In sensitive applications, such as drug development, offering experts an explanation for why data mining operations arrive at certain results adds a very valuable facet. In this work we benefit from modelling the task...
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ISBN:
(数字)9783031300479
ISBN:
(纸本)9783031300462;9783031300479
In sensitive applications, such as drug development, offering experts an explanation for why data mining operations arrive at certain results adds a very valuable facet. In this work we benefit from modelling the task as a constraint Satisfaction Problem (CSP) twice: by adding multiple constraints to the mining process and by deriving pattern failure explanations. We illustrate experimentally how to apply our method on data originally retrieved from the ChEMBL database [14]. We also report some interesting dependencies discovered by our method which are not easy to observe when analysing data manually.
The flexible job-shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is one of the most difficult problems in this area. In fact, this problem is a generalized a...
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Drones are currently seen as a viable way for improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles like trucks. In this paper we consider the p...
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Drones are currently seen as a viable way for improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles like trucks. In this paper we consider the parallel drone scheduling vehicle routing problem, where the service of a set of customers requiring a delivery is split between a fleet of trucks and a fleet of drones. We consider two variations of the problem. In the first one, the problem is more theoretical, and the target is the minimization of the time required to complete the service and have all the vehicles back to the depot. In the second variant, more realistic constraints involving operating costs, capacity limitation and workload balance, are considered, and the target is to minimize the total operational costs. We propose different constraint programming models to deal with the two problems. An experimental champaign on the instances previously adopted in the literature is presented to validate the new solving methods. The results show that, on top of being a viable way to solve problems to optimality, the models can also be used to derive effective heuristic solutions and high-quality lower bounds for the optimal cost, if the execution is interrupted before its natural end.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY-NC-ND license (http:// creativecommons .org /licenses /by -nc -nd /4 .0/).
Recent years have seen the rise of constraint programming (CP) to address pattern mining tasks. To deal with numerical data, many of the approaches require a prior discretization of the attributes by using a scaling p...
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Flight-gate assignment problems are complex real world problems involving different constraints. Some of these constraints include plane-gate eligibility, assigning planes of the same airline and planes getting servic...
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Flight-gate assignment problems are complex real world problems involving different constraints. Some of these constraints include plane-gate eligibility, assigning planes of the same airline and planes getting service from the same ground handling companies to adjacent gates, buffers for changes in flight schedules, night stand flights, priority of some gates over others, and so on. In literature there are numerous models to solve this highly complicated problem and tackle its complexity. In this study, first, we propose two different integer programming models, namely, timetabling and assignment based models, and then a scheduling based constraint programming model to solve the problem to optimality. These models prove to be highly efficient in that the computational times are quite short. We also present the results for one day operation of an airport using real data. Finally, we present our conclusions based on our study along with the possible further research.
The liner shipping network design problem consists, for a shipowner, in determining, on the one hand, which maritime lines (in the form of rotations allowing to serve a set of ports) to open, and, on the other hand, t...
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The Open Pit Mine Production Scheduling Problem (OPMPSP) is a complex optimization problem that seeks to determine the best sequence for extracting mineralized material from the ground in order to maximize the Net Pre...
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The guillotine rectangular cutting problem deals with a single rectangular plate of raw material and a collection of rectangular items to be cut from the plate. Each item is associated with a profit and a demand. The ...
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Short-term underground mine planning problems are often difficult to solve due to the large number of activities and diverse machine types to be scheduled, as well as multiple operational constraints. This paper prese...
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This paper presents constraint programming-based solution approaches for the three-dimensional loading capacitated vehicle routing problem (3l-CVRP) that consists of vehicle routing and three-dimensional loading probl...
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This paper presents constraint programming-based solution approaches for the three-dimensional loading capacitated vehicle routing problem (3l-CVRP) that consists of vehicle routing and three-dimensional loading problems in distribution logistics. Despite the practical benefits in the logistics world, the 3l-CVRP has not been extensively studied in the literature for its high combinatorial complexity. Therefore, we developed integrated and decomposed constraint programming-based solution methods in this study. The decomposed models outperformed the mixed-integer programming model proposed earlier in the literature for small-size problems. Furthermore, we solved the well-known benchmark problems with a decomposed model using constraint programming for the vehicle routing part of the problem and an evolutionary algorithm for the loading part. The computational study results show that the best-known results are improved in 36 of 93 problems.
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