GOR ■ Publications Managing Editor Kolisch, Rainer Editors Burkard, Rainer E Fleischmann, Bernhard Inderfurth, Karl Möhring, Rolf H Voss, Stefan Titles in the Series H.-O Günther and P v Beek (Eds.) Advanced Planning and Scheduling Solutions in Process Industry VI, 426 pages 2003 ISBN 3-540-00222-7 Jörn Schönberger Operational Freight Carrier Planning Basic Concepts, Optimization Models and Advanced Memetic Algorithms With 43 Figures and 24 Tables 123 Dr Jörn Schönberger University of Bremen Lehrstuhl für Logistik Fachbereich 07 Wilhelm-Herbst-Straße 28359 Bremen Germany E-mail: sberger@logistik.uni-bremen.de Library of Congress Control Number: 2005922933 ISBN 3-540-25318-1 Springer Berlin Heidelberg New York This work is subject to copyright.All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag.Violations are liable for prosecution under the German Copyright Law Springer is a part of Springer Science+Business Media springeronline.com © Springer-Verlag Berlin Heidelberg 2005 Printed in Germany The use of general descriptive names, 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 protective laws and regulations and therefore free for general use Cover design: Erich Kirchner Production: Helmut Petri Printing: Strauss Offsetdruck SPIN 11407584 Printed on acid-free paper – 42/3153 – Preface This book represents the compilation of several research approaches on operational freight carrier planning carried out at the Chair of Logistics, University of Bremen It took nearly three years from the first ideas to the final version, now in your hands During this time, several persons helped me all the time to keep on going and to re-start when I got stuck in a dead end or when I could not see the wood for the trees I am deeply indebted to them for their encouragement and comments Prof Dr Herbert Kopfer, holder of the Chair of Logistics, introduced me into the field of operational transport planning He motivated and supervised me Furthermore, he supported me constantly and allowed me to be as free as possible in my research and encouraged me to be as creative as necessary In addition, I have to thank Prof Dr Hans-Dietrich Haasis, Prof Dr Martin G Mohrle and Prof Dr Thorsten Poddig On behalf of all my colleagues, who supported me in numerous ways, I have to say thank you to Prof Dr Dirk C Mattfeld, Prof Dr Christian Bierwirth, Henner Gratz, Prof Dr Elmar Erkens, Nadja Shigo and Katrin Dorow They all helped me even with my most obscure and dubious problems My family supported me all the time They always showed me their trust and encouraged me continuously Special thanks are dedicated to my parents Monika and Heinz-Jiirgen However, there is somebody who helped and supported me much more than any other person It's my beloved wife Ilka She believes in me more often than I beheve in myself But more importantly, she periodically rescues me from the jungle of science and guides my attention to other wonderful aspects of life Thank you very much Bremen, January 2005 Jorn Schonberger Contents Transport in F'reight Carrier Networks I 1.1 Recent Trends in Freight Transportation 1.2 Carrier Tkansport Networks 1.3 Network Design, Configuration and Deployment 1.4 Distribution and Collection Planning 11 1.5 Aims of this Book and Used Methods 13 Operational Freight Transport Planning 2.1 Decision Problems 2.1.1 Request Acceptance 2.1.2 Mode Selection 2.1.3 Routing 2.1.4 Freight Optimization 2.2 Hierarchical and Simultaneous Planning 2.2.1 Hierarchical Approach 2.2.2 Simultaneous Routing and Freight Optimization 2.3 Generic Models for Simultaneous Problems 2.3.1 Maximal-Profit Selection 2.3.2 Bottleneck Selection 2.3.3 Selection with Compulsory Requests 2.3.4 Selection with Postponement 2.4 Conclusions 15 16 16 17 19 20 22 22 23 24 25 25 26 27 29 Pickup and Delivery Selection Problems 3.1 Problems with Pickup and Delivery Requests 3.1.1 Problems with Depot-Connected Requests 3.1.2 Problems with Direct Delivery Requests 3.1.3 Simultaneous Problems 3.2 Pickup and Delivery Paths and Schedules 3.3 Optimization Problem 3.4 Problem Variants 31 31 33 33 34 34 36 37 VIII Contents 3.4.1 The PDSP with LSP Incorporation 3.4.2 The Capacitated PDSP 3.4.3 The PDSP with Compulsory Requests 3.4.4 The PDSP with Postponement 3.5 Test Case Generation 3.5.1 Generation of Pickup and Delivery Requests 3.5.2 Freight Tariff 3.5.3 Benchmark Suites 3.6 Conclusions 38 39 39 40 42 42 45 46 48 Memetic Algorithms 49 4.1 Algorithmic Solving of Problems with PD-Requests 49 4.2 Evolutionary Algorithms 52 4.3 Genetic Algorithms 55 4.3.1 Terminus Technici 55 4.3.2 General Framework 56 4.3.3 Applicability of Genetic Search 57 4.3.4 Limits of the Genetic Search 58 4.4 Repairing and Improving the Genetic Code 60 4.5 Conclusions 64 Memetic Algorithm Vehicle Routing 5.1 Genetic Sequencing 5.2 Genetic Clustering 5.3 Combined Genetic Sequencing and Clustering 5.4 Advanced MA-Approaches: The State-of-the-Art 5.4.1 Multi-Chromosome Memetic Algorithms 5.4.2 Co-Evolution with Specialization 5.4.3 Co-Evolution of Partial Solutions 5.5 Conclusions Memetic Search for Optimal PD-Schedules 77 6.1 Permutation-Controlled Schedule Construction 78 6.1.1 Construction of Routes for more than one Vehicle 78 6.1.2 Parallel Time-Window-Based Routing 78 6.1.3 Algorithm Steps 79 6.1.4 Determination of the Request Instantiation Order 84 6.2 Representation of a PD-Schedule 84 6.3 Configuration of the Memetic Algorithm 85 6.3.1 Initial Population 85 6.3.2 Recombination 86 6.3.3 Mutation 90 6.3.4 