This paper deals with an application of constraint programming in production scheduling with earliness and tardiness penalties that reflects the scheduling part of the Just-In-Time inventory strategy. Two scheduling p...
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This paper deals with an application of constraint programming in production scheduling with earliness and tardiness penalties that reflects the scheduling part of the Just-In-Time inventory strategy. Two scheduling problems are studied, an industrial case study problem of lacquer production scheduling, and also the job-shop scheduling problem with earliness/tardiness costs. The paper presents two algorithms that help the constraint programming solver to find solutions of these complex problems. The first algorithm, called the cost directed initialization, performs a greedy initialization of the search tree. The second one, called the time reversing transformation and designed for lacquer production scheduling, reformulates the problem to be more easily searchable when the default search or the cost directed initialization is used. The conducted experiments, using case study instances and randomly generated problem instances, show that our algorithms outperform generic approaches, and on average give better results than other nontrivial algorithms.
Answer Set programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models can be hard. We propose a novel method, called Sketched Answer Set programming (SkASP), aimed at fa...
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ISBN:
(纸本)9781538674499
Answer Set programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models can be hard. We propose a novel method, called Sketched Answer Set programming (SkASP), aimed at facilitating this. In SkASP, the user writes partial ASP programs, in which uncertain parts are left open and marked with question marks. In addition, the user provides a number of positive and negative examples of the desired program behaviour. SkASP then synthesises a complete ASP program. This is realized by rewriting the SkASP program into another ASP program, which can then be solved by traditional ASP solvers. We evaluate our approach on 21 well known puzzles and combinatorial problems inspired by Karps 21 NP-complete problems and on publicly available ASP encodings.
The concept of symmetry has been extensively studied in the field of constraint programming and in the propositional satisfiability. Several methods for detection and removal of these symmetries have been developed, a...
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The concept of symmetry has been extensively studied in the field of constraint programming and in the propositional satisfiability. Several methods for detection and removal of these symmetries have been developed, and their use in known solvers of these domains improved dramatically their effectiveness on a big variety of problems considered difficult to solve. The concept of symmetry may be exported to other areas where some structures can be exploited effectively. Particularly, in the area of data mining where some tasks can be expressed as constraints or logical formulas. We are interested here, by the detection and elimination of local and global symmetries in the item-set mining problem. Recent works have provided effective encodings as Boolean constraints for these data mining tasks and some idea on symmetry elimination in this area begin to appear, but still few and the techniques presented are often on global symmetry that is detected and eliminated statically in a preprocessing phase. In this work we study the notion of local symmetry and compare it to global symmetry for the itemset mining problem. We show how local symmetries of the boolean encoding can be detected dynamically and give some properties that allow to eliminate theses symmetries in SAT-based itemset mining solvers in order to enhance their efficiency.
Statistical model learning problems are traditionally solved using either heuristic greedy optimization or stochastic simulation, such as Markov chain Monte Carlo or simulated annealing. Recently, there has been an in...
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Statistical model learning problems are traditionally solved using either heuristic greedy optimization or stochastic simulation, such as Markov chain Monte Carlo or simulated annealing. Recently, there has been an increasing interest in the use of combinatorial search methods, including those based on computational logic. Some of these methods are particularly attractive since they can also be successful in proving the global optimality of solutions, in contrast to stochastic algorithms that only guarantee optimality at the limit. Here we improve and generalize a recently introduced constraint-based method for learning undirected graphical models. The new method combines perfect elimination orderings with various strategies for solution pruning and offers a dramatic improvement both in terms of time and memory complexity. We also show that the method is capable of efficiently handling a more general class of models, called stratified/labeled graphical models, which have an astronomically larger model space.
Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rate...
