scholarly journals MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library

2021 ◽  
Author(s):  
Ambros Gleixner ◽  
Gregor Hendel ◽  
Gerald Gamrath ◽  
Tobias Achterberg ◽  
Michael Bastubbe ◽  
...  
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Optimization Problems ◽  
Selection Process ◽  
Data Driven ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Programming Library ◽  
And Performance ◽  
First Time

AbstractWe report on the selection process leading to the sixth version of the Mixed Integer Programming Library, MIPLIB 2017. Selected from an initial pool of 5721 instances, the new MIPLIB 2017 collection consists of 1065 instances. A subset of 240 instances was specially selected for benchmarking solver performance. For the first time, these sets were compiled using a data-driven selection process supported by the solution of a sequence of mixed integer optimization problems, which encode requirements on diversity and balancedness with respect to instance features and performance data.

scholarly journals A Data-Driven Hybrid Three-Stage Framework for Hospital Bed Allocation: A Case Study in a Large Tertiary Hospital in China

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Li Luo ◽  
Jialing Li ◽  
Xueru Xu ◽  
Wenwu Shen ◽  
Lin Xiao
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Tertiary Hospital ◽  
Data Driven ◽  
Mixed Integer ◽  
Hospital Bed ◽  
Tertiary Hospitals ◽  
Bed Allocation

Beds are key, scarce medical resources in hospitals. The bed occupancy rate (BOR) amongst different departments within large tertiary hospitals is very imbalanced, a situation which has led to problems between the supply of and the demand for bed resources. This study aims to balance the utilization of existing beds in a large tertiary hospital in China. We developed a data-driven hybrid three-stage framework incorporating data analysis, simulation, and mixed integer programming to minimize the gaps in BOR among different departments. The first stage is to calculate the length of stay (LOS) and BOR of each department and identify the departments that need to be allocated beds. In the second stage, we used a fitted arrival distribution and median LOS as the input to a generic simulation model. In the third stage, we built a mixed integer programming model using the results obtained in the first two stages to generate the optimal bed allocation strategy for different departments. The value of the objective function, Z, represents the severity of the imbalance in BOR. Our case study demonstrated the effectiveness of the proposed data-driven hybrid three-stage framework. The results show that Z decreases from 0.7344 to 0.0409 after re-allocation, which means that the internal imbalance has eased. Our framework provides hospital bed policy makers with a feasible solution for bed allocation.

scholarly journals The MiniZinc Challenge 2008–2013

AI Magazine ◽  
2014 ◽  
Vol 35 (2) ◽  
pp. 55-60 ◽  
Author(s):  
Peter J. Stuckey ◽  
Thibaut Feydy ◽  
Andreas Schutt ◽  
Guido Tack ◽  
Julien Fischer
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Constraint Programming ◽  
Optimization Problems ◽  
Mixed Integer ◽  
Modeling Language

MiniZinc is a solver agnostic modeling language for defining and solver combinatorial satisfaction and optimization problems. MiniZinc provides a solver independent modeling language which is now supported by constraint programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Since 2008 we have run the MiniZinc challenge every year, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models. Here we report on what we have learnt from running the competition for 6 years.

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