Efficient identifications of structural similarities for graphs

Measuring and detecting graph similarities is an important topic with numerous applications. Early algorithms often incur quadratic time or higher, making them unsuitable for graphs of very large scales. Motivated by the cooling process of an object in a thermodynamic system, we devise a new method for measuring graph similarities that can be carried out in linear time. Our algorithm, called Random Walker Termination (RWT), employs a large number of random walkers to capture the structure of a given graph using termination rates in a time sequence. To verify the effectiveness of the RWT algorithm, we use three major graph models, namely, the Erd?s-Rényi random graphs, the Watts-Strogatz small-world graphs, and the Barabási-Albert preferential-attachment graphs, to generate graphs of different sizes. We show that the RWT algorithm performs well for graphs generated by these models. Our experiment results agree with the actual similarities of generated graphs. Using self-similarity tests, we show that RWT is sufficiently stable to generate consistent results. We use the graph edge rerouting test and the cross model test to demonstrate that RWT can effectively identify structural similarities between graphs.     

Managing Quality in a Heterogeneous Contract Manufacturing Environment

The outsourcing of production is a prominent strategy across industries. While the strategy can have many benefits, the popular press reports numerous examples of quality issues originating with contract manufacturers (CMs). Observing these quality issues, multiple scholars call for the quality management (QM) literature to be extended to explicitly address the challenges of managing quality in an inter‐organizational context. Additionally, QM researchers recognize the need to consider contextual contingencies for the effectiveness of specific QM practices. Responding to these calls, we focus on the potential contingent factor of CM heterogeneity (i.e. the degree of product and process diversity at the CM plant). We first test the direct relationship between CM manufacturing heterogeneity and CM quality conformance performance, as reported by the CM's customers, brand‐owning firms. Next, we evaluate the effectiveness of multiple practices that these brand‐owning firms can employ to mitigate the anticipated negative effect of heterogeneity on their CM's conformance quality. We utilize paired dyadic data on 106 contract manufacturing relationships in the food, drug, and medical device industries to test our hypothesized model. The results of our analysis reveal a negative association between heterogeneity at CM facilities and their conformance quality performance. Our results also identify cooperative relationships, contractual coordination provisions, and formal performance assessment programs as practices that brand‐owning firms can employ to largely eliminate the negative impact of heterogeneity on CMs’ conformance quality performance.     

Approximations to Optimal k‐Unit Cycles for Single‐Gripper and Dual‐Gripper Robotic Cells

We consider the problem of scheduling operations in bufferless robotic cells that produce identical parts using either single‐gripper or dual‐gripper robots. The objective is to find a cyclic sequence of robot moves that minimizes the long‐run average time to produce a part or, equivalently, maximizes the throughput. Obtaining an efficient algorithm for an optimum k‐unit cyclic solution (k ≥ 1) has been a longstanding open problem. For both single‐gripper and dual‐gripper cells, the approximation algorithms in this paper provide the best‐known performance guarantees (obtainable in polynomial time) for an optimal cyclic solution. We provide two algorithms that have a running time linear in the number of machines: for single‐gripper cells (respectively, dual‐gripper cells), the performance guarantee is 9/7 (respectively, 3/2). The domain considered is free‐pickup cells with constant intermachine travel time. Our structural analysis is an important step toward resolving the complexity status of finding an optimal cyclic solution in either a single‐gripper or a dual‐gripper cell. We also identify optimal cyclic solutions for a variety of special cases. Our analysis provides production managers valuable insights into the schedules that maximize productivity for both single‐gripper and dual‐gripper cells for any combination of processing requirements and physical parameters.     

An empirical study of design and testing of hybrid evolutionary–neural approach for classification

We propose a hybrid evolutionary–neural approach for binary classification that incorporates a special training data over-fitting minimizing selection procedure for improving the prediction accuracy on holdout sample. Our approach integrates parallel global search capability of genetic algorithms (GAs) and local gradient-descent search of the back-propagation algorithm. Using a set of simulated and real life data sets, we illustrate that the proposed hybrid approach fares well, both in training and holdout samples, when compared to the traditional back-propagation artificial neural network (ANN) and a genetic algorithm-based artificial neural network (GA-ANN).     

