Describing the influence of set-up optimised sequencing on output lateness of workstations

Productivity and schedule reliability are two key objectives when designing, planning and controlling an enterprise’s internal supply chain. Productivity can be increased by implementing set-up optimised order sequencing, which reduces the time and costs required for set-ups. However, since planned end-of-order-processing dates are ignored with set-up optimised sequencing, it tends to cause increased lateness and thus poorer schedule reliability. Accordingly, enterprises must weigh the highest possible productivity against a high schedule reliability. In this paper, we develop a logistic model that can be used by enterprises to facilitate describing the impact of set-up optimised sequencing on a workstation’s lateness behaviour. This logistic model depicts the extent of the output lateness’ standard deviation as a function of the mean WIP, mean workstation output and the input lateness’ standard deviation when applying set-up optimised sequencing. It is thus possible to quantitatively describe the effects of set-up optimised sequencing on the logistic objective ‘schedule reliability’ and compare possible productivity gains.     

培养批判思维在英语课堂提问中的实现——基于小型语料库的研究

课堂提问是培养学生批判思维（CT）的重要手段之一.通过对英语教学设计和课堂记录的语料库和案例分析发现,高中英语教师的问题类型以“what”和“how”所占比重最多,表明教师对培养学生搜索理解“信息”和“程序”方面的思维能力的偏爱;同时,教师提问的顺序设计有优有劣.其问题主要在于对学生CT的难易度的把握和排序上;教师提问的时机与教学效果直接相关.     

A hybrid approach to sequencing jobs using heuristic rules and neural networks

A hybrid approach to solve job sequencing problems using heuristic rules and artificial neural networks is proposed. The problem is to find a job sequence for a single machine that minimizes the total weighted tardiness of the jobs. Two different cases are considered: (1) when there are no setups, and (2) when there are sequence-dependent setup times. So far, successful heuristic rules for these cases are: apparent tardiness cost (ATC) rule proposed by Vepsalainen and Morton for the former case, and an extended version of the ATC rule (ATCS) proposed by Lee, Bhaskaran, and Pinedo for the latter. Both approaches utilize some look-ahead parameters for calculating the priority index of each job. As reported by Bhaskaran and Pinedo, the proper value of the look-ahead parameter depends upon certain problem characteristics, such as due-date tightness and due-date range. Thus, an obvious extension of the ATC or the ATCS rule is to adjust the parameter values depending upon the problem characteristics: this is known to be a difficult task. In this paper, we propose an application of a neural network as a tool to ‘predict’ proper values of the look-ahead parameters. Our computational tests show that the proposed hybrid approach outperforms both the ATC rule with a fixed parameter value and the ATCS using the heuristic curve-fitting method.     

Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution

Count data analysis techniques have been developed in biological and medical research areas. In particular, zero-inflated versions of parametric count distributions have been used to model excessive zeros that are often present in these assays. The most common count distributions for analyzing such data are Poisson and negative binomial. However, a Poisson distribution can only handle equidispersed data and a negative binomial distribution can only cope with overdispersion. However, a Conway–Maxwell–Poisson (CMP) distribution [4] can handle a wide range of dispersion. We show, with an illustrative data set on next-generation sequencing of maize hybrids, that both underdispersion and overdispersion can be present in genomic data. Furthermore, the maize data set consists of clustered observations and, therefore, we develop inference procedures for a zero-inflated CMP regression that incorporates a cluster-specific random effect term. Unlike the Gaussian models, the underlying likelihood is computationally challenging. We use a numerical approximation via a Gaussian quadrature to circumvent this issue. A test for checking zero-inflation has also been developed in our setting. Finite sample properties of our estimators and test have been investigated by extensive simulations. Finally, the statistical methodology has been applied to analyze the maize data mentioned before.     

Uncovering bias in order assignment

To mitigate sequencing effects in decision-making, many situations require a set of items to be considered in a random order. When such orderings are repeated, one can test whether randomization indeed obtains, or whether some orderings have been manipulated in order to achieve a favorable result. This paper articulates the key features of this problem and presents three general tests for randomness. These methods are used to analyze the order in which lottery numbers are drawn in Powerball, contestants perform on American Idol, and candidates are placed on election ballots. This last application features frequent manipulation, with potentially serious consequences.     

Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium

Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole-genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77–0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92–0.94) for the logit boost algorithm. Logit boost algorithm was most accurate (valid accuracy: 92%, CI: 0.8706–0.9579) and predicted the origin of 81% of the domestic sporadic human salmonellosis cases. The most important source was Danish produced pigs (53%) followed by imported pigs (16%), imported broilers (6%), imported ducks (2%), Danish produced layers (2%), Danish produced cattle and imported cattle (<1%) while 18% was not predicted. Machine learning has potential for improving source attribution modeling based on sequence data. Results of such models can inform risk managers to identify and prioritize food safety interventions.     

At home with the young homeless

This paper considers ethnographic investigation 'at home' in reference, in part, to research carried out into youth homelessness in Britain. Relations of distance and familiarity in the field, and the notion of the ethnographer as stranger, are discussed so as to advance an understanding of the purpose and practice of participant observation. The paper explores fieldwork relations in the context of urban and social exploration, and also with reference to ethnographic research with young people and/or children. In conclusion, the paper argues for a recognition of difference and distance in fieldwork, not simply as obstacles to be somehow overcome but as something inevitable and necessary to the insights that fieldwork affords.     

Robotic assembly of printed circuit boards with component feeder location consideration

The use of industrial robots for the assembly of printed circuit boards (PCB) is fast gaining popularity. Minimization of the time required for board assembly while maintaining production quality is the process planner's main task. This task involves the specification of the insertion sequence of components in their respective locations and the assignment of component types to feeders or dispensing magazine slots. The time taken by a robot to fetch a component for insertion is dependent on the location of the feeder that holds the component type. Similarly, board assembly lime is dependent on the order of component insertion on the board and the fetch time of components between insertions. In this paper, models to minimize the total assembly time for a board are developed and studied. Factors considered in the analysis of the assembly time are the component assignment to magazine     

中国农村金融体制改革的最优路径选择

从系统论的观点出发,并利用“樊、胡福利指数模型”对我国农村金融体制改革路径进行了分析、验证。研究表明:我国现行农村金融体制所遵循的“循序渐进”改革路径造成了农村金融体制内部不协调发展。受“不协调成本”的影响,我国农村金融的功能非但没有在改革中更趋完善,反而出现了萎缩。建议我国农村金融体制选择“平行推进”的改革路径。     

On the Contingent Valuation of Safety and the Safety of Contingent Valuation: Part 2 - The CV/SG "Chained" Approach

This article reports the results of a study aimed at estimating a willingness-to-pay based value of statistical life for road risks using a multi-stage approach which involves "chaining together" responses to contingent valuation and standard gamble questions.The rationale for employing a multi-stage approach is to break the wealth/risk of death trade-off down into a number of conceptually manageable steps, thereby trying to attenuate the various biases that appear to be pervasive in responses to more direct contingent valuation questions in the health and safety field.