Number Of Time Windows Research Articles

Appointment scheduling for logistics parks under truck heterogeneity and order fulfillment delay

ABSTRACT Appointment scheduling is critical for logistic parks of manufacturing industry firms to manage inventory turnover and schedule service objects. This work studies the appointment scheduling problem of a logistic park considering truck heterogeneity and order fulfillment delay, where the number of time windows in a day, the number of each type of appointment slot, and the service schedule in each time window are decided. The objective is to minimise the expected total cost of the truck waiting time, inventory, and overtime. We introduce a weighted shortest processing time first scheduling rule to determine the service sequence in each time window and prove that it generates optimal scheduling solutions. Based on our proposed scheduling rule, we develop a recursive calculation to formulate the cost function, which provides a tractable approach to solving combinatorially complex problems. The proposed model is solved by a dual-selection based genetic algorithm (DSGA). The performance of the DSGA is validated by comparing it to a genetic algorithm using a single tournament selection operation. Our experimental results show that the DSGA enlarges the size of subgroups with better fitness and therefore performs satisfactorily.

Community evolution prediction based on a self-adaptive timeframe in social networks

The prediction of community evolution events in dynamic social networks is of great importance for network security alerts. Currently, most of the common prediction models use a fixed timeframe division strategy to divide the dynamic social network, such as the disjoint timeframe division strategy and overlapping timeframe division strategy. However, these frameworks cannot change once they are selected, and are not suitable for the prediction of network evolution with strong variability in real-world applications. In this paper, a new community evolution model is developed from the perspective of the universality of the timeframe, and a new optimized timeframe partitioning algorithm is proposed. Compared with the traditional fixed timeframe partitioning algorithm, this method adaptively adjusts the size and number of time windows according to the information fluctuations of the specific network at an acceptable extra computational cost. Based on the analysis of several real-world networks, we found that the proposed self-adaptive timeframe partitioning algorithm improved the quality of community tracking of the network and ensured the accuracy of prediction events.

Decomposition of the vehicle routing problem with time windows on the time dimension

The Vehicle Routing Problem with Time Windows (VRPTW) asks for the optimal set of routes to be performed by a fleet of vehicles to serve a set of customers within their assigned time windows. In this work, we propose a matheuristic for the VRPTW which utilizes the sub-problem constituted by optimizing only a selected time window of the VRPTW whereas the tours outside of this time window are regarded as fixed. We call this problem the Single Time Window Vehicle Routing Problem (STWVRP). For applying the STWVRP, we must assume that several customers are assigned to the same time window, i.e., the number of time windows is much smaller than the number of customers. An exact problem description of the STWVRP is given in the form of a Mixed-Integer Linear Programming formulation. We apply this exact formulation within a matheuristic for the VRPTW. The paper concludes with extensive computational experiments.

Predictive drug dosage control through a Fokker–Planck observer

During the years, various techniques have been presented for optimal control of tumor growth for which its stochastic behavior has rarely been considered. This article uses the well-known Gompertz stochastic model, for describing tumor growth, and by receding horizon model predictive control (RH-MPC) scheme computes the drug dosage for minimizing the tumor-cell population. To do this, a predictive control problem is defined based on the difference between the probability density function of tumor-cell population and the desired probability density function. In the model, both the drug dosage limitation and the Fokker–Planck equation have been considered as constraints. By solving this problem, the drug dosage which is considered as an external input to tumor model, is computed. In this article, the Fokker–Planck equation is used (1) as a nonlinear observer of probability density function of tumor-cell population and (2) a mapping vehicle from stochastic to deterministic domain. The Fokker–Planck equation is written for the Gompertz stochastic model of tumor growth. The equation is then solved through the path integral method. In this way, the probability density function of tumor-cell population, which contains the entire stochastic characteristics of the tumor growth, is obtained for any instance of time. The simulation results have also been presented for the evaluation of the suggested approach. The results show that the tumor-cell population can be controlled within a number of time windows (15 time windows in our case study) if an appropriate desired PDF is selected.

