Location Science (eBook)
XVII, 767 Seiten
Springer-Verlag
978-3-030-32177-2 (ISBN)
This book presents essential information on modern location science - in a word, all you need to know about location. The second edition of this handbook has been fully revised throughout, with numerous updates and chapters added, to offer an even more comprehensive overview of methods and applications.
The book is divided into three parts: basic concepts, advanced concepts and applications. Written by the most respected specialists in the field and thoroughly reviewed by the editors, it first lays out the fundamental problems in location science and provides readers with basic background information on location theory. Part II covers advanced models and concepts, broadening and expanding on the content presented in Part I. It also discusses important tools to help readers grasp and solve real-world location problems. Part III focuses on the links between location science and other areas like GIS, telecommunications, healthcare, rapid transit networks, districting problems and disaster events, and presents a wide range of applications to allow readers to understand the role of facility location in such areas and learn how to handle real-world location problems.
The book is intended for researchers working on theory and applications involving location problems and models. It is also suitable as a textbook for graduate courses on facility location.Gilbert Laporte obtained his Ph.D. in Operations Research from the London School of Economics in 1975. He is Professor of Operations Research at HEC Montréal, Canada Research Chair in Distribution Management. He has been Editor of Transportation Science, Computers & Operations Research and INFOR. He has authored or coauthored 19 books, as well as more than 550 scientific articles in combinatorial optimization, mostly in the areas of vehicle routing, location and timetabling. He has received many scientific awards including the Pergamon Prize (United Kingdom) in 1987, the 1994 Merit Award of the Canadian Operational Research Society, and the CORS Practice Prize on four occasions. He has been a member of the Royal Society of Canada since 1998, Fellow of INFORMS since 2005, and foreign member of the National Academy of Engineering (United States) since 2019. In 2009, he received the Robert M. Herman Lifetime Achievement Award in Transportation Science from the Transportation Science and Logistics Society of INFORMS. In 2014, he obtained the Lifetime Achievement in Location Analysis Award from the Section on Location Analysis of INFORMS. In 2018 he became a member of the Order of Canada.
Stefan Nickel is a full professor at the Karlsruhe Institute of Technology - KIT (Germany) and one of the directors of the Institute of Operations Research. He obtained his PhD in mathematics at the Technical University of Kaiserslautern (Germany) in 1995. From 2014-2016 he was the dean of the Department of Economics and Management at the KIT. He was also member of the scientific advisory board as well as of the management board of the Fraunhofer Institute for Applied Mathematics (ITWM) in Kaiserslautern from 2004-2016. Since 2011 he additionally holds the positions of one of the directors of the Karlsruhe Service Research Institute (KSRI) and of the FZI Research Center for Information Technology. From 2006-2015 he was editor-in-chief of Computers & Operations Research. Since 2016 he is editor-in-chief of Operations Research for Health Care. He has coordinated the Health Care working group within the German OR society (GOR) and has been the president of GOR from 2013-2014. Moreover, he was coordinator of the EURO working group on locational analysis. Since 2019 Stefan Nickel serves as VP IFORS in the EURO executive committee and is member of the AC of IFORS. He has authored or co-authored 5 books as well as more than 130 scientific articles in his research areas location analysis, supply chain management, health care logistics, and online optimization. He has been awarded the EURO prize for the best EJOR review paper (2012) and the Elsevier prize for the EJOR top cited article 2007-2011. In addition, he conducted several industry projects with well-known companies such as Bosch, BASF, Lufthansa, Miele, or SAP.
Francisco Saldanha da Gama is professor of Operations Research at the Department of Statistics and Operations Research at the Faculty of Science, University of Lisbon, where he received his Ph.D. in 2002. He has extensively published papers in scientific international journals mostly in the areas of location analysis, supply chain management, logistics and combinatorial optimization. Together with Stefan Nickel, he has been awarded the EURO prize for the best EJOR review paper (2012) and the Elsevier prize for the EJOR top cited article 2007-2011 (2012), both with the paper entitled 'Facility location and supply chain management-A review'. He is member of various international scientific organizations such as the EURO Working Group on Location Analysis of which he is one the past coordinators. Currently, he is editor-in-chief of Computers & Operations Research. His research interests include stochastic mixed integer optimization, location theory and project scheduling.
