Smart delivery systems: solving complex vehicle routing problems
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands ; Kidlington, Oxford, United Kingdom ; Cambridge, MA, United States
Elsevier
[2020]
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| Schriftenreihe: | Intelligent data centric systems
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| Schlagworte: | |
| Online-Zugang: | TUM01 |
| Beschreibung: | 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 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 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 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 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. - 9.5. Summary and future work |
| Beschreibung: | 1 Online-Ressource ( 345 Seiten) |
| ISBN: | 012815716X 9780128157169 |
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| 500 | |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 | ||
| 500 | |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 | ||
| 500 | |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 | ||
| 500 | |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 | ||
| 500 | |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. - 9.5. Summary and future work | ||
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| spelling | Smart delivery systems solving complex vehicle routing problems edited by Jakub Nalepa Amsterdam, Netherlands ; Kidlington, Oxford, United Kingdom ; Cambridge, MA, United States Elsevier [2020] 1 Online-Ressource ( 345 Seiten) txt rdacontent c rdamedia cr rdacarrier Intelligent data centric systems 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 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 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 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 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. - 9.5. Summary and future work Business logistics / Computer simulation Vehicle routing problem Nalepa, Jakub edt Erscheint auch als Druck-Ausgabe 978-0-12-815715-2 Erscheint auch als Druck-Ausgabe 0128157151 |
| spellingShingle | Smart delivery systems solving complex vehicle routing problems Business logistics / Computer simulation Vehicle routing problem |
| title | Smart delivery systems solving complex vehicle routing problems |
| title_auth | Smart delivery systems solving complex vehicle routing problems |
| title_exact_search | Smart delivery systems solving complex vehicle routing problems |
| title_exact_search_txtP | Smart delivery systems solving complex vehicle routing problems |
| title_full | Smart delivery systems solving complex vehicle routing problems edited by Jakub Nalepa |
| title_fullStr | Smart delivery systems solving complex vehicle routing problems edited by Jakub Nalepa |
| title_full_unstemmed | Smart delivery systems solving complex vehicle routing problems edited by Jakub Nalepa |
| title_short | Smart delivery systems |
| title_sort | smart delivery systems solving complex vehicle routing problems |
| title_sub | solving complex vehicle routing problems |
| topic | Business logistics / Computer simulation Vehicle routing problem |
| topic_facet | Business logistics / Computer simulation Vehicle routing problem |
| work_keys_str_mv | AT nalepajakub smartdeliverysystemssolvingcomplexvehicleroutingproblems |