Lean rules extraction methodology for lean PSS design via key performance indicators monitoring

Lean PSS (Product-Service System) design comprises a promising strategy for delivering sustainable PSS offerings, considering several well-established lean practices. However, automated ways to apply lean practices and more specifically lean rules in industry are limited. This work proposes a methodology for improving the leanness of PSS design, by combining real-time KPI monitoring with lean principles and practices. Through a correlation of typical wastes with the metrics used in the calculation of KPIs, the Total Leanness Index (TLI) of the procedures is defined. Based on automatically identified trade-off values for TLI, lean rules are extracted to improve the performance of PSS lifecycle phases. The proposed lean rules extraction methodology (LeanREM) is validated through a case study of power waste reduction and the concurrently maintenance time decrease in a mould-making company.     

How to design effective priority rules: Example of simple assembly line balancing

Priority rule-based methods (PRBMs) rely on problem-specific knowledge to construct good solutions in a very short time. They can be used as stand-alone procedures or can be integrated into (partial) enumeration procedures, like branch and bound or dynamic programming, and heuristic solution methods. PRBMs are especially important for solving NP-hard optimization problems.In this paper, we formulate general design principles on how to construct good-performing PRBMs, based on a thorough computational investigation. Our principles allow to construct effective PRBMs already ad hoc, i.e. without time-consuming data mining algorithms. We conduct our analysis on the example of the NP-hard Simple Assembly Line Balancing Problem (SALBP), on which with small modifications most situations in the planning of assembly lines are based. We also provide a cross-validation of our results and illustrate the application of the formulated principles.     

Investigation of work zone crash casualty patterns using association rules

Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40 mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties.     

Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%–3.9% in two simulation periods compared with the Logistic-CA model with a 3 × 3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration.     

Gauss–Galerkin quadrature rules for quadratic and cubic spline spaces and their application to isogeometric analysis

We introduce Gaussian quadrature rules for spline spaces that are frequently used in Galerkin discretizations to build mass and stiffness matrices. By definition, these spaces are of even degrees. The optimal quadrature rules we recently derived (Bartoň and Calo, 2016) act on spaces of the smallest odd degrees and, therefore, are still slightly sub-optimal. In this work, we derive optimal rules directly for even-degree spaces and therefore further improve our recent result. We use optimal quadrature rules for spaces over two elements as elementary building blocks and use recursively the homotopy continuation concept described in Bartoň and Calo (2016) to derive optimal rules for arbitrary admissible numbers of elements. We demonstrate the proposed methodology on relevant examples, where we derive optimal rules for various even-degree spline spaces. We also discuss convergence of our rules to their asymptotic counterparts, these are the analogues of the midpoint rule of Hughes et al. (2010), that are exact and optimal for infinite domains.     

GPU accelerated pivoting rules for the simplex algorithm

Simplex type algorithms perform successive pivoting operations (or iterations) in order to reach the optimal solution. The choice of the pivot element at each iteration is one of the most critical step in simplex type algorithms. The flexibility of the entering and leaving variable selection allows to develop various pivoting rules. In this paper, we have proposed some of the most well-known pivoting rules for the revised simplex algorithm on a CPU–GPU computing environment. All pivoting rules have been implemented in MATLAB and CUDA. Computational results on randomly generated optimal dense linear programs and on a set of benchmark problems (Netlib-optimal, Kennington, Netlib-infeasible, Mészáros) are also presented. These results showed that the proposed GPU implementations of the pivoting rules outperform the corresponding CPU implementations.     

A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance

In condition-based maintenance, a common practice is to record a condition reading at a regular interval, and once the reading is higher than a pre-set critical level, the item monitored is declared faulty and repair or replacement may be initiated. However, surprisingly both in practice and theory, little attention has been paid to whether or not the critical level and the monitoring interval are set in a cost effective way. This paper reports on the development of a model that can be used to determine the optimal critical level and interval in condition-based maintenance in terms of a criterion of interest. The model is established on the basis of the random coefficient growth model where the coefficients of the regression growth model are assumed to follow known distribution functions. A simple example is given in the paper to illustrate the modelling ideas.     

POLITICAL PARADOXES OF MAJORITY RULE VOTING AND HIERARCHICAL SYSTEMS

The use of majority rule voting is believed to be instrumental to establish democratic operating of political organizations. However in this work it is shown that, while applied to hierarchical systems, it leads to political paradoxes. To substantiate these findings a model to construct self-directed pyramidal structures from bottom up to the top is presented. Using majority rules it is shown that a minority and even a majority can be systematically self-eliminated from top leadership, provided the hierarchy has a minimal number of levels. In some cases, 70% of the population is found to have zero representation after 6 hierarchical levels. Results are discussed with respect to internal operating of political organizations.     

A single chart with supplementary runs rules for monitoring the mean vector and the covariance matrix of multivariate processes

The MRMAX chart is a single chart based on the standardized sample means and sample ranges for monitoring the mean vector and the covariance matrix of multivariate processes. User’s familiarity with the computation of these statistics is a point in favor of the MRMAX chart. As a single chart, the recently proposed MRMAX chart is very appropriate for supplementary runs rules. In this article, we compare the supplemented MRMAX chart and the synthetic MRMAX chart with the standard MRMAX chart. The supplementary and the synthetic runs rules enhance the performance of the MRMAX chart.