Preface 5
Contents 6
1 Introduction to Location Science 17
1.1 Introduction 17
1.2 The Roots 18
1.3 Towards a New Science 23
1.4 Purpose and Structure of This Book 25
1.5 How to Use This Book 26
References 35
Part I Basic Concepts 38
2 p-Median Problems 39
2.1 Introduction 39
2.2 Applications 42
2.3 Integer Programming Formulations for the p-Median Problem 43
2.4 Optimal Solution Procedures 49
2.5 Polyhedral Properties 52
2.6 p-Median Problem on a Graph and Additional Polyhedral Results 53
2.7 Heuristics 57
2.7.1 Classical Heuristics 58
2.7.2 Metaheuristics 59
2.7.3 Approximation Heuristics 61
2.8 Conclusions 61
References 62
3 p-Center Problems 65
3.1 Introduction 65
3.2 Polynomial Cases, Complexity and Approximation Results 67
3.3 Exact Methods 68
3.4 Heuristics 72
3.5 Variants 73
3.5.1 The Capacitated p-Center Problem 74
3.5.2 The Conditional p-Center Problem 75
3.5.3 The Continuous p-Center Problem 75
3.5.4 The Fault Tolerant p-Center Problem 76
3.5.5 The p-Center Problem with Uncertain Parameters 76
3.6 Conclusions 76
References 77
4 Fixed-Charge Facility Location Problems 80
4.1 Introduction 80
4.2 Overview and Modeling Issues 82
4.2.1 Set Partitioning Formulation of FLPs 86
4.3 Solution Algorithms for Fixed-Charge Facility Location 87
4.3.1 Lagrangean Relaxation 88
4.3.2 The Pricing Problem for SPSFLP 90
4.4 The Uncapacitated Facility Location Problem 91
4.4.1 Bounds for UFLP Derived from LP Duality 92
4.4.2 The UFLP as the Optimization of a Supermodular Set Function 95
4.5 Polyhedral Analysis of the UFLP 99
4.5.1 Extreme Points 101
4.5.2 Valid Inequalities and Facets 101
4.5.3 Lifting Procedures 104
4.6 Conclusions 105
References 106
5 Covering Location Problems 112
5.1 Introduction 112
5.2 Models 114
5.3 Theoretical Results 121
5.4 Solution Methods 123
5.5 Approximate Algorithms 124
5.6 Lagrangian Relaxation 125
5.7 Continuous Covering Location Problems 128
5.8 Conclusions 129
References 129
Part II Advanced Concepts 133
6 Anti-covering Problems 134
6.1 Introduction 134
6.2 Regional Covering Model 136
6.2.1 Individual-Facility Interactions 136
6.2.2 Facility-Facility Interactions 141
6.2.3 The Anti-covering Model 142
6.3 Computational Approach 143
6.4 Numerical Examples 145
6.5 Conclusions 148
References 149
7 Locating Dimensional Facilities in a Continuous Space 153
7.1 Introduction 153
7.2 Location of Dimensional Facilities 154
7.3 Locating Lines and Hyperplanes 156
7.3.1 Applications 157
7.3.2 Ingredients for Analyzing Hyperplane Location Problems 158
7.3.2.1 Distances Between Points and Hyperplanes 158
7.3.2.2 Dual Interpretation 159
7.3.3 The Minsum Hyperplane Location Problem 161
7.3.3.1 Minsum Hyperplane Location with Vertical Distance 161
7.3.3.2 Minsum Hyperplane Location with Norm Distance 163
7.3.3.3 Minsum Hyperplane Location with Gauge Distance 164
7.3.4 The Minmax Hyperplane Location Problem 165
7.3.4.1 Relation to Transversal Theory 165
7.3.4.2 The Finite Dominating Set Property 165
7.3.5 Algorithms for Minsum and Minmax Hyperplane Location 167
7.3.5.1 Enumeration 167
7.3.5.2 Linear Programming for Hyperplane Location with Vertical and Block Norm Distance 167
7.3.5.3 Enhancing the Enumeration for Line Location with Euclidean Distance 169
7.3.6 Ordered Median Line and Hyperplane Location Problem 170
7.3.7 Some Extensions of Line and Hyperplane Location Problems 171
7.3.7.1 Obnoxious Line and Hyperplane Location 171
7.3.7.2 Locating p Lines or Hyperplanes 172
7.3.7.3 Restricted Line Location 174
7.3.7.4 Line Location in RD 174
7.4 Locating Circles and Spheres 174
7.4.1 Applications 175
7.4.2 Distances Between Points and Hyperspheres 176
7.4.3 The Minsum Hypersphere Location Problem 177
7.4.3.1 Location of a Euclidean Minsum Circle 179