Population Model 92 6.4 Computational Experiments 93 6.4.1 Parameterization of the MA 94 65 65 68 71 71 72 74 75 76 Contents IX 6.4.2 Impacts of Spatial Distribution and Time Window Tightness 97 6.4.3 Identification of Profit-Maximum Request Selections 100 6.4.4 Consideration of Capacity Limitations 102 6.4.5 Identification of Deferrable Requests 109 6.5 Conclusions 113 Coping with Compulsory Requests 115 7.1 Limits of Fitness Penalization 116 7.1.1 Static Penalties 116 7.1.2 Dynamically Determined Penalties 118 7.1.3 Adaptive Penalization 119 7.2 A Double-Ranking Approach 120 7.3 Converging-Constraint Approach 121 7.3.1 Alternating and Converging Constraints 121 7.3.2 ACC-Algorithm Control 124 7.4 Assessing QC-MA and ACC-MA: Numerical Results 125 7.4.1 Experimental Setup 125 7.4.2 Numerical Results 125 7.4.3 Impacts of Intermediate Cost Reductions: An Example 130 7.5 Conclusions 133 Request Selection and Collaborative Planning 135 8.1 The Portfolio Re-composition Problem 136 8.1.1 Literature Review 136 8.1.2 Formal Problem Statement 137 8.2 Configuration of the Groupage System 139 8.2.1 Bundle Specification by the Carriers 140 8.2.2 Bundle Assignment by the Mediator 140 8.3 Computational Experiments 141 8.3.1 Test Cases 142 8.3.2 Collaborative Planning Approach 142 8.3.3 Reference Approach 143 8.3.4 Results 143 8.4 Conclusions 147 Conclusions 149 9.1 Understanding Freight Carrier Decision Problems 149 9.2 Model Building 150 9.3 Methodological Enhancements 151 References 153 Index 161 Transport in Freight Carrier Networks The division of labor among the continents, countries or regions over the world enables the production of goods in the most efficient manner Goods are produced at different locations so that the overall costs are minimized The manufacture of a certain product often concentrates on few places in a region, a country, a continent or even in the world However, the demand for the products manufactured at certain locations in an economic zone is typically scattered over the complete zone In order to satisfy this demand with the centrally produced goods, extensive transport is needed Transport describes the spatial transformation of goods or persons with the goal of balancing supply and demand The increase of goods transport is accompanied by a significant extension of passenger transport The movement of manpower to the centralized production facilities becomes necessary and additionally, the enlarged incomes are used for private travel In Sect 1.1of this chapter, the economic importance of freight transport is explored Some current trends, from which the demand for a reinforced planning arises, are shown by means of the examples European Union (EU) and United States (US) In Sect 1.2 the structure of a freight carrier network and the transport processes in such a network are analyzed Planning problems regarding the design, configuration and deployment of the transport system are discussed in Sect 1.3 The distribution and collection of freight from providers or suppliers to a consolidation facility, and in the reverse direction, is identified as a very critical phase of the transport and the need for additional planning support is emphasized in Sect 1.4 The goals and the organization of this thesis are given in Sect 1.5 1.1 Recent Trends in Freight Transportation The commonly used indicator for the performance of the goods transport sector is the amount of realized ton-kilometers (tkm) expressing the product of Conclusions To conclude this thesis, the main answers found for the three research topics mentioned in the introduction of this book, are summarized In 9.1, the main results on the analysis of the short-term freight carrier planning problems are presented In 9.2, the ideas for modeling simultaneous routing and freight optimization problems are summarized and in 9.3 the presented extensions of the memetic search method are listed For each topic further research requirements are pointed out 9.1 Understanding F'reight Carrier Decision Problems The geographical distribution of customer locations and the unpredictable demand for transport means that the operational short-term planning of a freight carrier company is very important The local collection and distribution activities from the customers to local transshipment and consolidation facilities and vice versa cannot be pre-determined in advance within long-lasting schedules The small quantities on the initial and last leg of a transport served by the freight carrier not justify the installation of repetitive itineraries Unbalanced demand over the long run in the local areas lead to bottleneck situations in which the carrier-owned fleet cannot serve all requests in a reliable manner Subcontractors (LSPs) are ordered to fulfill those requests The fulfillment mode is determined for each request: it is decided whether a request is given to an LSP or not All requests have to be considered simultaneously, a sequential treatment of the requests leads to inappropriate mode selections The derivation of the fulfillment mode requires the solving of a simultaneous model in which the benefits of both modes are compared To evaluate the sense of using own equipment, a routing and scheduling problem has to be solved and for evaluating the costs of subcontractor incorporation, a freight optimization problem requires solving Thus, the operational freight carrier planning problem is a composed vehicle routing and freight optimization problem The two previously studied problems