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Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na+/K+ ATPase, which hydrolyzes 1 ATP to move 3 Na+ outside and 2 K+ inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na+ and K+ ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by C-13-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na+ and K+ fluxes generated by neuronal activity was close to a 1: 1 relationship, and specifically 63/58 Na+/K+ ions per glutamate released. We found that astrocytes are stimulated by the extracellular K+ exiting neurons in excess of the 3/2 Na+/K+ ratio underlying Na+/K+ ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K+ uptake, but not astrocytic Na+-coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K+ in stimulating the activation of astrocytes, which is rele
Persistent calls come from within the graduate medical education community and from external sources for regulating the resident duty hours in order to meet the obligations about the quality of resident education, the...
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Persistent calls come from within the graduate medical education community and from external sources for regulating the resident duty hours in order to meet the obligations about the quality of resident education, the well-being of residents themselves, and the quality of patient care services. The report of the Accreditation Council for Graduate Medical Education (ACGME) proposes common program requirements for resident hours. In this paper, we first develop a mixed-integer programming model for scheduling residents' duty hours considering the on-call night, day-off, rest period, and total work-hour ACGME regulations as well as the demand coverage requirements of the residency program. Subsequently, we propose a column generation model that consists of a master problem and an auxiliary problem. The master problem finds a configuration of individual schedules that minimizes the sum of deviations from the desired service levels for the day and night periods. The formulation of this problem is possible by representing the feasible schedules using column variables, whereas the auxiliary problem finds the whole set of feasible schedules using constraint programming. The proposed approach has been tested on a series of problems using real data obtained from a hospital. The results indicate that high-quality schedules can be obtained within a few seconds. (C) 2010 Elsevier Ltd. All rights reserved.
Multi-agent path finding (MAPF) deals with the problem of finding a collision-free path for a set of agents. The agents are located at nodes of a directed graph, they can move over the arcs, and each agent has its own...
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Multi-agent path finding (MAPF) deals with the problem of finding a collision-free path for a set of agents. The agents are located at nodes of a directed graph, they can move over the arcs, and each agent has its own destination node. It is not possible for two agents to be at the same node at the same time. The usual setting is that each arc has length one so at any time step, each agent either stays in the node, where it is, or moves to one of its neighboring nodes. This paper suggests to model the MAPF problem using scheduling techniques, namely, nodes and arcs are seen as resources. The concept of optional activities is used to model which nodes and arcs an agent will visit. We first describe a model, where each agent can visit each node at most once. Then, we extend the model to allow agents re-visiting the *** major motivation for the scheduling model of MAPF is its capability to naturally include other constraints. We will study particularly the problems, where the capacity of arcs can be greater than one (more agents can use the same arc at the same time), and the lengths of arcs can be greater than one (moving between different pairs of nodes takes different times). These extensions make the model closer to reality than the original MAPF formulation. We compare the efficiency of models experimentally.
constraint programming is well suited for the computational modeling of music theories and composition: its declarative and modular approach shares similarities with the way music theory is traditionally expressed, na...
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constraint programming is well suited for the computational modeling of music theories and composition: its declarative and modular approach shares similarities with the way music theory is traditionally expressed, namely by a set of rules which describe the intended result. Various music theory disciplines have been modeled, including counterpoint, harmony, rhythm, form, and instrumentation. Because modeling music theories "from scratch" is a complex task, generic music constraint programming systems have been proposed that predefine the required building blocks for modeling a range of music theories. After introducing the field and its problems in general, this survey compares these generic systems according to a number of criteria such as the range of music theories these systems support.
We investigate constraint programming and Planning Domain Definition Language-based technologies for planning and scheduling multiple robots in a retirement home environment to assist elderly residents. Our robotics p...
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ISBN:
(纸本)9780999241103
We investigate constraint programming and Planning Domain Definition Language-based technologies for planning and scheduling multiple robots in a retirement home environment to assist elderly residents. Our robotics problem and investigation into proposed solution approaches provide a real world application of planning and scheduling, while highlighting the different modeling assumptions required to solve such a problem. This information is valuable to the planning and scheduling community as it provides insight into potential application avenues, in particular for robotics problems. Based on empirical results, we conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application.
constraint Logic programming (CLP) is a language scheme for combining two declarative paradigms: constraint solving and logic programming. Concurrent constraint programming (CCP) is a declarative model for concurrency...
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