On the number of local minima for the multidimensional assignment problem

The Multidimensional Assignment Problem (MAP) is an NP-hard combinatorial optimization problem occurring in many applications, such as data association, target tracking, and resource planning. As many solution approaches to this problem rely, at least partly, on local neighborhood search algorithms, the number of local minima affects solution difficulty for these algorithms. This paper investigates the expected number of local minima in randomly generated instances of the MAP. Lower and upper bounds are developed for the expected number of local minima, E[M], in an MAP with iid standard normal coefficients. In a special case of the MAP, a closed-form expression for E[M] is obtained when costs are iid continuous random variables. These results imply that the expected number of local minima is exponential in the number of dimensions of the MAP. Our numerical experiments indicate that larger numbers of local minima have a statistically significant negative effect on the quality of solutions produced by several heuristic algorithms that involve local neighborhood search.Partially supported by the NSF grant DMI-0457473.     

On variance amplification in a three-echelon supply chain with minimum mean square error forecasting

We analyse a three echelon supply chain model. First-order autoregressive end consumer demand is assumed. We obtain exact analytical expressions for bullwhip and net inventory variance at each echelon in the supply chain. All of the three supply chain participants employ the order-up-to policy with the minimum mean square error forecasting scheme. After demonstrating that the character of the stochastic ordering process observed at each level of the supply chain is mathematically tractable, we show that the upper stream participants have complete information of the market demand process. Then we quantify the bullwhip produced by the system, together with the amplification ratios of the variance of the net inventory levels. Our analysis reveals that the level of the supply chain has no impact upon the bullwhip effect, rather bullwhip is determined by the accumulated lead-time from the customer and the local replenishment lead-time. We also find that the conditional variance of the forecast error over the lead-time is identical to the variance of the net inventory levels and that the net inventory variance is dominated by the local replenishment lead-time.     

Probabilistic Nonadaptive and Two-Stage Group Testing with Relatively Small Pools and DNA Library Screening

We use a simple, but nonstandard, incidence relation to construct probabilistic nonadaptive group testing algorithms that identify many positives in a population with a zero probability of yielding a false positive. More importantly, we give two-stage modifications of our nonadaptive algorithms that dramatically reduce the expected number of sufficient pools. For our algorithms, we give a lower bound on the probability of identifying all the positives and we compute the expected number of positives identified. Our method gives control over the pool sizes. In DNA library screening algorithms, where relatively small pools are generally preferred, having control over the pool sizes is an important practical consideration.     

E-learning methodologies in practice: similarities and differences between North American countries and New Zealand

In this cross-national qualitative study, tertiary instructors from North America (Canada and the USA) and New Zealand were asked to share their perceptions regarding effective e-learning methodologies. The purpose of the research was to identify what similarities and differences in perceptions existed, and what implications, if any, such similarities and differences would have for the development of national and tertiary digital strategies for New Zealand. A comparison of the responses to four primary research questions indicated that while both similarities and differences in perception do exist, the similarities far outweigh the differences.     

Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem

In this work three genetic algorithms are presented for the permutation flowshop scheduling problem with total tardiness minimisation criterion. The algorithms include advanced techniques like path relinking, local search and a procedure to control the diversity of the population. We also include a speed up procedure in order to reduce the computational effort needed for the local search technique, which results in large CPU time savings. A complete calibration of the different parameters and operators of the proposed algorithms by means of a design of experiments approach is also given. We carry out a comparative evaluation with the best methods that can be found in the literature for the total tardiness objective, and with adaptations of other state-of-the-art methods originally proposed for other objectives, mainly makespan. All the methods have been implemented with and without the speed up procedure in order to test its effect. The results show that the proposed algorithms are very effective, outperforming the remaining methods of the comparison by a considerable margin.     

