Pivot, Cut, and Dive: a heuristic for 0-1 mixed integer programming
Journal of Heuristics
Repairing MIP infeasibility through local branching
Computers and Operations Research
Computers and Operations Research
The core concept for 0/1 integer programming
CATS '08 Proceedings of the fourteenth symposium on Computing: the Australasian theory - Volume 77
Generating Multiple Solutions for Mixed Integer Programming Problems
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
DINS, a MIP Improvement Heuristic
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Optimised Search Heuristic Combining Valid Inequalities and Tabu Search
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid
INFORMS Journal on Computing
Decomposition, reformulation, and diving in university course timetabling
Computers and Operations Research
Computers and Operations Research
The traveling tournament problem with predefined venues
Journal of Scheduling
Variable neighbourhood decomposition search for 0-1 mixed integer programs
Computers and Operations Research
Iterative Relaxation-Based Heuristics for the Multiple-choice Multidimensional Knapsack Problem
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
A Generalized Wedelin Heuristic for Integer Programming
INFORMS Journal on Computing
A GRASP and branch-and-bound metaheuristic for the job-shop scheduling
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
A parallel macro partitioning framework for solving mixed integer programs
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Combining Exact and Heuristic Approaches for the Capacitated Fixed-Charge Network Flow Problem
INFORMS Journal on Computing
An ILP improvement procedure for the Open Vehicle Routing Problem
Computers and Operations Research
Alternating control tree search for knapsack/covering problems
Journal of Heuristics
New hybrid matheuristics for solving the multidimensional knapsack problem
HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
A Lagrangian heuristic for satellite range scheduling with resource constraints
Computers and Operations Research
Improving CP-based local branching via sliced neighborhood search
Proceedings of the 2011 ACM Symposium on Applied Computing
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Improved core problem based heuristics for the 0/1 multi-dimensional knapsack problem
Computers and Operations Research
Computers and Operations Research
Solving production scheduling with earliness/tardiness penalties by constraint programming
Journal of Intelligent Manufacturing
The multi-commodity one-to-one pickup-and-delivery traveling salesman problem: a matheuristic
INOC'11 Proceedings of the 5th international conference on Network optimization
Expert Systems with Applications: An International Journal
Experiments with a feasibility pump approach for nonconvex MINLPs
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Computers and Electronics in Agriculture
MIP formulations and heuristics for two-level production-transportation problems
Computers and Operations Research
Heuristics for convex mixed integer nonlinear programs
Computational Optimization and Applications
Nonconvex, lower semicontinuous piecewise linear optimization
Discrete Optimization
Conflict analysis in mixed integer programming
Discrete Optimization
A feasibility pump heuristic for general mixed-integer problems
Discrete Optimization
Improving the feasibility pump
Discrete Optimization
Bounding, filtering and diversification in CP-based local branching
Journal of Heuristics
Branch-and-Price guided search
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
An empirical evaluation of walk-and-round heuristics for mixed integer linear programs
Computational Optimization and Applications
Improved Load Plan Design Through Integer Programming Based Local Search
Transportation Science
A set-covering based heuristic algorithm for the periodic vehicle routing problem
Discrete Applied Mathematics
Feasibility Pump-like heuristics for mixed integer problems
Discrete Applied Mathematics
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Given a feasible solution to a Mixed Integer Programming (MIP) model, a natural question is whether that solution can be improved using local search techniques. Local search has been applied very successfully in a variety of other combinatorial optimization domains. Unfortunately, local search relies extensively on the notion of a solution neighborhood, and this neighborhood is almost always tailored to the structure of the particular problem being solved. A MIP model typically conveys little information about the underlying problem structure. This paper considers two new approaches to exploring interesting, domain-independent neighborhoods in MIP. The more effective of the two, which we call Relaxation Induced Neighborhood Search (RINS), constructs a promising neighborhood using information contained in the continuous relaxation of the MIP model. Neighborhood exploration is then formulated as a MIP model itself and solved recursively. The second, which we call guided dives, is a simple modification of the MIP tree traversal order. Loosely speaking, it guides the search towards nodes that are close neighbors of the best known feasible solution. Extensive computational experiments on very difficult MIP models show that both approaches outperform default CPLEX MIP and a previously described approach for exploring MIP neighborhoods (local branching) with respect to several different metrics. The metrics we consider are quality of the best integer solution produced within a time limit, ability to improve a given integer solution (of both good and poor quality), and time required to diversify the search in order to find a new solution.