Minimizing the maximal weighted lateness of delivering orders between two railroad stations

We consider the planning problem for freight transportation between two railroad stations. We are required to fulfill orders (transport cars by trains) that arrive at arbitrary time moments and have different value (weight). The speed of trains moving between stations may be different. We consider problem settings with both fixed and undefined departure times for the trains. For the problem with fixed train departure times we propose an algorithm for minimizing the weighted lateness of orders with time complexity O(qn2 log n) operations, where q is the number of trains and n is the number of orders. For the problem with undefined train departure and arrival times we construct a Pareto optimal set of schedules optimal with respect to criteria wLmax and Cmax in O(n2 max{n log n, q log v}) operations, where v is the number of time windows during which the trains can depart. The proposed algorithm allows to minimize both weighted lateness wLmax and total time of fulfilling freight delivery orders Cmax.

CPB: a classification-based approach for burst time prediction in cascades

Studying the bursty nature of cascades in social media is practically important in many real applications such as product sales prediction, disaster relief, and stock market prediction. Although both the cascade size prediction and the burst patterns of the cascades have been extensively studied, how to predict when a burst will come remains an open problem. It is challenging for traditional time-series-based models such as regression models to address this task directly. Firstly, times-series-based prediction models focus on predicting the future values based on previously observed ones. It is hard to apply them to predict the time of a bursts with the "quick rise-and-fall" pattern. Secondly, besides the cascade popularity, a lot of other side information like user profile and social relation are available in social media. Although the potential utility of such information can be high, it is also hard for time-series-based models to capture and integrate these rich information with diverse formats seamlessly. This paper proposes a classification-based approach for burst time prediction by exploiting rich knowledge in information diffusion. Particularly, we first propose a time-window-based transformation to predict in which time window the burst will appear. By dividing the time spans of all the cascades into the same number of time windows K, the cascades with diverse time spans can thus be handled uniformly. To exploit the rich and heterogenous information in social media, we next propose a scale-independent feature extraction framework to model the heterogenous knowledge in a scale-independent manner. Systematical evaluations are conducted on the Sina Weibo reposting dataset and MemeTracker dataset. Besides the superior performance of the proposed approach, we also observe that: (1) surprisingly, social/structure knowledge is more indicative of the bursts than the cascade popularity information, especially for the bursts occurring in a farther future. (2) Larger cascades are harder to predict as the spreading process of the cascades with higher popularity is usually more diverse and fluctuant. (3) The proposed approach is robust in the sense that the result is not much sensitive to the popularity of the training cascades.

Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time. The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding. All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.

Adjoint time domain method for fluorescent imaging in turbid media

Application of adjoint time domain methods to the inverse problem in 3D fluorescence imaging is a novel approach. We demonstrate the feasibility of this approach experimentally on the basis of a time gating technique completely in the time domain by using a small number of time windows. The evolution of the fluorescence energy density function inside a highly scattering cylinder was reconstructed together with optical parameters. Reconstructed energy density was used in localizing two fluorescent tubes. Relatively accurate reconstruction demonstrates the effectiveness and the potential of the proposed technique.

Code for time-resolved x-ray pulse analysis at high count rates for short tokamak discharges

The measurement of soft x-ray spectrum in fusion plasmas allows for information on the nature of electron distribution, identification of impurity contents, calculation of electron temperature, estimation of Zeff, etc. All these measurements can be performed with a semiconductor detector. By amplifying the signal and using it as input to a multichannel analyzer (MCA), the spectrum is obtained. This kind of measurement does not have the desirable property of temporal resolution unless several detection chains are used or a special hardware is developed. In this paper, a method is presented to reach that type of resolution. This method requires only one measurement chain and the substitution of the MCA for a digitizer and mass storage. The digitized signal is stored in a computer, which will be the input for the several codes developed with the purpose of performing all the tasks of an MCA. These codes allow any number of time windows, of any length, along the digitized signal. Furthermore, in this way, it is possible to process one signal several times with different input parameters. Results in the TJ-I Tokamak (R0=0.3 m; a=0.1 m; BT<1.9 T; IP<70 kA) are shown.