are coupled by the (bi- 150 Conclusions nary) mode decisions for each request Freight carrier planning problems bring two so far separately considered problem classes together As soon as a sequence of consecutive planning periods is considered, additional benefits can be realized by selecting the most promising period for the fulfillment of a request The implications and benefits or problems associated with the postponement or acceleration of the execution have only been initially studied in this thesis Further research effort should be spent on this topic in order to investigate the symbiosis of request sub-contraction and postponement or acceleration of request completions 9.2 Model Building The setup of decision models for a freight carrier planning problem requires the merging of models for vehicle routing and scheduling problems, and for the freight optimization Besides the representation of the decisions within these submodels, additional decisions that couple both submodels have to be coded For each single request an additional coupling decision variable is required It is set true if, and only if, the corresponding request is served by carrier-owned equipment, and it is set false if, and only if, an LSP is ordered to serve this request Three models with binary coupling decisions variables have been presented In the Pickup and Delivery Selection Problem with Logistics Service Provider Incorporation (PDSPLSP), the costs of both modes are calculated for the requests If the self-fulfillment is cheaper than the LSP incorporation, then the corresponding requests are inserted into the routes of the own equipment Otherwise, LSPs are ordered to serve the mentioned requests A knapsack-type constraint hinders the determination of the cheapest mode for each request in the Capacitated Pickup and Delivery Selection Problem (CPSDP) Since the capacity of the own fleet is scarce, some requests have to be given to an LSP In the Pickup and Delivery Problem with Compulsory Requests (PDSPCR), the mode for the compulsory requests cannot be modified These requests cannot be given to an LSP Each of the three models represents a generic modeling approach In the PDSPLSP, the determination of the coupling variables is unconstrained and their instantiation is performed subject to the evaluation of the corresponding modes The knapsack constrained in the CPDSP prevents the selection of the true values for all coupling variables In the PDSP-CR the predetermination of the values for the decision variables associated with the compulsory requests forbids the other extreme solution that all binary coupling variables are set to false The main problem in the Pickup and Delivery Selection Problem with Postponement (PDSP-PP) is to determine the monetary value of a postponement or acceleration of a request execution If the postponement opportunity 9.3 Methodological Enhancements 151 should be applied in a dynamic scenario more effort should be spent on the valuation of this third decision possibility The second interesting topic is the investigation of impacts of different and more realistic freight tariffs for the LSP incorporation The first proposals of Pankratz (2002) should be incorporated into the four derived basic models in order to bring the so far academic models closer to real world applications 9.3 Methodological Enhancements The proposed Memetic Algorithms are able to solve the instances of the pickup and delivery selection problems The solution quality is convincing To cope with the intricate constraints, some extensions of the memetic search paradigm have been successfully implemented So far, these additional features have not received special attention in the scientific literature The abandonment of the string-based representation and the usage of the problem-specific structure-base representation have proven their applicability This motivates the application of the memetic approach to problems for which a string-based representation is not available However, the definition of the required problem specific search operators remains a very challenging task The introduction and successful application of the alternating and converging constraint memetic algorithm (ACC-MA) represents a new idea for handling constraints that are not accessible for the other feasibilityachieving and -preserving techniques, such as penalization or repairing This method should be applied to other combinatorial optimization problems with complicated constraints However, the main problem of the memetic search paradigm is that it is missing scalability The computational effort for the determination of the initial population, for the calls of the repair and the improvements procedures leads to unattractive running times The solved benchmark problems contains only between 50 and 60 requests Real-world applications have to cope with significantly larger instances Considering the running time observed so far, it can be expected that the memetic search paradigm is inappropriate for larger instances and the configuration of another meta-strategy should be taken into account Nevertheless the developments of the memetic search presented in this thesis are promising for a variety of similar applications If the computational effort in the hill-climber calls can be reduced, then Memetic Algorithms are very promising and will remain comparable to other metaheuristics References Aarts, E., Korst, J., van Laarhoven, P (1997) Simulated annealing In: Aarts and Lenstra (1997), pp 