We study planning problems where the transition function is described by a learned binarized neural network (BNN). Theoretically, we show that feasible planning with a learned BNN model is NP-complete, and present two...
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Minoan Linear A is still an undeciphered script mainly used for administrative purposes on Bronze Age Crete. One of its most enigmatic features is the precise mathematical values of its system of numerical fractions. ...
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Minoan Linear A is still an undeciphered script mainly used for administrative purposes on Bronze Age Crete. One of its most enigmatic features is the precise mathematical values of its system of numerical fractions. The aim of this article is to address this issue through a multi-stranded methodology that comprises palaeographical examination and statistical, computational, and typological approaches. Taking on from previous analyses, which suggested hypothetical values for some fractions, we extended our probe into assessing values for some problematic ones. The results achieved, based, on the one hand, on a close palaeographical analysis and, on the other, on computational, statistical and typological strategies, show a remarkable convergence and point towards a systematic assignment of mathematical values for the Linear A fraction signs. This contribution sets the agenda for a combinatorial, novel, and unbiased approach that may help advance our comprehension of some standing issues related to ancient undeciphered writing systems.
This paper proposes efficient heuristic approaches for the Hybrid Flexible Flowshop with Transportation Times (HFFTT), an extension of both the Hybrid Flowshop (HFP) and Hybrid Flexible Flowshop (HFF) problems. Two cl...
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This paper proposes efficient heuristic approaches for the Hybrid Flexible Flowshop with Transportation Times (HFFTT), an extension of both the Hybrid Flowshop (HFP) and Hybrid Flexible Flowshop (HFF) problems. Two classes of heuristics are introduced: constraint programming (CP)-based heuristics and decomposition heuristics. While the CP-based heuristics can be applied to any instance of the HFFTT, the decomposition heuristics are specifically designed for “rectangular” instances, where the number of machines is the same at each stage. Both approaches are compared against two iterated greedy algorithms adapted from the state-of-the-art, one of which is tailored exclusively for rectangular instances. The results show that the CP-based heuristics achieve the best performance for non-rectangular instances, while the decomposition heuristics strongly dominate all other approaches for rectangular instances, as soon as the size of the instances considered is large enough. We show that most of the results obtained can be generalized to the case without transportation times, where the HFFTT problem reduces to the HFF.
This paper makes a comparative study on two kinds of common optimization models and their solution techniques, vehicle routing problem and job shop scheduling problem, and explores the mutual transformation form of th...
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The relocation of containers is essential at port terminals to increase operational efficiency during container retrieval from the yard. When a container must be retrieved, any container placed on top of it must be mo...
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In this paper we present work towards automating two process steps supporting the optimization of the runnable-to-task mapping in automotive multi-core control units. We describe these steps in close relation to the A...
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ISBN:
(纸本)9781728151250
In this paper we present work towards automating two process steps supporting the optimization of the runnable-to-task mapping in automotive multi-core control units. We describe these steps in close relation to the AUTOSAR methodology to facilitate the integration with existing design processes. The first step is the automated generation of an initial configuration that balances the core utilization using constraint programming. The second step is the optimization of an existing configuration based on dynamic system behavior using an evolutionary algorithm. An abstract intermediate representation provides interoperability with existing AUTOSAR tools. We use a small case study to evaluate the feasibility of our approach.
In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. Tests must be allocated to suitable prototypes and ordered to satisfy t...
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In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. Tests must be allocated to suitable prototypes and ordered to satisfy temporal constraints and various kinds of test dependencies. The manufacturer aims to minimize the number of prototypes required. We present improvements of constraint programming (CP) and hybrid approaches to effectively solve random instances from an existing benchmark. CP mostly achieves better solutions than the previous heuristic technique and genetic algorithm. We also provide customized search schemes to enhance the performance of general search algorithms. The hybrid approach applies mixed integer linear programming (MILP) to solve the planning part and CP to find the complete schedule. We consider several logical principles such that the MILP model can accurately estimate the prototype demand, while its size particularly for large instances does not exceed memory capacity. Moreover, the robustness is alleviated when we allow CP to partially change the allocation obtained from the MILP model. The hybrid method can contribute to optimal solutions in some instances.
作者：
Hà, Minh HoàngTa, Dinh QuyNguyen, Trung ThanhORLab
Faculty of Computer Science Phenikaa University Hanoi12116 Viet Nam ORLab
Faculty of Information Technology VNU University of Engineering and Technology Hanoi Viet Nam
Machine scheduling problems involving conflict jobs can be seen as a constrained version of the classical scheduling problem, in which some jobs are conflict in the sense that they cannot be proceeded simultaneously o...
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This research details the creation of a large-scale optimization approach for solving an application of a multi-period bilevel network interdiction problem (NIP). In this class of multi-period NIP, interdiction activi...
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This research details the creation of a large-scale optimization approach for solving an application of a multi-period bilevel network interdiction problem (NIP). In this class of multi-period NIP, interdiction activities must be scheduled to minimize the cumulative maximum flow over a finite time horizon. A logic-based decomposition (LBD) approach is proposed that utilizes constraint programming to exploit the scheduling nature of this multi-period NIP. Computational results-comparing solutions obtained using the proposed approach versus traditional mixed-integer programming approach-suggest that the LBD approach is more efficient in finding solutions for medium to large problem instances. (C) 2018 Elsevier Ltd. All rights reserved.
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