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E-raamat: Freight Transport and Distribution: Concepts and Optimisation Models

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 19-Jun-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351702393
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Freight Transport and Distribution: Concepts and Optimisation ModelsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 19-Jun-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351702393
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This book serves as a primer on freight transportation and logistics, providing a general and broad coverage of concepts, mathematical models and methodologies available for freight transportation planning at strategic, tactical and operational levels. It is aimed at graduate students, and is also a reference book for practitioners in the field.

The book includes preliminaries, such as mathematical modeling and optimisation algorithms. The book also features case studies and practical real-life examples to illustrate applications of the concepts and models covered, and to encourage a hands-on and a practical approach. The author has taught and published extensively in the field and draw on state-of-the-art scientific research. He has also been part of a number of practical research projects, which underpin the real life examples in the book.

Foreword xiii
Preface xv
1 Freight logistics, distribution and transport: Concepts 1(14)
1.1 Actors
1(2)
1.1.1 Shippers
2(1)
1.1.2 Carriers
2(1)
1.1.3 Intermediaries
2(1)
1.2 Modes of transportation
3(7)
1.2.1 Road
3(1)
1.2.2 Rail
4(2)
1.2.3 Air
6(1)
1.2.4 Sea
6(1)
1.2.5 Intermodal transportation
7(2)
1.2.6 Intermodal terminals
9(2)
1.2.6.1 Containerised transport
9(1)
1.3 Choice of carrier and transportation mode
10(1)
1.4 Shipment options
11(1)
1.4.1 Direct and customised shipments
11(1)
1.4.2 Consolidated shipments
11(1)
1.5 Distribution structures
12(2)
1.5.1 Single-echelon
12(1)
1.5.2 Multi-echelon
12(2)
References and further reading
14(1)
2 Location in networks 15(42)
2.1 Hub location problems
17(2)
2.2 Common notation
19(1)
2.3 p-Hub median problems
19(11)
2.3.1 Single-allocation p-hub median problem
19(9)
2.3.1.1 Quadratic integer programming formulation
19(2)
2.3.1.2 Integer linear programming formulations
21(7)
2.3.2 Multiple-allocation p-hub median problem
28(2)
2.4 Capacitated hub location problems
30(2)
2.5 Other types of hub location problems
32(7)
2.5.1 p-Hub centre problem
32(5)
2.5.2 Hub covering problem
37(2)
2.6 Congestion in hub location problems
39(2)
2.7 Facility location problems
41(4)
2.7.1 Uncapacitated facility location problem
43(1)
2.7.2 Capacitated facility location problem
44(1)
2.8 Uncertainty in location problems
45(9)
2.8.1 Scenario-based modelling
48(2)
2.8.2 Recourse actions
50(4)
2.9 Practical application: Hub location for time-sensitive cargo deliveries
54(1)
References and further reading
55(2)
3 Transportation and service networks 57(36)
3.1 Features of transportation networks
58(2)
3.2 Common notation
60(1)
3.3 Multi-commodity network flow and design
61(11)
3.3.1 Single-commodity network design problem
61(1)
3.3.2 Shortest path problems
62(5)
3.3.2.1 Resource-constrained shortest path problem
63(2)
3.3.2.2 Multi-objective shortest path problem
65(2)
3.3.3 Multi-commodity network design problem
67(5)
3.4 Service network design
72(10)
3.4.1 Time-invariant formulation
75(3)
3.4.2 Time-space network representation
78(4)
3.5 Congestion in network design
82(6)
3.5.1 Modelling delay
82(1)
3.5.2 Network design with node congestion
83(5)
3.6 Practical application: Intermodal rail network service design
88(4)
References and further reading
92(1)
4 Routing problems 93(64)
4.1 Routing in freight distribution
93(5)
4.1.1 Network topology
94(1)
4.1.2 Timescales for planning and implementation
94(1)
4.1.3 Customers
95(1)
4.1.4 Time
96(1)
4.1.5 Vehicles
97(1)
4.1.6 Objectives
98(1)
4.2 Travelling salesman problem
98(22)
4.2.1 Asymmetric formulations
99(5)
4.2.2 Subtours and subtour elimination for the ATSP
104(13)