91-120 Aarts, E., Lenstra, J (Eds.) (1997) Local Search in combinatorial Optimization John Wiley & Sons Alander, J (1999) An indexed bibliography of genetic algorithm implementations Tech Rep 94-1-IMPLE, University of Vaasa Alvarenga, G., Mateus, G., de Tomi, G (2003) Finding near optimal solutions for vehicle routing problems with time windows using hybrid genetic algorithm Tech rep., Manuscript presented at Odysseus 2003 Conference Angelelli, E., Mansini, R (2002) The vehicle routing problem with time windows and simultaneous pick-up and delivery In: Klose, A., Speranza, M G., van Wassenhove, L (Eds.), Quantitative Approaches o Distribution Logistics and Supply Chain Management No 519 in Lecture Notes in Economics and Mathematical Systems Springer, pp 249-267 Arendt, M., Achermann, Y (2002) Aggregierte Verkehrsprognosen Schweiz und EU - Zusammenstellung vorhandener Prognosen bis 2020 Tech rep., Bundesamt fiir Raumentwicklung Back, T., Fogel, D., Michalewicz, Z (Eds.) (2000a) Evolutionary Computation Basic Algorithms and Operators Institute of Physics Publishing Back, T., Fogel, D., Michalewicz, Z (Eds.) (2000b) Evolutionary Computation Advanced Algorithms and Operators Institute of Physics Publishing Baker, B., Ayechew, M (2003) A genetic algorithm for the vehicle routing problem Computers & Operations Research 30, 787-800 Beasley, D (2000) Possible applications of evolutionary computation In: Back et al (2000a), pp 4-19 Beasley, D., Bull, D., Martin, R (1993) Reducing epistasis in combinatorial problems by expansive coding In: Forrest, S (Ed.), Proceeding of the Fifth Intern* tional Conference on Genetic Algorithms Morgan Kaufmann Berger, J., Barkaoui, M (2002) A memetic algorithms for the vehicle routing problem with time windows In: Proceedings of 7th International Command and Control Reserach and Technology Symposium 154 References Bierwirth, C., Mattfeld, D., Kopfer, H (1996) On permutation representations for scheduling problems In: Ebeling, W., Rechenberg, I., Schwefel, H., Voigt, H (Eds.),Proceedings of Parallel Problem Solving from Nature IV Springer-Verlag Blanton, J., Wainwright, R (1993) Multi vehicle routing with time and capacity constraints using genetic algorithms In: Forrest, S (Ed.), Proceedings of the Fifth International Conference On Genetic Algorithms Morgan Kaufmann Publishers, Inc Blum, C., Roli, A (2001) Metaheuristics in combinatorial optimization: Overview and conceptual comparison Tech Rep TR/IRIDIA/2001-3, IRIDIA Braysy, (2001) Genetic algorithms for the vehicle routing problem with time windows Arpakannus 1, 33-38 Burke, E., Newall, J., Weare, R (1996) A memetic algorithm for university exam timetabling In: Burke, E., Ross, P (Eds.), Proceedings of First International Conference of Practice and Theory of Automated Timetabling Vol 1153 of Lecture Notes in Computer Science Springer Butt, S., Cavalier, T (1994) A heuristic for the multiple tour maximum collection problem Computers & Operations Research 21 (I),101-111 Butt, S., Ryan, D (1999) An optimal solution procedure for the multiple tour maximum collection problem using column generation Computers & Operations Research 26, 427441 Casco, D., Golden, B., Wasil, E (1988) Vehicle routing with backhauls: Models, algorithms and case studies In: Golden and Assad (1988), pp 127-147 Chao, I.-M., Golden, B., Wasil, E (1996) The team orienteering problem European Journal of Operational Research 88, 464-474 Christofides, N (1990) Vehicle routing In: E.L Lawler et al (1990), pp 431-448 Cloonan, J (1966) A heuristic approach to some sales territory problems In: Hertz, D., Melese, J (Eds.), Proceedings of the Fourth International Conference on Operational Research Wiley-Interscience Coello, C C (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art Computational met hods in applied mechanics and engineering 191, 1245-1287 Coit, D., Smith, A., Tate, D (1996) Adaptive penalty methods for genetic optimization of constrained combinatorial problems INFORMS Journal on Computing (2), 173-182 Cordeau, J.-F., Laporte, G (2003) A tabu search heuristic for the static multivehicle dial-a-ride-problem Transportation Research Part B 37, 579-594 Crainic, T (2000) Service network design in freight transportation European Journal of Operational Research 122, 272-288 Crainic, T (2003) Handbook of Transportation Science, 2nd Edition Kluwer, Ch Long-haul Freight Transportation, pp 451-516 Crainic, T., Laporte, G (1997) Planning models for freight transportation European Journal of Operational Research 97, 409-438 Daganzo, C (1999) Logistic System Analysis, 3rd Edition Springer de Queir6s Vieira Martins, E., Pascoal, M B., Rasteiro, D L D., dos Santos, J (1999) The optimal path problem Investiga@o Operacional 19, 43-60 Desrosiers, J., Dumas, Y., Soumis, F (1998) The multi vehicle dial-a-ride problem In: Daduna, J., Wren, A (Eds.), Proceedings of Computer-Aided Transit Scheduling Vol 308 of Lecture Notes in Economics and Mathematical System References 155 Dethloff, J (1994) Verallgemeinerte Tourenplanungsprobleme Ph.D thesis, Universitat Hamburg Diaby, M., Ramesh, R (1995) The distribution problem with carrier service: A dual based penalty approach ORSA Journal on Computing ( I ) , 24-35 Doerner, K., Hartl, R., Reimann, M (2000) Ants solve time constraint pickup and delivery problems with full truckloads In: Fleischmann, B., Lasch, R., Derigs, U., Domschke, W., Rieder, U (Eds.) , Operations Research Proceedings 2000 Springer, pp 395-400 Domenjoud, E., Kirchner, C., Zhou, J (1998) Generating feasible schedules for a pick-up and delivery problem In: Proceedings of the 4th International Conference of Principles and Practice of Constraint Programming Vol 1520 of Lecture Notes in Computer Science Domschke, W (1985) Logistik: Rundreisen und Touren, 2nd Edition Oldenbourg Dorigo, M., Caro, G D., Gambardella, L (1999) Ant algorithms for discrete optimization Artificial Life (2), 137-172 Dowsland, K (19%) Simulated annealing In: Reeves (l993), pp 20-69 Duhamel, C., Potvin, J., Rousseau, J (1997) A tabu search heuristic for the vehicle routing problem with backhauls and time windows Transportation Science 31 ( I ) , 49-59 Dumas, Y., Desrosiers, J., Soumis, F (1991) The pickup and delivery problem with time windows European Journal of Operational Research 54, 7-22 E.L Lawler, J.K Lenstra, A.H.G Rinnooy Kan, D.B Shmoys (Eds.) (1990) The Traveling Salesman Problem, 4th Edition Wiley Interscience Erkens, E (1998) Kostenbasierte Tourenplanung im Straengiiterverkehr Ph.D thesis, Universitat Bremen Erkut, E., Zhang, J (1996) The maximum collection problem with time-dependent rewards Naval Research Logistics 43, 749-763 Eshelman, L (2000) Genetic algorithms In: Back et al (2000a), pp 64-80 European Commission (2001) White paper - european transport policy for 2010: time to decide Eurostat (2002) The eu energy and transport in figures statistical pocket book Feillet, D., Dejax, P., Gendreau, M (2001) The selective traveling salesman problem and extensions: An overview Tech Rep CRT-2001-25, CRT Fenn, M (Ed.) (2004) Transportation Statistics Annual Report U S Department of Transportation Bureau of Transportation Statistics Fiat, A., Woeginger, G (Eds.) (1998) Online Algorithms Vol 1442 of Lecture Notes in Computer Science Springer Fleischmann, B (1998) Design of freight traffic networks In: Fleischmann, B., van Nunen, J., Speranza, M G., Stahly, P (Eds.), Advances in Distribution Logistics Springer, pp 55-81 Fleischmann, B., Gnutzmann, S., SandvoB, E (2003) Dynamic vehicle routing based on on-line traffic information t o appear in Transportation Science Fogel, D (2000) Introduction to evolutionary computation In: Back et al (2000a), pp 1-3 Frantzeskakis, L., Powell, W (1990) A successive linear approximation procedure for stochastic, dynamic vehicle allocation problems Transportation Science 24 ( I ) , 40-57 156 References Gambardella, L., Taillard, E., Agazzi, G (1999) Macs-vrptw: A multiple ant colony system for vehcle routing problems with time windows Tech Rep IDSIA-06-99, IDSIA Gendreau, M., Laporte, G., Semet, F (1998) A branch-and-cut algorithm for the undirected selective traveling salesman problem Networks 32 (4), 263-279 Gensch, D (1978) An industrial application of the traveling salesman's subtour problem AIIE Transactions 10 (4), 362-370 Ghiani, G., Guerriero, F., Laporte, G., Musmanno, R (2003) Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies European Journal of Operational Research 151, 1-11 Glover, F., Laguna, M (1993) Tabu search In: Reeves (1993), pp 70-150 Goldberg, D (1989) Genetic Algorithm in Search, Optimization & Machine Learning Addison Wesley Publishing Company Golden, B., Assad, A (Eds.) (1988) Vehicle Routing: Methods and Studies Elsevier Science Publishers B.V Golden, B., Assad, A., Dahl, R (1984) Analysis of a large scale vehicle routing problem with an inventory component Large Scale Systems 7, 181-190 Golden, B., Levy, L., Dahl, R (1981) Two generalizations of the traveling salesman problem OMEGA (4), 439-441 Golden, B., Stewart, W (1985) Empirical analysis of heuristics In: E.L Lawler et al (1990), pp 207-249 Gomber, P., Schmidt, C., Weinhardt, C (1997) Elektronische Markte fiir dezentrale Transportplanung Wirtschaftsinformatik 39 (2), 137-145 Greb, T (1998) Interaktive Tourenplanung mit Tabu Search Ph.D thesis, Universitt Bremen Griinert, T., Sebastian, H.-J (2000) Planning models for long-haul operations of postal and express shipment companies European Journal of Operational Research 122, 289-309 Kerry, M (2001) Transportpreise und Transportkosten der verschiedenen Verkehrstrager im Giiterverkehr Tech Rep 14, Kammer fur Arbeiter und Angestellte, Abt Umwelt und Verkehr Hertz, A., Taillard, E., de Werra, D (1997) Tabu search In: Aarts and Lenstra (1997), pp 121-136 Ibaraki, T (1997) Combination with local search In: Back, T., Fogel, D., Michaelwicz, Z (Eds.), Handbook of Evolutionary Computation Institute of Physics Publishing, p D3.2 IGF (Ed.) (2002) Economic Effects of Transportation: The Freight Story ICF Consulting Joines, J., Kay, M (2002) Utilizing hybrid genetic algorithms In: Sarker et al (2002), pp 199-228 Kilger, C , Renter, B (2002) Collaborative planning In: Stadtler, H., Kilger, C (Eds.), Supply Chain Management and Advanced Planning Springer, pp 223237 Kopfer, H (1984) Losung des Frachtoptimierungsproblems im gewerblichen Giiterfernverkehr - Losungsaufwand versus Losungsqualitat Ph.D thesis, Universitat Bremen Kopfer, H (1989) Heuristische Suche in Operations Research und Kiinstlicher Intelligenz - Strategien des heuristischen Problemlosens und die Losung betrieblicher Dispositionsprobleme Habilitationsschrift References 157 Kopfer, H (1992) Konzepte genetischer Algorithmen und ihre Anwendung auf das Frachtoptimierungsproblem im gewerblichen Guterfernverkehr OR Spektrum 14, 137-147 Kopfer, H., Pankratz, G (1998) Das Groupage-Problem kooperierender Verkehrstrager In: Kall, P., Luthi, J.