Smart delivery systems solving complex vehicle routing problems /
| Other Authors: | Nalepa, Jakub., ScienceDirect (Online service) |
|---|---|
| Format: | Electronic |
| Language: | English |
| Published: |
San Diego :
Elsevier,
2019.
|
| Physical Description: |
1 online resource (345 pages). |
| Series: |
Intelligent data centric systems.
|
| Subjects: |
Table of Contents:
- 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.