The Common Optimization INterface for Operations Research: Promoting open-source software in the operations research community

Lougee-Heimer, Robin

doi:10.1147/rd.471.0057

Cited by 285 publications

(102 citation statements)

References 13 publications

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“…The algorithm is implemented in the branch-and-cut-and-price framework from COIN-OR, see Lougee-Heimer (2003), using the COIN-OR opensource LP solver CLP. All tests are run on 2.2 GHz processors.…”

Section: Computational Resultsmentioning

confidence: 99%

The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

Rasmussen

Justesen

Dohn

et al. 2012

European Journal of Operational Research

339

225

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In the Home Care Crew Scheduling Problem a staff of caretakers has to be assigned a number of visits to patients' homes, such that the overall service level is maximised. The problem is a generalisation of the vehicle routing problem with time windows. Required travel time between visits and time windows of the visits must be respected. The challenge when assigning visits to caretakers lies in the existence of soft preference constraints and in temporal dependencies between the start times of visits.We model the problem as a set partitioning problem with side constraints and develop an exact branch-and-price solution algorithm, as this method has previously given solid results for classical vehicle routing problems. Temporal dependencies are modelled as generalised precedence constraints and enforced through the branching. We introduce a novel visit clustering approach based on the soft preference constraints. The algorithm is tested both on real-life problem instances and on generated test instances inspired by realistic settings. The use of the specialised branching scheme on real-life problems is novel. The visit clustering decreases run times significantly, and only gives a loss of quality for few instances. Furthermore, the visit clustering allows us to find solutions to larger problem instances, which cannot be solved to optimality.

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Section: Computational Resultsmentioning

confidence: 99%

The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

Rasmussen

Justesen

Dohn

et al. 2012

European Journal of Operational Research

339

225

View full text Add to dashboard Cite

show abstract

“…-Branch-and-cut for QCQP [18,19,20,21] Audet et al [18] discuss their implementation of a branch-and-cut global optimization algorithm for QCQP which made contributions to generating cutting planes and boundupdating strategies. -Couenne [27,88] Like αBB and BARON, Couenne addresses generic MINLP to ε-global optimality and relaxes bilinear terms using the convex hull. There are several novel branching strategies within Couenne [27].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The code is itself built as a library and linked to GAMS [33] using the GAMS Modeling Object (GMO) interface. The code linking our solver to GAMS is adapted from the COIN-OR/GAMSLinks project [88,136].…”

Section: Testing the Global Mixed-integer Quadratic Optimizer (Glomiqo)mentioning

confidence: 99%

GloMIQO: Global mixed-integer quadratic optimizer

2012

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This paper introduces the Global Mixed-Integer Quadratic Optimizer, GloMIQO, a numerical solver addressing mixed-integer quadratically-constrained quadratic programs (MIQCQP) to ε-global optimality. The algorithmic components are presented for: reformulating user input, detecting special structure including convexity and edge-concavity, generating tight convex relaxations, partitioning the search space, bounding the variables, and finding good feasible solutions. To demonstrate the capacity of GloMIQO, we extensively tested its performance on a test suite of 399 problems of diverse size and structure. The test cases are taken from process networks applications, computational geometry problems, GLOBALLib, MINLPLib, and the Bonmin test set. We compare the performance of GloMIQO with respect to four state-of-the-art global optimization solvers: BARON 10.1.2,

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“…GloMIQO and this framework are equivalent when there are no signomial terms in MISO (c m, s = 0 ∀ s, m). Other relevant codes include αBB [27,[33][34][35][36]; BARON [31,37,38]; Couenne [39,40]; LINDO [41,42]; SCIP [43][44][45][46][47]; and SGO [48][49][50][51]. Observe that αBB, ANTIGONE, BARON, Couenne, LINDO, SCIP, and SGO all admit more functional forms than this computational framework; as with GloMIQO, our focus is careful analysis of special mathematical structure for specific problem classes.…”

Section: Problem Definition and Literature Reviewmentioning

confidence: 99%

A Framework for Globally Optimizing Mixed-Integer Signomial Programs

Misener

Floudas

2013

J Optim Theory Appl

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Mixed-integer signomial optimization problems have broad applicability in engineering. Extending the Global Mixed-Integer Quadratic Optimizer, GloMIQO[1], this manuscript documents a computational framework for deterministically addressing mixed-integer signomial optimization problems to ε-global optimality. This framework generalizes the GloMIQO strategies of (1) reformulating user input, (2) detecting special mathematical structure, and (3) globally optimizing the mixed-integer nonconvex program. Novel contributions of this paper include: flattening an expression tree towards term-based data structures; introducing additional nonconvex terms to interlink expressions; integrating a dynamic implementation of the reformulationlinearization technique into the branch-and-cut tree; designing term-based underestimators that specialize relaxation strategies according to variable bounds in the current tree node. Computational results are presented along with comparison of the computational framework to several state-of-the-art solvers.

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The Common Optimization INterface for Operations Research: Promoting open-source software in the operations research community

Cited by 285 publications

References 13 publications

The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

GloMIQO: Global mixed-integer quadratic optimizer

A Framework for Globally Optimizing Mixed-Integer Signomial Programs

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