Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Relaxed heaps: an alternative to Fibonacci heaps with applications to parallel computation
Communications of the ACM
Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
A tabu search heuristic for the vehicle routing problem
Management Science
Ejection chains, reference structures and alternating path methods for traveling salesman problems
Discrete Applied Mathematics - Special volume: first international colloquium on graphs and optimization (GOI), 1992
A Subpath Ejection Method for the Vehicle Routing Problem
Management Science
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Tabu Search
The Granular Tabu Search and Its Application to the Vehicle-Routing Problem
INFORMS Journal on Computing
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Very large-scale vehicle routing: new test problems, algorithms, and results
Computers and Operations Research
Solving the vehicle routing problem with adaptive memory programming methodology
Computers and Operations Research
Sequential search and its application to vehicle-routing problems
Computers and Operations Research
A general heuristic for vehicle routing problems
Computers and Operations Research
Active-guided evolution strategies for large-scale capacitated vehicle routing problems
Computers and Operations Research
INFORMS Journal on Computing
A Unified Modeling and Solution Framework for Vehicle Routing and Local Search-Based Metaheuristics
INFORMS Journal on Computing
Edge assembly crossover for the capacitated vehicle routing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Efficient local search limitation strategies for vehicle routing problems
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
An effective local search approach for the Vehicle Routing Problem with Backhauls
Expert Systems with Applications: An International Journal
Annals of Mathematics and Artificial Intelligence
The Pallet-Packing Vehicle Routing Problem
Transportation Science
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This article focuses on the mechanism of evaluating solution neighborhoods, an algorithmic aspect which plays a crucial role on the efficiency of local-search based approaches. In specific, it presents a strategy for reducing the computational complexity required for applying local search to tackle various combinatorial optimization problems. The value of this contribution is two-fold. It helps practitioners design efficient local search implementations, and it facilitates the application of robust commercial local search-based algorithms to practical instances of very large size. The central rationale underlying the proposed complexity reduction strategy is straightforward: when a local search operator is applied to a given solution, only a limited part of this solution is modified. Thus, to exhaustively examine the neighborhood of the new solution, only the tentative moves that refer to the modified solution part have to be evaluated. To reduce the complexity of neighborhood evaluation, the static move descriptor (SMD) data structures are introduced, which encode local search moves in a systematic and solution independent manner. The proposed strategy is applied to the vehicle routing problem (VRP) which is of high importance both from the practical and theoretical viewpoints. The use of the SMD concept, for encoding three commonly applied quadratic local search operators, results into a VRP local search method which exhibits an almost linearithmic complexity in respect to the instance size. Furthermore, exploiting the SMD representation of tentative moves, a metaheuristic strategy is proposed, which is aimed at diversifying the conducted search via a simple penalization policy. The proposed metaheuristic was tested on various large and very large scale VRP benchmark instances. It produced fine results, and managed to improve several best known solutions. The method was also executed on real-world instances of 3000 customers, the data of which reflects the actual geographic distribution of customers within four major cities.