Integer and combinatorial optimization
Integer and combinatorial optimization
A global approach to crew-pairing optimization
IBM Systems Journal
A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
Solving airline crew scheduling problems by branch-and-cut
Management Science
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Tabu Search
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Drive: Dynamic Routing of Independent Vehicles
Operations Research
A Heuristic Method for the Set Covering Problem
Operations Research
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A Set-Covering-Based Heuristic Approach for Bin-Packing Problems
INFORMS Journal on Computing
Selected Topics in Column Generation
Operations Research
An Optimization-Based Heuristic for the Split Delivery Vehicle Routing Problem
Transportation Science
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Heuristics for a real-world mail delivery problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Hybrid column generation and large neighborhood search for the dial-a-ride problem
Computers and Operations Research
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We propose a new generic framework for solving combinatorial optimization problems that can be modeled as a set covering problem. The proposed algorithmic framework combines metaheuristics with exact algorithms through a guiding mechanism based on diversification and intensification decisions. After presenting this generic framework, we extensively demonstrate its application to the vehicle routing problem with time windows. We then conduct a thorough computational study on a set of well-known test problems, where we show that the proposed approach not only finds solutions that are very close to the best-known solutions reported in the literature, but also improves them. We finally set up an experimental design to analyze the effects of different parameters used in the proposed algorithm.