76 research outputs found

    Single machine scheduling with job-dependent machine deterioration

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    We consider the single machine scheduling problem with job-dependent machine deterioration. In the problem, we are given a single machine with an initial non-negative maintenance level, and a set of jobs each with a non-preemptive processing time and a machine deterioration. Such a machine deterioration quantifies the decrement in the machine maintenance level after processing the job. To avoid machine breakdown, one should guarantee a non-negative maintenance level at any time point; and whenever necessary, a maintenance activity must be allocated for restoring the machine maintenance level. The goal of the problem is to schedule the jobs and the maintenance activities such that the total completion time of jobs is minimized. There are two variants of maintenance activities: in the partial maintenance case each activity can be allocated to increase the machine maintenance level to any level not exceeding the maximum; in the full maintenance case every activity must be allocated to increase the machine maintenance level to the maximum. In a recent work, the problem in the full maintenance case has been proven NP-hard; several special cases of the problem in the partial maintenance case were shown solvable in polynomial time, but the complexity of the general problem is left open. In this paper we first prove that the problem in the partial maintenance case is NP-hard, thus settling the open problem; we then design a 22-approximation algorithm.Comment: 15 page

    Isomorphism and Similarity for 2-Generation Pedigrees

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    We consider the emerging problem of comparing the similarity between (unlabeled) pedigrees. More specifically, we focus on the simplest pedigrees, namely, the 2-generation pedigrees. We show that the isomorphism testing for two 2-generation pedigrees is GI-hard. If the 2-generation pedigrees are monogamous (i.e., each individual at level-1 can mate with exactly one partner) then the isomorphism testing problem can be solved in polynomial time. We then consider the problem by relaxing it into an NP-complete decomposition problem which can be formulated as the Minimum Common Integer Pair Partition (MCIPP) problem, which we show to be FPT by exploiting a property of the optimal solution. While there is still some difficulty to overcome, this lays down a solid foundation for this research

    On the Approximability of the Exemplar Adjacency Number Problem for Genomes with Gene Repetitions

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    In this paper, we apply a measure, exemplar adjacency number, which complements and extends the well-studied breakpoint distance between two permutations, to measure the similarity between two genomes (or in general, between any two sequences drawn from the same alphabet). For two genomes and drawn from the same set of n gene families and containing gene repetitions, we consider the corresponding Exemplar Adjacency Number problem (EAN), in which we delete duplicated genes from and such that the resultant exemplar genomes (permutations) G and H have the maximum adjacency number. We obtain the following results. First, we prove that the one-sided 2-repetitive EAN problem, i.e., when one of and is given exemplar and each gene occurs in the other genome at most twice, can be linearly reduced from the Maximum Independent Set problem. This implies that EAN does not admit any -approximation algorithm, for any , unless P = NP. This hardness result also implies that EAN, parameterized by the optimal solution value, is W[1]-hard. Secondly, we show that the two-sided 2-repetitive EAN problem has an -approximation algorithm, which is tight up to a constant factor

    The Parallel Flow Shop Problem

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    This talk was given to the Shandong University School of Computer Science and Technology

    Approximability Smoothed Analysis

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    This talk was given to the Georgia Southern University Department of Computer Science

    A PTAS for the Multiple Parallel Identical Multi-stage Flow-shops to Minimize the Makespan

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    Scheduling is the process of arranging, controlling and optimizing work and workloads in a production process or manufacturing process. It has wide applications in manufacturing and engineering, as it can have a major impact on the productivity of a process. We will focus on one specific scheduling problem --- parallel k-stage flow-shops problem and propose an efficient algorithm. This theoretical result has been submitted to an international conference and is under the peer review. The abstract of this paper is as follows. In the parallel k-stage flow-shops problem, we are given m identical k-stage flow-shops and a set of jobs. Each job can be processed by any one of the flow-shops but switching between flow-shops is not allowed. The objective is to minimize the makespan, which is the finishing time of the last job. This problem generalizes the classical parallel identical machine scheduling (where k = 1) and the classical flow-shop scheduling (where m = 1) problems, and thus it is NP-hard. We present a polynomial-time approximation scheme for the problem, when m and k are fixed constants. The key technique is non-trivial and interesting, to enumerate over schedules for big jobs, solve a linear programming for small jobs, and add the fractional small jobs at the end

    Machine learning for spatiotemporal big data in air pollution

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    An accurate understanding of air pollutants in a continuous space-time domain is critical for meaningful assessment of the quantitative relationship between the adverse health effects and the concentrations of air pollutants. Traditional interpolation methods, including various statistic and nonstatistic regression models, typically involve restrictive assumptions regarding independence of observations and distributions of outcomes. Moreover, a set of relationships among variables need to be defined strictly in advance. Machine learning opens a new door to understand the air pollution data based on the exposing data-driven relationships and predicting outcomes without empirical models. In this chapter, the state-of-the-art machine learning methods will be introduced to unlock the full potential of the air pollutant data, that is, to estimate the PM2.5 concentration more accurately in the spatiotemporal domain. The methods can be extended to the other air pollutants
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