MiniBrass: Soft constraints for MiniZinc

Schiendorfer, Alexander; Knapp, Alexander; Anders, Gerrit; Reif, Wolfgang

doi:10.1007/s10601-018-9289-2

Cited by 7 publications

(3 citation statements)

References 53 publications

(100 reference statements)

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“…Another possible future work is to integrate our proposal into a larger assignment optimization platform to reduce the workload of the clinical staff. Furthermore, multi-objective optimization is a promising perspective, since various criteria might need to added to our approach (e.g., skylines [9,13,22]) for getting optimal solutions w.r.t various criteria simultaneously or even test other types of soft constraints [19,21] to represent better user preferences into the model.…”

Section: Discussionmentioning

confidence: 99%

Constraint Programming for the Pandemic in Peru

Ugarte

2021

Communications in Computer and Information Science

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Currently, the world requires techniques that match infected people and hospital beds together given various criteria such as the severity of infection, patient location, hospital capacity, etc. Deep Learning might seems to be a perfect fit for this: various configurations from a broad range of parameters that need to be reduced to a few solutions. But, this models require to be trained, hence the need for historical data on previous cases leading to a waste of time would in cleaning and consolidating a dataset and lengthy training sessions need to be performed with a variety of architectures.Nevertheless, formulating this problem as a Constraint Satisfaction Problem (CSP), the aforementioned downsides will not be present while still optimal results, and without the need for any historical data. In this paper, a CSP model is used to search for the best distribution of COVID-19 patients with a severity of patients requiring hospitalization and patients requiring ICU beds, in hospitals in a part of Lima.

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Section: Discussionmentioning

confidence: 99%

Constraint Programming for the Pandemic in Peru

Ugarte

2021

Communications in Computer and Information Science

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“…Below we discuss how we handle the one soft constraint relevant to the case study. It is possible to include soft constraints in the model, whereupon a soft CP solving approach is relevant (Schiendorfer et al 2018).…”

Section: Objective and Constraintsmentioning

confidence: 99%

Berth planning and real-time disruption recovery: a simulation study for a tidal port

Steeg

Oudshoorn²,

Yorke-Smith

2022

Flex Serv Manuf J

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With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year’s operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.

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“…Training was performed in batches of 64 samples using the AdamW optimizer [21], with a learning rate set at 10 −5 . The simulations were implemented in Python and NetLogo, the NN in PyTorch [24] and the CP model in MiniZinc [22], supported by MiniBrass [26] to implement soft-constraints. The JaCoP [17] WCSP solver was used.…”

Section: Experimental Validationmentioning

confidence: 99%

Flexible Enterprise Optimization with Constraint Programming

Andringa

Yorke-Smith

2022

Advances in Enterprise Engineering XV

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Simulation-optimization is often used in enterprise decisionmaking processes, both operational and tactical. This paper shows how an intuitive mapping from descriptive problem to optimization model can be realized with Constraint Programming (CP). It shows how a CP model can be constructed given a simulation model and a set of business goals. The approach is to train a neural network (NN) on simulation model inputs and outputs, and embed the NN into the CP model together with a set of soft constraints that represent business goals. We study this novel simulation-optimization approach through a set of experiments, finding that it is flexible to changing multiple objectives simultaneously, allows an intuitive mapping from business goals expressed in natural language to a formal model suitable for state-of-the-art optimization solvers, and is realizable for diverse managerial problems.

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MiniBrass: Soft constraints for MiniZinc

Cited by 7 publications

References 53 publications

Constraint Programming for the Pandemic in Peru

Constraint Programming for the Pandemic in Peru

Berth planning and real-time disruption recovery: a simulation study for a tidal port

Flexible Enterprise Optimization with Constraint Programming

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