7.4.3.2 Location of Minsum Circles and Hyperspheres with Block Norm Distance 179
7.4.4 The Minmax Hypersphere Location Problem 181
7.4.4.1 Relation to Minimal Covering Annulus Problem and Equity Problem 181
7.4.4.2 Location of a Euclidean Minmax Circle 182
7.4.4.3 Location of a Minmax Circle with Rectangular Distance 182
7.4.4.4 Location of a Euclidean Minmax Hypersphere 183
7.4.5 Some Extensions of Circle Location Problems 183
7.4.5.1 Minimizing the Sum of Squared Distances 183
7.4.5.2 Locating Euclidean Concentric Circles 184
7.4.5.3 Location of a Circle with Fixed Radius 184
7.4.5.4 Locating a Hypersphere of One Norm Measuring Distances with Respect to Another Norm 185
7.5 Locating Other Types of Dimensional Facilities 186
7.5.1 Locating Line Segments 186
7.5.2 The Widest Empty 1-Corner Corridor in the Plane 186
7.5.3 Two-Dimensional Facilities 187
7.5.4 General Approaches for Locating Dimensional Facilities 188
7.6 Conclusions 189
References 190
8 Facility Location Under Uncertainty 195
8.1 Introduction 195
8.2 Uncertainty Issues 196
8.3 Robust Facility Location Problems 197
8.4 Stochastic Facility Location Problems 204
8.5 Chance-Constrained Facility Location Problems 213
8.6 Challenges and Further Readings 215
8.6.1 Multi-Stage Stochastic Programming Models 216
8.6.2 Algorithms 217
8.6.3 Scenario Generation 218
8.6.4 Other Notes 219
8.7 Conclusions 219
References 220
9 Location Problems with Multiple Criteria 224
9.1 Introduction 224
9.2 1-Facility Planar/Continuous Location Problems 226
9.2.1 Polyhedral Planar Minisum Location Problems 232
9.2.1.1 Bicriteria Case 234
9.2.1.2 Three-Criteria Case 239
9.2.1.3 Case Where Q> 3
9.2.2 Other References in Continuous Multicriteria Location Problems 244
9.3 Network Location Problems 245
9.3.1 1-Facility Median Problems 245
9.3.1.1 Pareto Locations in General Networks 245
9.3.1.2 Bi-Criteria Case 248
9.3.1.3 Q-Criteria Case 256
9.3.1.4 Multicriteria Median Problems on a Tree 257
9.3.2 Other Multicriteria Location Problems on Networks 261
9.4 Discrete Location Problems 261
9.4.1 Model and Notation 262
9.4.2 Determining the Entire Set of Pareto-Optimal Solutions 263
9.4.3 Determining Supported Pareto-Optimal Solutions 265
9.4.4 Other References in Discrete Location Problems 266
9.5 Conclusions 267
References 267
10 Ordered Median Location Problems 270
10.1 Introduction 270
10.2 The Ordered Median Function 272
10.3 The Continuous Ordered Median Problem 275
10.3.1 The Single Facility Polyhedral Ordered Median Location Problem 275
10.3.2 Generalized Continuous Ordered Median Location Problems 281
10.4 The Ordered Median Problem on Networks 287
10.4.1 The Single Facility Ordered Median Problem 290
10.4.2 The p-Facility Ordered Median Problem 293
10.4.2.1 A Finite Set of Candidates for the Two Facility Case 295
10.4.2.2 A Discouraging Result for the p-Facility Case 298
10.5 The Capacitated Discrete Ordered Median Problem 301
10.5.1 A Three-Index Formulation 301
10.5.2 A Covering Formulation and Some Properties 303
10.6 Conclusions 306
References 309
11 Multi-Period Facility Location 312
11.1 Introduction 312
11.2 Continuous Problems 313
11.3 Network Problems 316
11.4 Discrete Problems 318
11.5 Modular Construction of Intrinsic Multi-Period Facility Location Models 321
11.6 The Value of the Multi-Period Solution 330
11.7 Conclusions 332
References 333
12 Hub Location Problems 336
12.1 Introduction 336
12.2 Fundamentals 338
12.2.1 Features, Assumptions and Properties 339
12.2.2 Supermodular Properties 342
12.2.3 Objectives 343
12.3 Formulating Hub Location Problems 345
12.3.1 Single Assignments 345
12.3.2 Multiple Assignments 347
12.4 Main Developments and Recent Trends 350
