12 research outputs found
Efficient procedures for the weighted squared tardiness permutation flowshop scheduling problem
A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty
Mode generation rules to define activity flexibility for the integrated project staffing problem with discrete time/resource trade-offs
Distributed permutation flowshop scheduling problem with total completion time objective
Mixed integer linear programming models for Flow Shop Scheduling with a demand plan of job types
Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration
Iterated Greedy
Iterated greedy is a search method that iterates through applications of construction heuristics using the repeated execution of two main phases, the partial destruction of a complete candidate solution and a subsequent reconstruction of a complete candidate solution. Iterated greedy is based on a simple principle, and methods based on this principle have been proposed and published several times in the literature under different names such as simulated annealing, iterative flattening, ruin-and-recreate, large neighborhood search, and others. Despite its simplicity, iterated greedy has led to rather high-performing algorithms. In combination with other heuristic optimization techniques such as a local search, it has given place to state-of-the-art algorithms for various problems. This paper reviews the main principles of iterated greedy algorithms, relates the basic technique to the various proposals based on this principle, discusses its relationship with other optimization techniques, and gives an overview of problems to which iterated greedy has been successfully applied.SCOPUS: ch.binfo:eu-repo/semantics/publishe