A Multi-Exchange Neighborhood for Minimum Makespan Parallel Machine Scheduling Problems

We propose new local search algorithms for minimum makespan parallel machine scheduling problems, which perform multiple exchanges of jobs among machines. Inspired by the work of Thompson and Orlin (1989) on cyclic transfer neighborhood structures, we model multiple exchanges of jobs as special disjoint cycles and paths in a suitably defined improvement graph, by extending definitions and properties introduced in the context of vehicle routing problems (Thompson and Psaraftis, 1993) and of the capacitated minimum spanning tree problem (Ahuja et al., 2001). Several algorithms for searching the neighborhood are suggested.We report the results of a wide computational experimentation, on different families of benchmark instances, performed for the case of identical machines. This problem has been selected as a case study to perform a comparison among the alternative algorithms, and to discover families of instances for which the proposed neighborhood may be promising in practice. Based on the results of the experiments, we can suggest which among the many possible variants of the proposed approaches may be more promising for developing local search algorithms based on multi-exchange moves for related problems. Also, on some families of instances, which are very hard to solve exactly, the most promising multi-exchange algorithms were observed to dominate, in solution quality and in computational time, competitive benchmark heuristics.     

Immigration,Growth, and Unemployment: Panel VAR Evidence from OECD Countries

This paper examines empirically the interaction between immigration and host country economic conditions. We employ panel vector autoregression (VAR) techniques for a large annual data set on 22 OECD countries over the period 1987–2009. The VAR approach addresses the endogeneity problem by allowing for endogenous interactions between the variables in the system. Our results provide evidence of migration contribution to host economic prosperity (positive impact on GDP per capita and negative impact on aggregate unemployment, native‐ and foreign‐born unemployment rates). We also find that migration is influenced by host economic conditions (migration responds positively to host GDP per capita and negatively to host total unemployment rate).     

Efficient Algorithms for Similarity Search

The problem of our interest takes as input a database of m sequences from an alphabet and an integer k. The goal is to report all the pairs of sequences that have a matching subsequence of length at least k. We employ two algorithms to solve this problem. The first algorithm is based on sorting and the second is based on generalized suffix trees. We provide experimental data comparing the performances of these algorithms. The generalized suffix tree based algorithm performs better than the sorting based algorithm.     

Learning and Open Source Software License Choice

Licensing is the defining characteristic of open source software (OSS) and often has tremendous impact on the success of OSS projects. However, OSS licenses are very different from those for proprietary software, and our understanding of the choice of OSS licenses is very limited. In this study, we explore this important decision from a learning perspective. We build collaboration networks and trace paths through which potential learning and knowledge flow across projects using a dataset derived from SourceForge. We identify that both experiential learning and vicarious learning have significant influence on OSS license choice. We provide reasons why experiential learning and vicarious learning affect decision‐making regarding OSS license choice, and explore important contingencies under which the two modes of learning are more effective. We find that leadership roles on prior projects and similarities between projects significantly moderate these two modes of learning, respectively. More importantly, we argue and empirically illustrate that experiential learning is more effective than vicarious learning in influencing OSS license choice. Our research sheds new light on our understanding of license choice for OSS projects and provides practical guidelines for future OSS development.     

Do makerspaces represent scalable production models of community-based redistributed manufacturing?

Abstract

This research explores the development of local community-based ‘makerspaces’ as potential scalable forms of redistributed manufacturing (RDM). Makerspaces are rapidly emerging in post-industrial economies and have been identified as a catalyst of local regeneration in urban areas. However, their role in local production systems is limited. There is a gap in the literature, with respect to the evolution of makerspaces and their productive contribution. The purpose of this paper therefore is to identify, classify and examine the different types of makerspaces. Our focus is on the implementation characteristics that enable industrial production activity to take place. First, we used Leximancer (to identify from the literature) three types of makerspace. Second, we then identify five RDM implementation characteristics. The characteristics were integrated together to form the RDM-makerspace implementation model. Third, case studies were purposively selected to test and advance this model. They were subsequently classified as a Type 1 (educational), Type 2 (design) or Type 3 (production) makerspace. Only one of the case studies was classified as a fully evolved Type 3 production space. The findings concur with the literature that makerspaces tend to be primarily Type 1 or Type 2. Finally, the contribution to local production theory is emphasized.     

Predicting above-median and below-median growth rates

Multiannual periods of consecutive above-median or below-median growth rates in operating performance, called runs, have a substantial influence on firm valuations. For estimating the probability of an above-median or below-median run and utilizing information efficiently, we employ a stepwise regression to automatically identify the parsimonious indicator-specific set of economically and empirically meaningful variables. Our novel approach uses logit models to distinguish firms that will persistently grow above or below the median over a period of up to 6 years. The predictive power for sales growth rates is highest to discriminate between above-median and below-median growth rates, while the future behaviour of operating income and net income growth rates can partially be explained for below-median growth rates.     