12.4.1 Hub Network Topologies 350
12.4.2 Modeling Flow Costs 352
12.4.3 Capacitated Models 354
12.4.4 Uncertainty in Hub Location 355
12.4.5 Dynamic and Multi-Modal Models 357
12.4.6 Competition and Collaboration 358
12.5 Solving Hub Location Problems 361
12.5.1 Complexity Results 361
12.5.2 Heuristic Algorithms 362
12.5.3 Lower Bounding Procedures and Exact Algorithms 363
12.6 Conclusions 365
References 366
13 Hierarchical Facility Location Problems 373
13.1 Introduction 373
13.2 Fundamentals 375
13.2.1 Nature of Demand 375
13.2.2 Service Availability 376
13.2.3 Flow Pattern and Spatial Configuration 377
13.2.4 Decisions and Objectives 378
13.2.5 Classification Scheme 379
13.3 Applications 380
13.3.1 Health Care Systems 380
13.3.2 Production-Distribution Systems 381
13.3.3 Telecommunications Systems 381
13.3.4 Urban Transportation Systems 382
13.3.5 Air Transportation Systems 382
13.3.6 Cargo and Postal Delivery Systems 383
13.4 Families of Hierarchical Facility Location Problems 383
13.4.1 Multi-Level Facility Location Problems 384
13.4.2 Median and Covering Hierarchical Location Problems 387
13.4.3 Multi-Echelon Location-Routing Problems 389
13.4.4 Hierarchical Hub Location Problems 391
13.5 Conclusions 393
References 394
14 Competitive Location Models 398
14.1 The Basic Model: The First 50 Years 398
14.2 Elements of Competitive Location Models 403
14.3 Consumer Behavior in Competitive Location Models 406
14.4 Results for Different Behavioral Assumptions 411
14.4.1 UD1a, Linear Market, Nash Equilibria 412
14.4.2 UD1a, Linear Market, von Stackelberg Solution 412
14.4.3 UD1a, Plane, Nash Equilibrium 413
14.4.4 UD1a, Plane, von Stackelberg Solution 414
14.4.5 UD1a, Networks, Nash Equilibria 415
14.4.6 UD1a, Networks, von Stackelberg Solution 415
14.4.7 UD1b, Linear Market, Nash Equilibria 417
14.4.8 UD1b, Plane, Nash Equilibria 419
14.4.9 UD1b, Networks, Nash Equilibria 420
14.4.10 UD1, Linear Market, Nash Equilibria 420
14.4.11 UD1, Linear Market, von Stackelberg Solution 421
14.4.12 UD1, Plane, Nash Equilibria 422
14.4.13 UD1, Plane, von Stackelberg Solution 422
14.4.14 UD2a, Linear Market, Nash Equilibria 423
14.4.15 UD2a, Plane, von Stackelberg Solution 423
14.4.16 UD2a, Network, Nash Equilibria 424
14.4.17 UD2a, Network, von Stackelberg Solution 424
14.4.18 UD2b, Plane, von Stackelberg Solution 424
14.4.19 UD2b, Network, von Stackelberg Solution 425
14.4.20 UP1, Linear Market, Nash Equilibria 425
14.4.21 UP1, Plane, Nash Equilibria and von Stackelberg Solutions 426
14.4.22 UP1, Network, Nash Equilibria 426
14.4.23 UP1, Network, von Stackelberg Solution 427
14.4.24 UP2, Plane, von Stackelberg Solution 427
14.4.25 UP2, Network, von Stackelberg Solution 427
14.5 Summary, Extensions, and Outlook 428
References 429
15 Location-Routing and Location-Arc Routing 437
15.1 Introduction 437
15.2 Problem Definition and Notation 439
15.3 Formulations and Exact Algorithms 441
15.3.1 Flow Formulations 442
15.3.2 Set-Partitioning Formulations 445
15.3.3 Valid Inequalities 447
15.4 Heuristic Algorithms 450
15.5 Location-Arc Routing 452
15.6 Conclusions 454
References 455
16 Location Logistics in Supply Chain Management 458
16.1 Introduction 458
16.2 From Logistics to Location Models 460
16.2.1 Why Logistics Matters in Location Modeling 461
16.2.2 Building Blocks of Logistics 462
16.2.2.1 Procurement 463
16.2.2.2 Production 464
16.2.2.3 Inventory 464
16.2.2.4 Routing and Distribution 464
16.3 A Basic Integrated Logistics Location Model 465
16.3.1 Notation 465
16.3.2 The BILL Model 466
16.4 Challenges in Industrial Logistics 469
16.4.1 Sustainability 470
16.4.2 Uncertainty, Risk and Disaster Events 470
16.4.3 Digital Supply Chain Transformation and Supply Chain Integration 471
