The maximum number of clues in an n × n American-style crossword puzzle grid is explored. Grid constructions provided for all n are proved to be maximal for all even n. By using mixed integer linear programming, ...
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Power grid resilience has been a major concern as extreme weather events become more frequent recently. Particularly, wind power generation has constituted a significant body of generation portfolio worldwide. Under s...
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Power grid resilience has been a major concern as extreme weather events become more frequent recently. Particularly, wind power generation has constituted a significant body of generation portfolio worldwide. Under stormy weather conditions, wind power generation would be curtailed due to the over-speed protection of wind turbines. Subsequently, the sharply reduced generation resources would significantly endanger the reliability of power supply. In this study, the authors propose to apply the risk-limiting methodology to improve the efficient utilisation of wind power prior to reluctant wind curtailment on the advent of wind stormy events. The proposed method integrates three programs, namely, day-ahead unit commitment (UC), hourly-ahead UC, and real-time load shedding into one single model along with risk-limiting constraints. The first program provides the baseline of hourly dispatch;whereas, the latter two programs serve as recourse means while wind stormy event unfolds. The proposed model is cast into a multi-stage mixed-integer linear programming model and solved by commercial solvers. Illustrative examples demonstrate that the proposed method can reduce the total dispatch cost over the time-horizon including the wind stormy event, by postponing the timing of proactively starting quick-start generating units and shedding loads, in contrast to conventional two-stage stochastic dispatch methods.
Scientific conferences have become an essential part of academic research and require significant investments (e.g. time and money) from their participants. It falls upon the organizers to develop a schedule that allo...
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Scientific conferences have become an essential part of academic research and require significant investments (e.g. time and money) from their participants. It falls upon the organizers to develop a schedule that allows the participants to attend the talks of their interest. We present a combined approach of assigning talks to rooms and time slots, grouping talks into sessions, and deciding on an optimal itinerary for each participant. Our goal is to maximize attendance, taking into account the common practice of session hopping. On a secondary level, we accommodate presenters' availabilities. We use a hierarchical optimization approach, sequentially solving integer programming models, which has been applied to construct the schedule of the MathSport (2013), MAPSP (2015 and 2017) and ORBEL (2017) conferences. (C) 2017 Elsevier Ltd. All rights reserved.
This article proposes to simultaneously plan inbound and outbound truck arrivals and departures in a cross-docking platform, as well as the internal pallet handling. The objective is to minimize both the total number ...
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This article proposes to simultaneously plan inbound and outbound truck arrivals and departures in a cross-docking platform, as well as the internal pallet handling. The objective is to minimize both the total number of pallets put in storage and the dissatisfaction of the transportation providers, by creating a truck schedule as close as possible to the wished schedule they communicate in advance. The problem is modeled with an integer program tested on generated instances to assess its performance, especially regarding the computation time. The problem is proven to be np-hard in the strong sense. Since the execution takes too long to be used on a daily basis by platform managers, three heuristics are also proposed and tested. Two are based on integer programs solved sequentially, the third one is a tabu search in which the storage part of the objective function is evaluated by a maximum flow model in a graph. Numerical experiments show in which conditions each heuristic performs best, which can help choosing a solution method when confronted to a real-life problem.
Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a...
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Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a new two-stage methodology is proposed to supply critical loads based on cost-effective improvement. In the first stage, the interruption cost is proposed as the load priority and the ON/OFF status of switches are considered as the binary decision variables. Therefore, IIO is considered as a mixed integer linear programming (MILP) problem to minimise the interruption cost. At the second stage, the power flow calculation is performed on the initial islands for the real-time operation. The proposed method can be utilised for both long- and short-term studies. In the long-term study, the inherent uncertainty of ADN is considered in MILP by using a Monte-Carlo simulation. This concept is used for clustering ADN into self-sufficient microgrids. Moreover, by taking a snapshot of the ADN status and performing operational feasibility, the proposed method can be considered as a real-time power regulation method. The proposed methodology is implemented on the IEEE 33-bus distribution network, and the results are discussed in detail.
Effective unsupervised query-focused extractive summarization systems use query-specific features along with short-range language models (LMs) in sentence ranking and selection summarization subtasks. We hypothesize t...
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ISBN:
(纸本)9783319769417;9783319769400
Effective unsupervised query-focused extractive summarization systems use query-specific features along with short-range language models (LMs) in sentence ranking and selection summarization subtasks. We hypothesize that applying long-span n-gram-based and neural LMs that better capture larger context can help improve these subtasks. Hence, we outline the first attempt to apply long-span models to a query-focused summarization task in an unsupervised setting. We also propose the Across Sentence Boundary LSTM-based LMs, ASB LSTM and biASB LSTM, that is geared towards the query-focused summarization subtasks. Intrinsic and extrinsic experiments on a real word corpus with 100 Wikipedia event descriptions as queries show that using the long-span models applied in an integer linear programming (ILP) formulation of MMR criterion are the most effective against several state-of-the-art baseline methods from the literature.
This paper considers a tactical block scheduling problem at a major Norwegian hospital. Here, specific patient groups are reserved time blocks for scanning at a heterogeneous set of Magnetic Resonance Imaging (MRI) la...
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This paper considers a tactical block scheduling problem at a major Norwegian hospital. Here, specific patient groups are reserved time blocks for scanning at a heterogeneous set of Magnetic Resonance Imaging (MRI) labs. The time blocks consist of several time slots, and one or more patients from the same group are scanned in a block. A total weekly number of time slots for each specific patient group is given through demand forecast and negotiations, and several restrictions apply to the allocation of time blocks. Only part of the week is allocated to blocks for the specific patient groups. The rest is classified as open time. Thus, the MRI block scheduling problem consists of finding a cyclic weekly plan where one or more time blocks are to be allocated to each specific patient group, by deciding the day, start time and length, to minimise unfavourable patient group allocations, as well as allocations of open time. For the problem, we propose an integer programming model with an objective function that combines penalties for allocating time blocks to patient groups at unfavourable time slots and labs, and rewards for advantageous positioning of open time slots. The aim of the optimisation model is to facilitate the coordination of the MRI resources between the hospital departments, that are responsible for the specific patient groups, to achieve a fair distribution of time slots to the specific patient groups and open time blocks. The computational study is based on the real problem as well as artificially generated instances. Real-sized instances for our case hospital can be solved in short time. We illustrate how the model can be used to produce Pareto optimal solutions, and how these solutions can provide the decision makers with managerial insight. (C) 2017 Elsevier Ltd. All rights reserved.
In this letter, we propose a peak-to-average power ratio (PAPR) efficient non-coherent orthogonal frequency division multiplexing with index modulation (OFDM-IM). It is shown that the non-coherent OFDM-IM design, whic...
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Suppose a target is hidden in one of the vertices of an edge-weighted graph according to a known probability distribution. The expanding search problem asks for a search sequence of the vertices so as to minimize the ...
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The block relocation problem (BRP) is a fundamental operational issue in modern warehouse and yard management, which, however, is very challenging to solve. In this paper, to advance our understanding on this problem ...
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