- J (Eds.), Operations Research Proceedings 1998 Kopfer, H., Pankratz, G., Erkens, E (1994) Entwicklung eines hybriden Genetischen Algorithmus zur Tourenplanung OR Spektrum 16, 21-31 Laporte, G., Martello, S (1990) The selective travelling salesman problem Discrete Applied Mathematics 26 Larsen, J (2001) Parallelization of the vehicle routing problem with time windows Ph.D thesis, University of Copenhagen Lau, H., Liang, Z (2001) Pickup and delivery with time windows: Algorithms and test case generation In: Proceedings of the IEEE Conference on Tools with Artificial Intelligence (ICTAI) Li, H., Lim, A (2001) A mataheuristic for the pickup and delivery problem with time windows In: proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'Ol) Machado, P., Tavares, J., Pereira, F., Costa, E (2002) Vehicle routing problem: Doing it the evolutionary way In: Proceedings on the Genetic and Evoloutionary Computation Conference Morgan Kaufmann, Inc Mattfeld, D (1996) Evolutionary Search and the Job Shop Physica-Verlag Meier-Sieden, M (1978) Die Auftragsauswahl in Betrieben des Gelegenheitsverkehrs Vol 22 of Verkehrswissenschaftliche Studien Vandenhoeck & Ruprecht in Gottingen Merz, P., Freisleben, B (2001) Memetic algorithms for the traveling salesman problem Complex Systems 13 (4), 297-345 Michaelewicz, Z (1996) Genetic Algorithms + Data Structures = Evolution Programs, 3rd Edition Springer Michaelewicz, Z (2000) Constraint-preserving operators In: Back et al (2000b), pp 62-68 Michalewicz, Z (2000a) Decoders In: Back et al (2000b), pp 49-55 Michalewicz, Z (2000b) Repair algorithms In: Back et al (2000b), pp 56-61 Millar, H (1996) Planning fish scouting activity in industrial fishing Fisheries Research 25, 63-75 Millar, H., Kiragu, M (1997) A time-based formulation and upper bounding scheme for the selective travelling salesperson problem Journal of the Operational Research Society 48, 511-518 MitroviC-MiniC, S (1998) Pickup and delivery problems with time windows: A survey Tech Rep SFU CMPT T R 1998-12, Simon Fraser University Moore, C (Ed.) (2004) National Transportation Statistics 2003 U.S Department of Transportation Bureau of Transportation Statistics Moore, W (Ed.) (2002) Freight Shipments in America U.S Department of Transportation Bureau of Transportation Statistics Moscato, P (1989) On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms Tech Rep 790, Caltech Concurrent Computation Program, California Institute of Technology 158 References Nanry, W., Barnes, J (2000) Solving the pickup and delivery problem with time windows using reactive tabu search Transportation Research Part B 34, 107121 Nash, C., Niskanen, E (2003) Experiences in road pricing in europe - review of research and practice In: Proceedings of TRIP research conference: The Economic and Environmental Consequences of Regulating Traffic www.akf.dk/trip/konference03/papers/niskanen-nash.pdf, 25.06.2003 Naudts, B., Suys, D., Verschoren, A (1997) Epistasis as a basic concept in formal landscape analysis In: Back, T (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms Morgan Kaufmann Nemhauser, G., Wolsey, L (1988) Integer and Combinatorial Optimization Wiley Oberhausen, J (2003) Trends in road freight transport 1990-2001 Tech rep., Eurostat Ochi, L., Vianna, D., Drummond, L., Victor, A (1998) A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet In: Proceedings of IPPS/SPDP Workshops 1998 Pankratz, G (2002) Dynamische speditionelle Transportdisposition unter besonderer Beriicksichtigung der Fremdvergabe Ph.D thesis, FernUniversitatGesamthochschule Hagen Pekny, J., Miller, D (1990) An exact algorithm for the resource constrained traveling salesman problem with application to scheduling with an aggregate deadline In: Eighteenth Annual Computer Science Conference ACM Pereira, F., Tavares, J., Machado, P., Costa, E (2002) GVR: a new genetic representation for the vehicle routing problem In: Proceedings of the 13th Irish Conference on Artificial Intelligence and Cognitive Science AICS2002 Porto, V (2000) Evolutionary programming In: Back et al (2000a), pp 89-102 Potvin, J.-Y., Bengio, S (1996) The vehicle routing problem with time windows part ii: Genetic search INFORMS Journal on Computing (2), 165-172 Powell, W., Sheffi, Y., Nickerson, K., Butterbraugh, K., Atherton, S (1988) Maximizing profits for north american van lines' truckload division: A new framework for pricing and operations Interfaces 18 (I), 21-41 Powell, W., Snow, W., Cheung, R.-M (2000) Adaptive labeling algorithms for the dynamic assignment problem Transportation Science 34 (I), 67-85 Psaraftis, H (1983) An exact algorithm for the single vehicle many-to-many diala-ride problem with time windows Transportation Science 17 (3), 351-357 Psaraftis, H (1995) Dynamic vehicle routing: Status and prospects Annals of Operations Research 61 Psaraftis, H N (1988) Dynamic vehicle routing problems In: Golden and Assad (1988), pp 223-248 Punakivi, M., Yrjola, H., Holmstrom, J (2001) Solving the last mile issue: Reception box or delivery box? International Journal of Physical Distribution and Logistics Management 31 (6), 427-439 Reeves, C (Ed.) (1993) Modern Heuristic Techniques for Combinatorial Problems Blackwell Scientific Publications Rego, C., Roucairol, C (1995) Using tabu search for solving a dynamic multiterminal truck dispatching problem European Journal of Operational Research 83, 411-429 References 159 Riebel, P (1986) Probleme einer entscheidungsorientierten Kosten-, Erlos- und Deckungsbeitragsrechnung im Guterkraftverkehr Zeitschrift fur Verkehrswissenschaft 57 ( I ) , 3-38 Rodrigue, J.