16.5 Modeling Formulations for Industrial Location Decisions 472
16.5.1 Reverse Logistics 472
16.5.2 Supply Chain Risk 476
16.6 Conclusions 479
References 480
17 Stochastic Location Models with Congestion 482
17.1 Introduction 482
17.2 Key Model Components 485
17.2.1 Customers 485
17.2.2 Facilities 486
17.2.3 Costs, Revenues, and Constraints 490
17.2.3.1 Travel Cost and Coverage Constraints 491
17.2.3.2 Congestion Costs and Service Level Constraints 492
17.2.3.3 Facility Costs 498
17.2.3.4 Revenues and Overall Objectives 498
17.3 Customer Response: Demand Levels and Allocations 499
17.3.1 Customer Utility Functions 500
17.3.2 SLCIS Models with Customer Reaction 502
17.3.2.1 Customer Reaction Models with Deterministic Utility 1: Models with Allocation-Only Reaction (AR) 502
17.3.2.2 Customer Reaction Models with Deterministic Utility 2: Models with Demand-Only Reaction (DR) 507
17.3.2.3 Customer Reaction Models with Deterministic Utility 3: Full Response Models (FR) 509
17.3.2.4 Proportional Allocation (PA) Models 510
17.4 General SLCIS Model Specification 513
17.5 SLCIS Models in the Literature: Overview and Classification 515
17.5.1 Coverage-Type (CT) Models 515
17.5.2 Service-Objective (SO) Models 527
17.5.3 Balanced-Objective (BO) Models 528
17.5.4 Explicit Customer Response (ECR) Models 531
17.5.5 Proportional Allocation (PA) Models 534
17.6 Conclusions 536
References 538
18 Aggregation in Location 541
18.1 Introduction 541
18.2 Terminology and Examples 543
18.3 Case Study 545
18.4 Aggregation Error Measures 550
18.5 Error Bounds 555
18.6 Conclusions 558
References 558
Part III Applications 561
19 Location and Geographic Information Systems 562
19.1 Introduction 562
19.2 Overview of GIS 563
19.2.1 GIS Software 565
19.3 Generalities on Facility Location Problems 567
19.4 Interconnections Between Location Science and GIS: Emerging Trends 570
19.4.1 Location Modeling with Spatio-Temporal Big Data 571
19.4.2 GIS Tools Integration to Data Analytics Libraries 573
19.4.3 GIS as Interactive DSS 575
19.5 Using GIS in Location Science Applications 576
19.6 Conclusions 586
References 588
20 Green Location Problems 594
20.1 Sustainability and ``Green'' in Location Problems 594
20.2 Environmental Considerations in Location Problems 596
20.2.1 Accounting for Emissions in Facility Location Problems 598
20.3 Reverse Logistics Network Design 601
20.3.1 A Generic Reverse Logistics Network Design Model 603
20.3.2 Extensions 605
20.4 Location Problems Related to Alternative Fuel Vehicles 607
20.5 Research Prospects 609
References 610
21 Location Problems in Humanitarian Supply Chains 614
21.1 General Description of Humanitarian Supply Chains 614
21.1.1 International and Regional Distribution Centers 615
21.1.2 Dispensing Points 617
21.1.3 Transportation Flows 618
21.2 Humanitarian Facility Location Problems 619
21.2.1 Locations in Global Humanitarian Supply Chains 620
21.2.2 Locations in Local Humanitarian Supply Chains 620
21.2.3 General Overview 622
21.3 A Generic Location Model for Humanitarian Supply Chains 623
21.3.1 Notation 623
21.3.2 Basic Mathematical Model 624
21.4 Location Problems with Additional Considerations 626
21.4.1 Location and Prepositioning 626
21.4.2 Location-Routing Problems 629
21.5 Conclusion 630
References 630
22 Location Problems Under Disaster Events 633
22.1 Introduction 633
22.2 Notation 635
22.3 Identifying Critical Facilities: Interdiction Models 636
22.4 Hardening Facilities: Protection Models 639
22.5 Planning Robust Systems: Design Models 643
22.5.1 Planning Against Worst-Case Disruptions 643
22.5.2 Planning Against Random Disruptions 646
22.5.3 Planning Against Specific Disruption Scenarios 649
