This paper analyzes the performance of five well-known off-the-shelf
optimization solvers on a set of mixed-integer conic programs proposed for the
congested capacitated facility location problem. We aim to compare the
computational efficiency of the solvers and examine the solution strategies
they adopt when solving instances with different sizes and complexity.
The solvers we compare are Gurobi, Cplex, Mosek, Xpress, and Scip. We run
extensive numerical tests on a testbed of 30 instances from the literature. Our
results show that Mosek and Gurobi are the most competitive solvers, as they
achieve better time and gap performance, solving most instances within the time
limit. Mosek outperforms Gurobi in large-size problems and provides more
accurate solutions in terms of feasibility. Xpress solves to optimality about
half of the instances tested within the time limit, and in this half, it
achieves performance similar to that of Gurobi and Mosek. Cplex and Scip emerge
as the least competitive solvers. The results provide guidelines on how each
solver behaves on this class of problems and highlight the importance of
choosing a solver suited to the problem type.
Dettaglio pubblicazione
2023, , Pages -
Off-the-shelf solvers for mixed-integer conic programming: insights from a computational study on congested capacitated facility location instances (13b Working paper)
Avella Pasquale, Calamita Alice, Palagi Laura
Gruppo di ricerca: Continuous Optimization
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