constraint answer set programming (CASP) is a family of hybrid approaches integrating answer set programming (ASP) and constraint programming (CP). These hybrid approaches have already proven to be successful in vario...
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Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we ...
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Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that clearly describes the problem and a constraint programming model capable of effectively solving it. Due to the usage of interval variables, global constraints, a powerful constraint programming solver, and a heuristic we have identified, which we call the "due times rule", the constraint programming model can reach excellent solutions. Furthermore, we employ a hybrid approach that exploits three local search improvement procedures in a schema where the constraint programming part of the solver plays a central role. These improvement procedures exhaustively enumerate portions of the search space by exchanging consecutive jobs with a single job of the same duration, moving cost-incurring jobs to earlier times in a consecutive sequence of jobs or even exploiting periods where capacity is not fully utilized to rearrange jobs. On the other hand, subproblems are given to the exact constraint programming solver, allowing freedom of movement only to certain parts of the schedule, either in vertical ribbons of the time axis or in groups of consecutive sequences of jobs. Experiments on publicly available data show that our approach is highly competitive and achieves the new best results in many problem instances.
As an important component of flow type production systems, assembly lines are widely used from automotive, appliance to apparel industry. In this research, the aim is finding the optimum task assignment that minimizes...
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As an important component of flow type production systems, assembly lines are widely used from automotive, appliance to apparel industry. In this research, the aim is finding the optimum task assignment that minimizes the cycle time under the assumption that station quantities are constant which are described as Type-2 assembly lines. Stochastic assembly lines are considered in which task times are distributed according to a statistical distribution. A new algorithm which gives optimum solution using constraint programming and Queuing Network is proposed. In this algorithm, the possible combinations are determined by constraint programming, and then, the performance measures are evaluated by Queuing Network. The method is tested with several numerical experiments from literature and the applicability is confirmed.
This paper studies a simultaneous scheduling of production and material transfer in a flexible job shop environment. The simultaneous scheduling approach has been recently adopted by a robotic mobile fulfillment syste...
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This paper studies a simultaneous scheduling of production and material transfer in a flexible job shop environment. The simultaneous scheduling approach has been recently adopted by a robotic mobile fulfillment system, wherein transbots pick up jobs and deliver to pick-stations for processing, which requires a simultaneous scheduling of jobs, transbots, and stations. Two different constraint programming formulations are proposed for the first time for a flexible job shop scheduling problem with transbots, significantly outperforming all other benchmark approaches in the literature and proving optimality of the well-known benchmark instances.
The importance of computer-aided process planning (CAPP) for assembly is widely recognized, as it holds the promise of efficient and automated construction of solutions for a complex, geometrically, technologically, a...
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The importance of computer-aided process planning (CAPP) for assembly is widely recognized, as it holds the promise of efficient and automated construction of solutions for a complex, geometrically, technologically, and economically constrained planning problem. This complexity led to the introduction of decomposition approaches, separating the macro-level planning problem that oversees the complete assembly process from the various micro-level problems that look into the details of individual assembly operations. The paper introduces a constraint model for solving the macro-level assembly planning problem based on a generic feature-based representation of the product and the assembly operations involved. Special attention is given to capturing the feedback from micro-level planners expressed in the form of feasibility cuts, and hence, to the integration of the approach into a complete CAPP workflow. Results on three case studies from different industries are also presented to illustrate the practical applicability of the approach.
We study a variant of the multiprocessor job scheduling problem, where jobs are processed by several identical machines. The machines are ordered in a sequence, and each job is processed by several consecutive machine...
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We study a variant of the multiprocessor job scheduling problem, where jobs are processed by several identical machines. The machines are ordered in a sequence, and each job is processed by several consecutive machines simultaneously. The jobs are characterized by their processing time, the number of required consecutive machines, and their ready time. The objective function is to minimize the sum of general functions defined over the completion time of each job. This study is motivated by a real problem in the semiconductor industry. We present a time-indexed integer programming and a constraint programming formulations for the problem and demonstrate their applicability through an extensive numerical study and an industrial case study. (C) 2020 Elsevier B.V. All rights reserved.
Background Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs)...
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Background Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. Results To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. Conclusion PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
This paper describes a unified global constraint to model scheduling problems with unary resources, i.e., each resource can process only a single activity at a time. In addition, the constraint enforces sequence-depen...
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This paper describes a unified global constraint to model scheduling problems with unary resources, i.e., each resource can process only a single activity at a time. In addition, the constraint enforces sequence-dependent transition times between activities. It often happens that activities are grouped into families with zero transition times within a family. Moreover, some of the activities might be optional from the resource viewpoint (typically in the case of alternative resources). The global constraint unifies reasoning with both optional activities and families of activities. The scalable filtering algorithms we discuss keep a low time complexity of O(n center dot log(n)center dot log(f)), where n is the number of tasks on the resource and f is the number of families. This results from the fact that we extend the Theta-tree data structure used for the Unary Resource constraint without transition times. Our experiments demonstrate that our global constraint strengthens the pruning of domains as compared with existing approaches, leading to important speedups. Moreover, our low time complexity allows maintaining a small overhead, even for large instances. These conclusions are particularly true when optional activities are present in the problem.
This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and ...
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This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and due to limited tool magazine capacity, and because it is not possible to load all available tools on the machine, tools must be switched. The ultimate goal, in this framework, is to minimize the total number of tool switching instants. We provide a mathematical programming model and two constraint programming models for the problem. Because the problem is proven to be NP-hard, we develop two heuristic approaches, and compare their performance with methods described in the literature. Our analysis indicates that our constraint programming models perform relatively well in solution quality and execution time in small-sized problem instances. The performance of our greedy approach shows potential, reaching the optimal solution in 82.5% of instances. We also statistically demonstrate that the search algorithm enhances the quality of the solution obtained by the greedy heuristic, particularly in large sets. Hence, the solution approach, i.e., the greedy heuristic and the search algorithm proposed in this study is able to quickly reach near-optimal solutions, showing that the method is appropriate for manufacturing settings requiring sudden adjustments.
Neutral landscape models have many applications in ecology, such as supporting spatially explicit simulations, developing and evaluating landscape indices. However, current approaches provide few options to produce la...
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Neutral landscape models have many applications in ecology, such as supporting spatially explicit simulations, developing and evaluating landscape indices. However, current approaches provide few options to produce large landscapes with controlled composition and fragmentation indices. We introduce flsgen (Fragmented Landscape Generator), a new neutral landscape generator that addresses this limitation by providing a high level of control over 14 landscape indices. The main novelty of flsgen is the decomposition of landscape generation into two steps: the solving of a constraint satisfaction problem and the generation of a landscape raster with a stochastic algorithm. The latter relies on a continuous environmental gradient that influences the landscape's spatial configuration. flsgen can generate fine-grained artificial landscapes in small amounts of time, which makes it suited to produce large landscape series systematically. We demonstrate the features of flsgen through three illustrative use cases. flsgen is a practical and efficient tool that expands the current possibilities of neutral landscape models and widens their potential applications. To facilitate its uptake, flsgen is available as free and open-source software through a Java API, a command-line interface or an R package.
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