22.6 Future Trends 652
22.7 Conclusions 654
References 654
23 Location Problems in Healthcare 659
23.1 Introduction 659
23.2 Healthcare Facility Location 660
23.2.1 Objective Functions in Healthcare Facility Location 660
23.2.2 An Overview of Healthcare Facility Location Models 663
23.2.2.1 Modeling Capacity 664
23.2.2.2 Assumptions on Allocation 665
23.2.2.3 Assumptions on Demand and Patient Choice 666
23.2.2.4 Assumptions on Facility Types and Patient Flows: Hierarchical Models 667
23.2.2.5 Modeling Dynamic Aspects of Location Decisions 669
23.2.2.6 Further Reading 670
23.3 Ambulance Location 671
23.3.1 The Strategic and Tactical Level: Finding Ambulance Base Locations and Assigning Ambulances 671
23.3.1.1 A Double Coverage Model 672
23.3.1.2 Considering Ambulance Utilization 674
23.3.1.3 Further Reading 675
23.3.2 The Operational Level: Ambulance Relocation 675
23.3.2.1 Ambulance Preparedness 676
23.3.2.2 Further Reading 677
23.4 Hospital Layout Planning 677
23.4.1 The Quadratic Assignment Problem 678
23.4.2 A Mixed-Integer Programming Formulation 679
23.4.3 Further Reading 681
23.5 Conclusions 682
References 683
24 The Design of Rapid Transit Networks 689
24.1 Introduction 689
24.2 Objectives and Network Assessment 692
24.3 Location of Rapid Transit Networks: Models and Algorithms 696
24.3.1 Location of a Single Alignment 696
24.3.2 Rapid Transit Network Design 698
24.4 Location of Stations 699
24.5 Conclusions 701
References 702
25 Districting Problems 706
25.1 Introduction 706
25.2 Applications 708
25.2.1 Political Districting 708
25.2.2 Sales Territory Design 710
25.2.3 Service Districting 712
25.2.4 Distribution Districting 714
25.3 Notations 714
25.3.1 Basic Units 714
25.3.2 Districts 715
25.3.3 Problem Formulation 716
25.4 Districting Criteria 716
25.4.1 Complete and Exclusive Assignment 716
25.4.2 Balance 716
25.4.3 Contiguity 718
25.4.3.1 Graph-Based Measures 718
25.4.3.2 Geometric Measures 719
25.4.3.3 Mathematical Modelling 720
25.4.4 Compactness 722
25.4.4.1 Geometric Measures 722
25.4.4.2 Distance-Based Measures 723
25.4.4.3 Mathematical Modelling 726
25.4.5 District Center 726
25.4.6 Other Criteria 727
25.5 Solution Approaches 727
25.5.1 Location-Allocation Methods 728
25.5.2 Exact Methods 730
25.5.3 Computational Geometry Methods 731
25.5.4 Construction Methods 733
25.5.5 Metaheuristics 734
25.5.5.1 Greedy Randomized Adaptive Search Procedure (GRASP) 734
25.5.5.2 Tabu Search (TS) 736
25.5.5.3 Simulated Annealing (SA) 737
25.5.5.4 Genetic Algorithm (GA) 738
25.5.5.5 Hybrid and Miscellaneous Approaches 739
25.5.6 Lower Bounding Schemes 740
25.6 Conclusions 740
References 741
26 Facility Location in the Public Sector 745
26.1 Introduction 745
26.2 Bike Sharing 746
26.3 Location Decisions in Public Transport 747
26.4 Electric Vehicle Charging Station Location 749
26.5 Spatial Planning for Health Care Facilities 754
26.6 School Location 759
26.7 Summary 761
References 762
About the Editors 765
| Erscheint lt. Verlag | 16.3.2020 |
|---|---|
| Zusatzinfo | XVII, 767 p. 97 illus., 32 illus. in color. |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie | |
| Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation | |
| Schlagworte | Coverage • Demand point aggregation • Distance Minimization • Fixed-charge facility • Hub location • Location • Location problems in healthcare • Location problems in telecommunications • Optimization • p-center problem • p-Median Problem • Rapid transit networks • spatial planning • Stochastic location models |
| ISBN-10 | 3-030-32177-0 / 3030321770 |
| ISBN-13 | 978-3-030-32177-2 / 9783030321772 |
| Haben Sie eine Frage zum Produkt? |
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