5.1 Procedure for Determining the First Value of the Control Parameter 137 6.5.2 Equilibrium Criterion and Stop Criterion 138 6.5.3 Decrement Rule for the Control Parameter 138 6.5.4 Function Perturb(si → s j ) 139 6.6 Experimental Results 140 6.6.1 Presentation of the Experiments and Implementation Details 140 6.6.2 Analysis of the Experiments 141 6.6.3 Computational Times 146 6.7 Conclusions 147 xii Contents A AMPL Code: The Container-allocation Problem 149 B AMPL Code: The Inspection Scheduling Problem 151 C Code in the C Language: The Iterative Penalty Method Algorithm 153 D Code in the C Language: The P-Dispersion Algorithm 163 References 175 Index 187 D Code in the C Language: The P-Dispersion Algorithm float minimum; minimum=10.0; for (h=1;h