-P (1999) Globalization and the synchronization of transport terminals Journal of Transport Geography 7, 255-261 Rothlauf, F (2003) Redundant representations in evolutionary computation, working Paper 312003 Rudolph, G (2000) Evolution strategies In: Back et al (2000a), pp 81-88 Sandholm, T (2002) Algorithm for optimal winner determination in combinatorial auctions Artificial Intelligence 135 (2), 1-54 Sarker, R., Mohammadian, M., Yao, X (Eds.) (2002) Evolutionary Optimization International Series in Operations Research and Management Science Kluwer Academic Publishers Sarma, J., Jong, K D (2000) Generation gab methods In: Back et al (2000a), pp 205-211 Savelsbergh, M., Sol, M (1995) The general pickup and delivery problem Transportation Science 29 Savelsbergh, M., Sol, M (1998) Drive: Dynamic routing of independent vehicles Operations Research 46, 474-490 Schmidt, K (1989) Die Einzelkosten- und Deckungsbeitragsrechnung als Instrument der Erfolgskontrolle und Fahrzeugdisposition im gewerblichen Guterfernverkehr Vol 22 of GVB Schriftenreihe GVB Schonberger, J., Kopfer, H (2003) A memetic algorithm for a pickup and delivery selection problem In: Proceedings of CIMCA 2003 published on CD Schonberger, J., Mattfeld, D., Kopfer, H (2004) Memetic algorithm timetabling for non-commercial sport leagues European Journal of Operational Research 153 ( I ) , 102-116 Seuring, S (2001) Die Produkt-Kooperations-Matrix im Supply Chain Managc ment Tech Rep EcoMTex-Diskussionspapier Nr 02, Universitat Oldenburg Sexton, T., Choi, Y.-M (1986) Pickup and delivery of partial loads with "soft" time windows American Journal of Mathematical and Management Sciences (3-4), 369-398 Sigurd, M., Pisinger, D., Sig, M (2000) The pickup and delivery problem with time windows and precedences Tech Rep 0018, University of Copenhagen, Dept of Computer Sciences Slater, A (2002) Specification for a dynamic vehicle routing and scheduling system International Journal of Transport Management 1, 29-40 Smith, A., Coit, D (2000) Penalty functions In: Back et al (2000b), pp 41-48 Solomon, M (1987) The vehicle routing and scheduling problems with tome window constraints Operations Research 35 (2), 254-265 Stumpf, P (1998) Tourenplanung im speditionellen Guterfernverkehr Vol 37 of GVB Schriftenreihe GVB Tasgetiren, M., Smith, A (2000) A genetic algorithm for the orienteering problem In: Proceedings of the Congress on Evolutionary Computation Tavares, J., Pereira, F., Machado, P., Costa, E (2003) On the influence of gvr in vehicle routing In: Proceedings of the ACM Symposium on Applied Computing 2003 160 References Thangiah, S (1995) Vehicle routing with time windows using genetic algorithms In: Chambers, L (Ed.), Applications Handbook of Genetic Algorithms: New Frontiers CRC Press, pp 253-278 Thangiah, S., Vinayagamoorthy, R., Gubbi, A (1995) Vehicle routing with time deadlines using genetic and local algorithms In: Forrest, S (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms Morgan Kaufmann Publishers, Inc Toth, P., Vigo, D (1999) A heuristic algorithm for the symmetric and asymmetric vehicle routing problems with backhauls European Journal of Operational Research 113, 528-543 Trip, J., Bontekoning, Y (2002) Integration of small freight flows in the intermodal transport system Journal of Transport Geography 10, 221-229 van der Bruggen, L., Lenstra, J., Schuur, P (1993) Variable-depth search for the single-vehicle pickup and delivery problem with time windows Transportation Science 27 (3), 298-311 van Laarhoven, P., Aarts, E (1987) Simulated Annealing: Theory and Applications Kluwer Academic Publishers Verweij, A., Aardal, K (2000) The merchant subtour problem Tech rep., Universiteit Utrecht Vohra, R., s de Vries (2003) Combinatorial auctions: A survey to appear in INFORMS Journal on Computing 15 Voss, S (1993) Concepts for parallel tabu search In: Proceedings on Symposium on Applied Mathematical Programming and Modeling (APMOD93) Whitley, D (2000) Permutations In: Back et al (2000a), pp 139-150 Williams, H (1993) Model Solving in Mathematical Programming, 2nd Edition John Wiley & Sons Williams, H (1999) Model Building in Mathematical Programming, 4th Edition John Wiley & Sons Withley, D., Gordon, V., Mathias, K (1994) Lamarckian evolution, the baldwin effect and function optimization In: Davidor, Y., Schwefel, H.-P., Manner, R (Eds.), Proceedings of Parallel Problem Solving from Nature - PPSN 111 No 866 in Lecture Notes in Computer Science Springer Wlcek, H (1998) Gestaltung der Giiterverkehrsnetze von Sammelgutspeditionen No 37 in GVB Schriftenreihe GVB Yao, X (2002) Evolutionary computation In: Sarker et al (2002), pp 27-53 Zhu, K Q (2000) A new genetic algorithm for vrptw, working paper, National University of Singapore Ziegelmann, M (2001) Constrained shortest paths and related problems Ph.D thesis Universitt des Saarlandes Index adaptation, 52 adaptive penalties, 119 advanced memetic algorithm, 71 aggregation of flows, alternating and converging constraint, 123 ant algorithms, 51 auction combinatorial, 141 back-freight, backward feeding, baldwin effect, 63 benchmark instances, 42 biased random drawing, 84 bottleneck resource, 18 bottleneck selection, 25 capacitated vehicle routing problem, 20 carrier, 17 charge function, 21 chromosome, 55 cluster builder, 66 cluster first-route second, 69 co-evolution of partial solutions, 75 with specialization, 74 co-evolutionary memetic algorithm, 72 collaborative planning, 