Record Breaking Optimization Results Using the Ruin and Recreate Principle

By G. Schrimpf, Johannes Josef Schneider, H. Stamm-Wilbrandt, G. Dueck. 2000

462 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/4f80e70e51e368858c3df0787f05c3aa2b9650b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Javier Ferrer
2. Enrique Alba
3. Thomas Stützle
4. Stefan Røpke
5. Michel Gendreau
6. Helena Ramalhinho Lourenço
7. David Pisinger
8. Olivier C. Martin

In Computer Vision and Pattern Recognition

1. Stefan Røpke
2. David Pisinger
3. Michel Gendreau
4. Olli Bräysy
5. Javier Ferrer
6. Enrique Alba
7. Thomas Stützle
8. Helena Ramalhinho Lourenço

In Industrial and Manufacturing Engineering

1. Michel Gendreau
2. Stefan Røpke
3. David Pisinger
4. Thomas Stützle
5. Enrique Alba
6. Gilbert Laporte
7. Olli Bräysy
8. Teodor Gabriel Crainic

Citations in the corpus

• A general heuristic for vehicle routing problems (2007, methodology): “[33], or the Ripup and Reroute paradigm applied in [34].”
• The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations (2016, unknown): “In Shaw (1998) the author presents a search procedure for a larger neighbourhood defined by a destroy and repair operation.”
• An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows (2006, background): “The vehicle minimization algorithm only works for homogeneous fleets without an upper bound on the number of vehicles available.”
• A unified heuristic for a large class of Vehicle Routing Problems with Backhauls (2006, methodology): “The heuristic is based on Large Neighborhood Search (LNS) as proposed by Shaw [35] and it has similarities with the Ruin and Recreate (R&R) framework proposed by Schrimpf et al. [33].”
• BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level (2018, methodology): “Given a set of containers, we use an evolutionary algorithm EA(1 + 1) based on the principle of ruin and recreation (Schrimpf et al., 2000) that allows us to generate e ffi cient collection routes, taking into account the distance and the driving time between them.”
• A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows (2004, unknown): “…2000), HG = (Homberger and Gehring 1999), IKM = (Ibaraki et al. 2001), IKP = (Ioannou et al. 2001), PB = (Potvin and Begio 1996), RGP = (Rousseau et al. 2002), RT = (Rochat and Taillard 1995), S = (Shaw 1998), SSS = (Schrimpf et al. 2000), TBG = (Taillard et al. 1997), TOS = (Thangiah et al. 1994).”
• Introduction to Global Optimization (2006, unknown): “In the stochastic global optimization field, recent advances include Simulated and Nested Annealing [Raj00, Loc02], Tabu Search (TS) [CS00, KVvA + 99], Multi-Level Single Linkage (MLSL) [Sch02, KS04], Variable Neighbourhood Search (VNS) [MPKVv04], Differential Evolution [SP97b], Adaptive Lagrange-Mult”
• Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms (2005, methodology): “The only stochastic approaches are that of Russell (1995), Shaw (1997 and 1998) and Schrimpf et al. (2000) do not report the software used.”
• Iterated Local Search: Framework and Applications (2018, background): “algorithms [45, 80], is to perturb the current solution by a destruction/construction mechanism.”