gmu-35-50

benchmark benchmark_suitable variable_bound set_packing mixed_binary

Submitter Variables Constraints Density Status Group Objective MPS File
Nora Konnyu 1919 435 1.03538e-02 easy gmu -2607958.33 gmu-35-50.mps.gz

Timber harvest scheduling model These are harvest scheduling models of hypothetical forest planning problems where net timber revenues are maximized over a planning horizon subject to four sets of constraints: 1. Each management unit can be harvested only once over the planning horizon, 2. Volume harvested in one planning period should not be less or more than some portion of that in the preceding period, 3. Area-weighted average age of the forest by the end of the plan should notbe less than a certain target age. 4. Clearcut size in any planning period has to be below a specific limit. Decision variable are management units and generalized management units (group of management units with a combined area not exceeding the limit on clearcut size) and can be either fully harvested or left untouched in any planning period, therefore there is a binary restriction on the decision variables.

Imported from the MIPLIB2010 submissions.

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 1919 1024
Constraints 435 432
Binaries 1914 1019
Integers 0 0
Continuous 5 5
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.0103538 0.0191854
Nonzeroes 8643 8487
Constraint Classification Properties
Original Presolved
Total 435 432
Empty 0 0
Free 0 0
Singleton 0 0
Aggregations 0 0
Precedence 0 0
Variable Bound 16 16
Set Partitioning 0 0
Set Packing 406 403
Set Covering 0 0
Cardinality 0 0
Invariant Knapsack 0 0
Equation Knapsack 0 0
Bin Packing 0 0
Knapsack 0 0
Integer Knapsack 0 0
Mixed Binary 13 13
General Linear 0 0
Indicator 0 0

Structure

Available nonzero structure and decomposition information. Further information can be found here.

value min median mean max
Components 0.845098
Constraint % 1.851850 16.1651 10.1852 46.7593
Variable % 0.486855 16.6180 13.6806 41.6748
Score 0.673548

Best Known Solution(s)

Find solutions below. Download the archive containing all solutions from the Download page.

ID Objective Exact Int. Viol Cons. Viol Obj. Viol Submitter Date Description
2 -2607958 -2607958 0 0 0 - 2018-10-29 Solution found during MIPLIB2017 problem selection.
1 -2607958 -2607958 0 0 0 - 2018-10-12 Solution imported from MIPLIB2010.

Similar instances in collection

The following instances are most similar to gmu-35-50 in the collection. This similarity analysis is based on 100 scaled instance features describing properties of the variables, objective function, bounds, constraints, and right hand sides.

Instance Status Variables Binaries Integers Continuous Constraints Nonz. Submitter Group Objective Tags
gmu-35-40 easy 1205 1200 0 5 424 4843 Nora Konnyu gmu -2406733.3688 benchmark benchmark_suitable variable_bound set_packing mixed_binary
gmut-76-40 open 24338 24332 0 6 2586 153017 Nora Konnyu gmu -14169441.78* variable_bound set_packing mixed_binary
cap6000 easy 6000 6000 0 0 2176 48243 MIPLIB submission pool -2451377 binary decomposition aggregations precedence variable_bound set_partitioning set_packing knapsack
gmut-76-50 open 68865 68859 0 6 2586 470045 Nora Konnyu gmu -14171397.828* variable_bound set_packing mixed_binary
gmut-75-50 hard 68865 68859 0 6 2565 571475 Nora Konnyu gmu -14180699.047 variable_bound set_packing mixed_binary

Reference

No bibliographic information available

Last Update Jun 24, 2019 by Gregor Hendel
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