136 collaborative planning approach, 142 collection, combinatorial auction, 141 compulsory request, 27 consolidation strategy, construction approach parallel, 78 sequential, 78 control alternating and converging constraints, 124 cooperation, 136 coupling savings, 18 crew scheduling, 11 crossover, 57 modified precedence preserving crossover, 88 mPPX, 88 PPX, 88 precedence preserving crossover, 88 data hull, 85 decision problem, 16 decomposition approach, 71 deferrable request, 109 delivery, destination, dial-a-ride-problem, 33 direct representation, 84 distribution center, distribution network, domain integration, 136 double ranking, 120 dynamic penalties, 118 empty balancing, 11 empty miles, enonomies of scale, environmental impacts, 162 Index evolutionary algorithms, 52 evolutionary programming, 53 evolutionary strategies, 53 five-phase transport-process, flow of goods aggregation, bi-directional, uni-directional, forward feeding, freight carrier, freight charge, 17 freight charge optimization, 22 freight charge optimization problem, 22, 34 freight tariff function, 34 freight transport network, fulfillment costs, 12 fulfilment mode, 17 full truckload problem, 33 gene, 55 genetic algorithm, 53 genetic clustering, 68 genetic local search, 63 genetic sectoring, 69 genetic sequencing, 65 genetic vehicle representation, 73 genotype, 55 groupage system, 135 heuristic, 50 heuristic algorithms, 20 heuristics meta-, 20 hierarchical planning approach, 22 hill climber, 62 hub, hybrid genetic search, 63 improvement approach, 50 individual, 52 inner ranking, 120 inst ant iation order, 84 itinerary, 10 lamarckian evolution, 63 less-than-truckload, line haul, local search methods, 50 location in networks, locus, 55 logistics service provider, 17 incorporation, 19 logistics system configuration, 10 logistics system deployment, 10 logistics system design, mating pool, 56 means of transport, meme, 63 memetic algorithm, 63 advanced, 71 co-evolutionary, 72 meta-heuristics, 20 modal split, mode fulfilment, 17 selection, 18 mode selection problem, 19 modes of road transport hire or reward, own account, multi-chromosome representation, 71 multiple traveling salesman problem, 20 mutation, 52 myopic planning, 28 network design, network layout, online planning problems, 41 operation, 34 optimal approaches, 20 origin, overhead cost, passenger-kilometer , pd-path, 35 pd-schedule, 35 penalty adaptive, 119 dynamic, 118 static, 116 penalty value, 118 performance of transport, phenotype, 55 pickup, pickup and delivery selection problem Index capacitated, 39 general, 36 pickup and delivery planning problems, 12 pickup and delivery problem with time windows, 33 pickup and delivery path, 35 pickup and delivery planning problem simultaneous, 23 pickup and delivery problem, 33 pickup and delivery request, 31 pickup and delivery schedule, 35 pickup and delivery selection problem with compulsory requests, 40 pickup and delivery selection model simultaneous, 23 pickup and delivery selection problem with logistics service provider incorporation, 38 with postponement, 41 planning collaborative, 136 population, 52 model, 92 population based approaches, 50 portfolio sub-, 22 portfolio re-composition problem, 138 postponement, 27 problem portfolio re-composition, 138 profit contribution maximization, 18 quote, 117 quote first-cost second, 121 quote-class, 120 rail transport, ranking double, 120 inner, 120 regional multimodal planning, 10 rejection of transport demands, 34 replenishment, representation, 55 direct, 84 multi-chromosome, 71 of a pd-schedule, 84 163 permutation based, 65 problem specific, 72 reproduction, 52 reproduction model (CL A), 120 request, 31 compulsory, 27 deferrable, 109 urgent, 28 request acceptance, 16 operational, 17 problem, 16 tactical, 16 request selection, 18 requests internal, 17 revenue, 16 road transport, rolling planning horizon, 27 roulette-wheel-selection, 56 route, 19 improvement, 82 route construction heuristic, 69 route first-cluster second, 66 routes, 19 tentative, 80 routing, 19 routing problem, 19 + search algorithms, 49 selection, 52 bottleneck, 25 maximal-profit , 25 with compulsory requests, 26 with postponement, 27 separation genes, 71 sequencing genetic, 65 shared distances, 46 shipment, simulated annealing, 50 simultaneous approach, 23 simultaneous construction approach, 50 simultaneous planning models generic, 24 static penalties, 116 sub-portfolio, 22 successive approaches, 50 164 Index tabu search, 51 third party, ton-kilometer , tour, 19 trail of a population, 130 trajectory methods, 50 transport process, transportation plan, 12 transshipment, traveling salesman problem, 20 truckload, vehicle allocation problem, 33 vehicle routing and scheduling, 11 vehicle routing problem capacitated, 20 vehicle routing problem with backhauls, 33 vehicle routing problem with time windows, 20 waterway transport, ... Günther and P v Beek (Eds.) Advanced Planning and Scheduling Solutions in Process Industry VI, 426 pages 2003 ISBN 3-540-00222-7 Jörn Schönberger Operational Freight Carrier Planning Basic Concepts, ... investigations and formulates topics for future research in the field of planning models for freight transportation with the possibility of incorporating a paid carrier Operational Freight Transport Planning. .. relations is called freight charge optimization Mathematical optimization models and solution approaches for this type of carrier planning problem are investigated in Pankratz (2002) and Kopfer (1989,1984)