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|a Smart delivery systems :
|b solving complex vehicle routing problems /
|c edited by Jakub Nalepa.
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| 260 |
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|a San Diego :
|b Elsevier,
|c 2019.
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| 300 |
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|a 1 online resource (345 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Intelligent Data-Centric Systems: Sensor Collected Intelligence
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| 588 |
0 |
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|a Print version record.
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|a Intro; Title page; Table of Contents; Copyright; Dedication; Contributors; Chapter 1: Current and emerging formulations and models of real-life rich vehicle routing problems; Abstract; Acknowledgement; 1.1. Introduction; 1.2. Vehicle Routing Problem and its variants; 1.3. Bus Routing Problem and its variants; 1.4. Unmanned Vehicle Routing Problem; 1.5. The other routing problems of electric vehicles; 1.6. Conclusions; References; Chapter 2: On a road to optimal fleet routing algorithms: a gentle introduction to the state-of-the-art; Abstract; Acknowledgements; 2.1. Introduction
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| 505 |
8 |
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|a 2.2. Optimal Route Choice problem2.3. Traveling Salesman Problem; 2.4. Vehicle Routing Problem; 2.5. Conclusions; References; Chapter 3: Exact algorithms for solving rich vehicle routing problems; Abstract; 3.1. Branch-and-bound methods; 3.2. Branch-and-cut methods; 3.3. Branch-and-price methods; 3.4. Branch-and-cut-and-price methods; 3.5. Constraint Programming; 3.6. Summary; References; Chapter 4: Heuristics, metaheuristics, and hyperheuristics for rich vehicle routing problems; Abstract; 4.1. Heuristics for rich vehicle routing problems
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|a 4.2. Metaheuristics for rich vehicle routing problems4.3. Hyperheuristics for rich vehicle routing problems; 4.4. Summary; References; Chapter 5: Hybrid algorithms for rich vehicle routing problems: a survey; Abstract; 5.1. Introduction; 5.2. Mathematical model for traditional CVRP; 5.3. From traditional VRP to rich VRP; 5.4. Solution approaches for RVRPs; 5.5. Literature review of hybrid approaches for VRPs; 5.6. Conclusion and future directions; References; Chapter 6: Parallel algorithms for solving rich vehicle routing problems; Abstract; 6.1. Parallelism ideas and taxonomies
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|a 6.2. Cooperative search strategies6.3. Parallel tabu search; 6.4. Parallel genetic and evolutionary algorithms; 6.5. Parallel memetic algorithms; 6.6. Parallel ant colony algorithms; 6.7. Parallel simulated annealing; 6.8. Summary; References; Chapter 7: Where machine learning meets smart delivery systems; Abstract; Acknowledgements; 7.1. Introduction; 7.2. Tuning hyper-parameters of existent algorithms for solving rich vehicle routing problems using machine learning; 7.3. Solving rich vehicle routing problems using hybrid algorithms that exploit machine learning
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|a 7.4. Solving rich vehicle routing problems using data-driven machine learning algorithms7.5. Summary; References; Chapter 8: How to assess your Smart Delivery System?; Abstract; Acknowledgements; 8.1. Introduction; 8.2. Literature review; 8.3. Notation and definition; 8.4. Model description; 8.5. Real-world PostVRP benchmark (RWPostVRPB); 8.6. Final remarks and conclusion; References; Chapter 9: Practical applications of smart delivery systems; Abstract; 9.1. Introduction; 9.2. Literature review; 9.3. Mine evacuation as a rich VRP; 9.4. Evacuation scenario examples
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| 500 |
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|a 9.5. Summary and future work
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| 650 |
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|a Business logistics
|x Computer simulation.
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| 650 |
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|a Vehicle routing problem.
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| 650 |
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6 |
|a Logistique (Organisation)
|0 (CaQQLa)201-0024063
|x Simulation par ordinateur.
|0 (CaQQLa)201-0379159
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| 650 |
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6 |
|a Probl�emes de tourn�ees.
|0 (CaQQLa)201-0072377
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| 650 |
|
7 |
|a Business logistics
|x Computer simulation
|2 fast
|0 (OCoLC)fst00842762
|
| 650 |
|
7 |
|a Vehicle routing problem
|2 fast
|0 (OCoLC)fst01744165
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| 700 |
1 |
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|a Nalepa, Jakub.
|
| 776 |
0 |
8 |
|i Print version:
|a Nalepa, Jakub.
|t Smart Delivery Systems.
|d San Diego : Elsevier, �2019
|z 9780128157152
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| 830 |
|
0 |
|a Intelligent data centric systems.
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128157152
|z Texto completo
|