4.2.2.1 Cutset inequalities
104(4)
4.2.2.2 Compact formulations
108(1)
4.2.2.3 Compact node-ordering formulation
109(2)
4.2.2.4 Compact arc-ordering or flow-based formulation
111(3)
4.2.2.5 Compact precedence-based formulation
114(3)
4.2.3 Symmetric formulations
117(3)
4.3 Vehicle routing problem
120(3)
4.3.1 Common notation
121(2)
4.4 Capacitated vehicle routing
123(10)
4.4.1 Two-index formulations
123(5)
4.4.1.1 Cutset inequalities for tour feasibility
124(3)
4.4.1.2 Compact inequalities based on node-ordering for tour feasibility
127(1)
4.4.1.3 Compact inequalities based on arc-ordering for tour feasibility
127(1)
4.4.2 Three-index formulations
128(2)
4.4.3 Set partitioning formulations
130(3)
4.5 Vehicle routing with time windows
133(4)
4.5.1 Soft time windows
135(2)
4.6 Distance-constrained vehicle routing
137(1)
4.7 Uncertainty in vehicle routing problems
138(10)
4.7.1 Recourse models
139(7)
4.7.1.1 Vehicle routing problems with stochastic travel times
141(5)
4.7.2 Chance-constrained models
146(2)
4.8 Dynamic vehicle routing problems
148(5)
4.9 Practical application: Charity collection
153(3)
References and further reading
156(1)
5 Integrated routing problems 157(18)
5.1 Location-routing problem
158(6)
5.1.1 Formal problem definition
160(1)
5.1.2 Formulation based on flow
161(1)
5.1.3 Formulation based on two commodities
162(2)
5.2 Inventory-routing problem
164(7)
5.2.1 Formal problem definition
167(1)
5.2.2 Exponential-size formulation
168(2)
5.2.3 Polynomial-size formulation
170(1)
5.3 Production-routing problem
171(2)
5.3.1 Formal problem definition
172(1)
5.3.2 Mathematical modelling
172(1)
References and further reading
173(2)
6 Green freight distribution 175(26)
6.1 Introduction
175(2)
6.1.1 Emissions and pollution
175(2)
6.1.2 Noise and vibration
177(1)
6.1.3 Land and resource consumption
177(1)
6.1.4 Toxic effects
177(1)
6.2 Mitigating measures
177(1)
6.3 Quantifying externalities
178(7)
6.3.1 Fuel consumption models
179(6)
6.3.1.1 Emission factor model
179(1)
6.3.1.2 Average speed model
180(3)
6.3.1.3 Instantaneous emissions model
183(2)
6.4 Green freight distribution planning
185(11)
6.4.1 Pollution-routing problems
186(2)
6.4.1.1 Fleet mix, fuel consumption and emissions
188(6)
6.4.2 Speed optimisation on fixed routes
194(2)
6.5 Practical application: Speed optimisation in maritime shipping
196(3)
References and further reading
199(2)
7 Collaboration in freight distribution 201(12)
7.1 Cooperative game theory
202(9)
7.1.1 Core
202(7)
7.1.2 Shapley value
209(2)
7.1.3 Banzhaf index
211(1)
7.2 Practical application: Cooperation among freight carriers
211(1)
References and further reading
212(1)
8 Methodology 213(44)
8.1 General-purpose solvers
214(2)
8.2 Exact solution techniques
216(27)
8.2.1 Benders decomposition
217(2)
8.2.2 Application to uncapacitated network design
219(15)
8.2.2.1 Benders decomposition using an aggregated formulation
219(8)
8.2.2.2 Benders decomposition using a stronger formulation
227(7)
8.2.3 Lagrangean relaxation
234(3)
8.2.4 Application to uncapacitated network design
237(6)
8.2.4.1 Relaxing the arc constraints
238(4)
8.2.4.2 Relaxing the flow constraints
242(1)
8.3 Heuristic solution techniques
243(13)
8.3.1 Constructive heuristics
246(2)
8.3.2 Neighbourhoods
248(1)
8.3.3 Evaluation function
249(1)
8.3.4 Local search
249(2)
8.3.5 Metaheuristics
251(4)
8.3.5.1 Simulated annealing
252(1)
8.3.5.2 Tabu search
253(1)
8.3.5.3 Variable neighbourhood descent
253(1)
8.3.5.4 Variable neighbourhood search
254(1)
8.3.5.5 Large neighbourhood search
254(1)
8.3.5.6 Adaptive large neighbourhood search
254(1)
8.3.5.7 Iterated local search
255(1)
8.3.5.8 Genetic algorithms
255(1)
8.3.6 Matheuristics
255(1)
References and further reading
256(1)
References 257(8)
Index 265
Tolga Bektas is Professor of Logistics Management at the University of Southampton. He has taken part in a number of practical logistics projects, in collaboration with industrial partners such as Canadian National Railway (Canada) and Rail Safety and Standards Board (UK).
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