Springer operational freight carrier planning basic concepts optimization models and advanced memetic algorithms 2005 ISBN3540253181

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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 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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)
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