Intake fraction for multimedia pollutants: a tool for life cycle analysis and comparative risk assessment.

We employ the intake fraction (iF) as an effective tool for expressing the source-to-intake relationship for pollutant emissions in life cycle analysis (LCA) or comparative risk assessment. Intake fraction is the fraction of chemical mass emitted into the environment that eventually passes into a member of the population through inhalation, ingestion, or dermal exposure. To date, this concept has been primarily applied to pollutants whose primary route of exposure is inhalation. Here we extend the use of iF to multimedia pollutants with multiple exposure pathways. We use a level III multimedia model to calculate iF for TCDD and compare the result to one calculated from measured levels of dioxin toxic equivalents in the environment. We calculate iF for emissions to air and surface water for 308 chemicals. We correlate the primary exposure route with the magnitudes of the octanol-water partition coefficient, Kow, and of the air-water partitioning coefficient (dimensionless Henry constant), Kaw. This results in value ranges of Kow and Kaw where the chemical exposure route can be classified with limited input data requirements as primarily inhalation, primarily ingestion, or multipathway. For the inhalation and ingestion dominant pollutants, we also define empirical relationships based on chemical properties for quantifying the intake fraction. The empirical relationships facilitate rapid evaluation of many chemicals in terms of the intake. By defining a theoretical upper limit for iF in a multimedia environment we find that iF calculations provide insight into the multimedia model algorithms and help identify unusual patterns of exposure and questionable exposure model results.     

Two new robust genetic algorithms for the flowshop scheduling problem

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (_Cmax$C_{\max }$). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.     

Household‐Level Coastal Adaptation and Its Drivers: A Systematic Case Study Review

Evidence‐based information on household‐level adaptation is an important element of integrated management of vulnerable coastal regions. A growing number of empirical studies deal with household‐level adaptation at the coast in different regions. This article provides a systematic review of these studies. We analyze studies according to how households in different parts of the world are currently adapting, or how they are intending to adapt, and identify explanatory factors for adaptation behavior and intention. We find that households implement a broad range of adaptation measures and that adaptation behavior is explained by individual factors such as socioeconomic and cognitive variables, experience, and perceived responsibilities. Nonpersonal characteristics have also been used to explain adaptation behavior and intention but have not been extensively investigated. Few studies employ qualitative research methods and use inductive approaches as well as models stemming from behavioral economics. Our findings suggest that coastal risk management policies should communicate the efficacy of household‐level adaptation, in addition to information about flood risk, in order to encourage coastal households in their adaptation activities. In this context, we discuss the role of resources and responsibility of households for their adaptation behavior. We describe the lessons learnt and formulate a research agenda on household‐level adaptation to coastal flood risk. In practice, coastal risk management policies should further promote individually driven adaptation by integrating it in adaptation strategies and processes.     

Discovering classes in microarray data using island counts

We present a new biclustering algorithm to simultaneously discover tissue classes and identify a set of genes that well-characterize these classes from DNA microarray data sets. We employ a combinatorial optimization approach where the object is to simultaneously identify an interesting set of genes and a partition of the array samples that optimizes a certain score based on a novel color island statistic. While this optimization problem is NP-complete in general, we are effectively able to solve problems of interest to optimality using a branch-and-bound algorithm. We have tested the algorithm on a 30 sample Cutaneous T-cell Lymphoma data set; it was able to almost perfectly discriminate short-term survivors from long-term survivors and normal controls. Another useful feature of our method is that can easily handle missing expression data.     

On the complexity and approximation of non-unique probe selection using d-disjunct matrix

In this paper, we studied the MINimum-d-Disjunct Submatrix (MIN-d-DS), which can be used to select the minimum number of non-unique probes for viruses identification. We prove that MIN-d-DS is NP-hard for any fixed d. Using d-disjunct matrix, we present an O(log k)-approximation algorithm where k is an upper bound on the maximum number of targets hybridized to a probe. We also present a (1+(d+1)log n)-approximation algorithm to identify at most d targets in the presence of experimental errors. Our approximation algorithms also yield a linear time complexity for the decoding algorithms. The research of T. Znati was supported in part by National Science Foundation under grant CCF-0548895.