Knowledge collation of VRP related

One, the extension problem classification:

★the capacitated Vehicle Routing problem (CVRP), classical VRP

★the Vehicle routing problem with time windows (VRPTW) with window
-Vrphtw Hard Time window | vrpstw Soft Time Window | vrptd (VRP with time deadlines) The latest service time with customers

★ (VRPSD) The Vehicle Routing problem with split deliveries, split distribution

-The Vehicle Routing problem with split demands, demand segmentation [both substantially the same]

★the multiple Depot Vehicle Routing problem (MDVRP), Multi-Depot

★the Period Vehicle Routing problem (PVRP), periodic vehicle routing problems

★the site-dependent Vehicle Routing problem (SDVRP),

★the Open Vehicle Routing problem (OVRP), open

★the Vehicle Routing problem with Backhauls (VRPB) with return

★the Vehicle Routing problem with Pickup and Delivery (VRPPD), Fetch delivery

-With simultaneous distribution and Collection

★the Vehicle Routing problem with multiple Trips (VRPMT), multi-drive

★the fleet size and mix vehicle-routing problem (FSMVRP), profiled vehicle

-Vehicle routing problem with heterogeneous fleet (VRPHE) [looks like these two are the same type]

★the Vehicle Routing problem with Truck and Trailer (VRPTT) with trailer

★vehicle routing problem with multiple uses of vehicles (vrpm), vehicle reuse

★stochastic VRP (SVRP) Random vehicle routing problem:

-Random Customer | random demand | random time

★fuzzy Vehicle Routing problem (FVRP), fuzzy vehicle routing problem

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Second, the algorithm classification:

★exact algoritms:direct Tree Search, Direct treeview |   Dynamic programming, Motion planning | Integer linear programming, integral linear programming

★classical Heuristics:

Saving heuristic, saving algorithm (Clarke & Wright) Matching algorithm
Sweep algorithm, scanning algorithms (Gillet & Miller)
Two-phase Process, two-stage algorithm (Christofides Fisher Jaikumar)

①route construction Heuristics, PATH construction algorithm

-Savings Heuristic Parallel |  Sequential | Enhancement

-Insertion heuristic

②route improvement heuristics, path improvement algorithm Thompson & Psaraftis

-2-change |  3-change |  1-relocate |  2-relocate |  Swap |  GENI | B-cyclic K-transfer

③composite heuristics (both categories included above)

-Greedy randomized Adaptive search Procedure (GRASP), greedy random adaptive searching process

Column Generation algorithm
Adaptive Memory Algorithms
Ejection Chain Algorithms

Set covering based algorithms

Sequential insertion heuristic

• Hvattum, l.m., A. Løkketangen and G. Laporte. "A Branch-and-prune heuristic for Stochastic and Dynamic Vehicle Routing problems".
• Hvattum, l.m., A. Løkketangen and G. Laporte. "Solving a Dynamic and Stochastic Vehicle Routing problem with a Sample Scenario based Hedging heuristic". Forthcoming in transportation.

★metaheuristics (modern heuristic):

Simulated annealing simulated annealing

Tabu Search Taboo Searches

Genetic ALGORITM Genetic Algorithm | Genetic programming of genetic programming
Genetic network programming genetic networks planning

Memetic Algorithm:hybrid Genetic algorithms is often referred as memetic algorithms.
Parallel Genetic Algorithms | Genetic Local Search

Ant Algorithms:aco
ACS, Ant Colony System ant colony algorithm

Pso,particle Swarm optimization particle swarm optimization algorithm

De,differential Evolution Differential Evolutionary algorithm
Evolutionary programming Evolutionary planning

★local Search: belongs to heuristic algorithm

Variable Neighborhood Search (VNS), change neighborhood searching: Change neighborhood descent Search (VND, descent)
Evolutionary local Search (ELS), evolutionary partial searching
Iterated Local Search (ILS), iterative partial searches
Iterative improvement, iterative improvements
Large Neighborhood Search (LNS), Big Neighborhood Finder | Adaptive Large Neighborhood Search (ALNS)
Guided Local Search (GLS), guided partial searches
Cyclic Transfer Algorithms

★ Neighborhood Operation Method:
2-opt |    2-opt* | Or-opt
The Cross Exchange

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Third, obtained books:

Book chapters: (In vrp->books\)
1. Carlos cotta.evolutionary computation in combinatorial optimization.springer.2007.pdf
(P106) accelerating Local Search in a memetic algorithm for the capacitated Vehicle Routing problem
(P131) A New metaheuristic for the Vehicle Routing problem with Split demands
(P152) Edge Assembly Crossover for the capacitated Vehicle Routing problem
(P176) A population-based Local Search for solving A bi-objective Vehicle Routing problem

2. Christofides (ed.). Combinatorial Optimization (Wiley, 1978) (L) (T) (212s). DjVu

(P159) The Vehicle Routing problems

3. Springer.combinatorial.optimization.and.applications.second.international.conference.cocoa.2008.sep.2008.pdf

(P296) An improved approximation algorithm for the capacitated multicast Tree Routing problem

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Iv. Resources on the Internet:

1. The VRP Web very detailed website

2. Solomon's benchmark DataSet: 56 Datasets R1, R2, C1, C2, RC1, and RC2 are the classic datasets for the VRPTW problem numerical experiment.
The R1 contains 12 questions about the random distribution of customer points. C1 has 9 questions, among which the customer points are clustered to the identified area. The RC1 contains 8 questions, and their customer points are mixed with clustering and random distribution.

3. Network and emerging optimization

4. Or-library of J. E. Beasley: Data sets for various optimization problems.

5. J. E. Beasly's publications

Items on Google code:

1. Vrp-uts-insa2008-5info (Solving the VRP problem with a unified Tabu search in JAVA. That project was a last year study project at INSA Toulouse. ）

2. Vrpsolver (a wizard-like application which solves a basic VRP using the Savings heuristic Method invented by Clarke and Wright in 1964. ）

3. SWAN-VRP

4. EVITA-VRP (Project to unify the EVITA (evolutionary inventory and transportation algorithm), code for VRP book chapter. ）

5. VRPFT (Vehicle Routing problem with full truckloads. ）

Programmers to jointly develop online programs:

1. C # program for vehicle routing optimization based on genetic algorithm

2. Source code for the VRP problem written in C + +

3. A particle swarm algorithm for solving vehicle routing problem source code, written in C + +

4. Ant colony algorithm solves the problem of vehicle optimal path

5. PSO, through iterative search and update to produce better next generation solution

Knowledge collation of VRP related