We consider a real problem faced by a large company providing repair services of office machines in Santiago, Chile. In a typical day about twenty technicians visit seventy customers in a predefined service area in Sa...
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We consider a real problem faced by a large company providing repair services of office machines in Santiago, Chile. In a typical day about twenty technicians visit seventy customers in a predefined service area in Santiago. We design optimal routes for technicians by considering travel times, soft time windows for technician arrival times at client locations, and fixed repair times. A branch-and-price algorithm was developed, using a constraint branching strategy proposed by Ryan and Foster along with constraint programming in the column generation phase. The column generation takes advantage of the fact that each technician can satisfy no more than five to six service requests per day. Different instances of the problem were solved to optimality in a reasonable computational time, and the results obtained compare favorably with the current practice. (C) 2014 Elsevier B.V. All rights reserved.
constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions i...
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constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. Although several generic variable-selection heuristics are available in the literature, the options for value-selection heuristics are more scarce. We propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network. Experiments on graph coloring, maximum independent set, maximum cut, and minimum vertex cover problems show that this framework competes with the well-known impact-based and activity-based search heuristics and can find solutions close to optimality without requiring a large number of backtracks. Additionally, we observe that fine-tuning a model with a different problem class can accelerate the learning process.
constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relev...
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constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relevant problem instances. This gives rise to the question of how to select a promising solver when knowing the concrete instance to be solved. In this article, we aim to provide first insights into this question for the flexible job shop scheduling problem. We investigate relative performance differences among five constraint programming solvers on problem instances taken from the literature as well as randomly generated problem instances. These solvers include commercial and non-commercial software and represent the state-of-the-art as identified in the relevant literature. We find that two solvers, the IBM ILOG CPLEX CP Optimizer and Google's OR-Tools, outperform alternative solvers. These two solvers show complementary strengths regarding their ability to determine provably optimal solutions within practically reasonable time limits and their ability to quickly determine high quality feasible solutions across different test instances. Hence, we leverage the resulting performance complementarity by proposing algorithm selection approaches that predict the best solver for a given problem instance based on instance features or parameters. The approaches are based on two machine learning techniques, decision trees and deep neural networks, in various variants. In a computational study, we analyze the performance of the resulting algorithm selection models and show that our approaches outperform the use of a single solver and should thus be considered as a relevant tool by decision makers in practice. (C) 2022 Elsevier B.V. All rights reserved.
During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obta...
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During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
In this paper, we consider the problem of scheduling sports competitions over several venues which are not associated with any of the competitors. A two-phase, constraint programming approach is developed, first ident...
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In this paper, we consider the problem of scheduling sports competitions over several venues which are not associated with any of the competitors. A two-phase, constraint programming approach is developed, first identifying a solution that designates the participants and schedules each of the competitions, then assigning each competitor as the "home" or the "away" team. Computational experiments are conducted and the results are compared with an integer goal programming approach. The constraint programming approach achieves optimal solutions for problems with up to sixteen teams, and near-optimal solutions for problems with up to thirty teams. (c) 2004 Elsevier Ltd. All rights reserved.
This paper investigates the routing of pressurized tank trailers and proposes a scheduling plan which ensures the practical delivery of industrial gases under the objective of reducing transportation costs. Using cons...
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This paper investigates the routing of pressurized tank trailers and proposes a scheduling plan which ensures the practical delivery of industrial gases under the objective of reducing transportation costs. Using constraint programming, we solve a combinatorial optimization problem that incorporates both hard and soft constraints for routing and scheduling tank trailers. Hard constraints are resource and safety/regulation constraints, whereas soft constraints are utilization and efficiency constraints. This approach enables tank-trailer routing and scheduling management to consider different combinations of parameters and view the results in 'real-time.' The routing and scheduling results based on a case study in Taiwan fulfil the goals of avoiding risks associated with transporting industrial gases, and attaining efficient delivery while conforming to regulations and consistent with good business practice. The results also suggest that significant economies in distribution costs are possible.
In this paper, we present a constraint programming model for the routing and scheduling of trains running through a junction. The model uses input data from relevant time events of train runs calculated by a simulator...
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In this paper, we present a constraint programming model for the routing and scheduling of trains running through a junction. The model uses input data from relevant time events of train runs calculated by a simulator. The model can be integrated into a decision support system used by operators who make decisions to change train routes or orders to avoid conflicts and delays. The model has been applied to a set of problem instances. This set has been defined from a real case study of traffic on the Pierrefitte-Gonesse node, North of Paris. Preliminary results show that the solution identified by the model yields a significant improvement in performance within an acceptable computation time. (c) 2006 Elsevier Ltd. All rights reserved.
Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a constraint programming approach capable of automating the short-term scheduling process in a c...
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Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a constraint programming approach capable of automating the short-term scheduling process in a cut-and-fill mine. The approach extends previous work by accounting for fleet travel times, and thus captures an important aspect of the real-world machine scheduling problem. We introduce two models: one that directly solves the original interruptible scheduling problem, and one that is based on solving a related uninterruptible scheduling problem and transforming its solution back to the original domain. Large Neighborhood Search is also employed with a domain-specific neighborhood definition that helps to find high-quality schedules faster. Problem instances derived from an operational mine are used to demonstrate the efficacy of our approach. (C) 2020 Elsevier Ltd. All rights reserved.
The solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALB...
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The solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity.
In this manuscript we present an original implementation of network management functions in the context of Software Defined Networking. We demonstrate a full integration of an artificial intelligence driven management...
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In this manuscript we present an original implementation of network management functions in the context of Software Defined Networking. We demonstrate a full integration of an artificial intelligence driven management, an SDN control plane, and a programmable data plane. constraint programming is used to implement a management operating system that accepts high level specifications, via a northbound interface, in terms of operational objective and directives. These are translated in technology-specific constraints and directives for the SDN control plane, leveraging the programmable data plane, which is enriched with functionalities suited to feed data that enable the most effective operation of the "intelligent" control plane, by exploiting the P4 language.
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