Multi-ontology fusion and rule development to facilitate automated code compliance checking using BIM and rule-based reasoning

Code compliance checking plays a critical role that identifies substandard designs according to regulatory documents and promises the accuracy of the designs before construction. However, the traditional code compliance checking process relies heavily on human work. To help the users better understand the checking process, this study proposes a gray-box checking technique and a BIM-based (Building Information Modeling) automated code compliance checking methodology that leverages ontology. The proposed approach contains a code ontology, a designed model ontology, a merged ontology, a code compliance checking ontology, a set of mapping rules, and a set of checking rules. During the checking process, the ontologies provide knowledge bases, and the rules provide necessary logic. A five-step roadmap is proposed for code ontology development for domain experts. For the time being, pre-processing is applied to create the designed model ontology to achieve time saving. Next, an ontology mapping procedure between the code and the designed model ontology is executed to obtain the merged ontology. In the ontology mapping procedure, the mapping rules aim to mitigate the semantic ambiguity between design information and regulatory information and enrich building information's semantics. Subsequently, rule-based reasoning is applied based on the checking rules and the merged ontology for checking reports generation. Finally, according to Chinese building codes, an automated code compliance checking platform is implemented for real construction projects to validate the proposed methodology.     

Quay crane scheduling in automated container terminal for the trade-off between operation efficiency and energy consumption

At present, the automation of handling equipment has changed the operation mode in the automated container terminal. This paper investigates the automated quay crane scheduling problem (AQCSP) for the automated container terminal. The operation process of AQCSP is decomposed, and formulated it as a mixed integrated programming model. In the numerical experiments, the relation between operation efficiency and energy consumption has been quantitative analyzed by case study. Moreover, the sensitivity analysis of the ratios for all tasks in a vessel bay and the tasks in each stack are also presented. The findings of this study will provide a theoretical reference for the study on the trade-off operation efficiency and energy consumption on the operational level.     

A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce

Facility location problem is one of the most critical elements in the design of distribution systems, and numerous studies have focused on this issue. However, facility location theory and guidelines for B2C firms are sparse. In this paper, with regard to the customer characteristics peculiar to B2C e-commerce and the turbulence of the competitive market, a new fuzzy location model is proposed to optimize the distribution system design in B2C e-commerce. The model adopts a hierarchical agglomerative clustering method to classify customers and estimate the fuzzy delivery cost. At the same time, due to the turbulence of competitive market, both market supply and customer demand are treated as fuzzy variables in the model. Afterward, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one which has NP-hard complexity. In order to solve the crisp model, an improved genetic algorithm with particle swarm optimization is developed. Finally, the computational results of some numerical examples are used to illustrate the application and performance of the proposed model and algorithm.     

Characterization and explanation of the sustainability of the European wood manufacturing industries: A quantitative approach

This paper has a twofold purpose. First, to characterize the sustainability of the European wood manufacturing industry. In this way, a ranking of the European countries analyzed in terms of sustainability is established. To undertake this task the sustainability of each country is defined by using several indicators of diverse nature (economic, environmental and social). These indicators are aggregated into a composite or synthetic index with the help of a binary goal programming model. In this way, a ranking according to the sustainability of the wood manufacturing industry in the European countries studied is obtained. The second step in the research consists of explaining the causes behind the level of sustainability of each country. This task is carried out by taking the composite indexes of sustainability as endogenous variables and a tentative set of economic, environmental and social variables as explanatory variables. The link between endogenous and exogenous variables is made with the help of econometric models.     

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest.     

A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles

We address a bilevel decomposition algorithm for solving the simultaneous scheduling and conflict-free routing problems for automated guided vehicles. The overall objective is to minimize the total weighted tardiness of the set of jobs related to these tasks. A mixed integer formulation is decomposed into two levels: the upper level master problem of task assignment and scheduling; and the lower level routing subproblem. The master problem is solved by using Lagrangian relaxation and a lower bound is obtained. Either the solution turns out to be feasible for the lower level or a feasible solution for the problem is constructed, and an upper bound is obtained. If the convergence is not satisfied, cuts are generated to exclude previous feasible solutions before solving the master problem again. Two types of cuts are proposed to reduce the duality gap. The effectiveness of the proposed method is investigated from computational experiments.     

A new hybrid fuzzy-goal programming scheme for multi-objective topological optimization of static and dynamic structures under multiple loading conditions

This work presents a hybrid fuzzy-goal multi-objective programming scheme for topological optimization of continuum structures, in which both static and dynamic loadings are considered. The proposed methodology fortopological optimization first employs a fuzzy-goal programming scheme at the top level for multi-objective problems with static and dynamic objectives. For the static objective with multi-stiffness cases in the fuzzy-goal formulation, a hybrid approach, involving a hierarchical sequence approach or a hierarchical sequence approach coupled with a compromise programming method, is especially suggested for the statically loaded multi-stiffness structure at the sublevel. Concerning dynamic optimization problems of freevibration cases, nonstructural mass, oscillation of the objective function, and repeated eigenvalues are also discussed. Solid Isotropic Material with Penalization density–stiffness interpolation scheme is used to indicate the dependence ofmaterial modulus upon regularized element densities. The globally convergent version of the method of moving asymptotes and the sequential linear programming method areboth employed as optimizers. Several applications have been applied to demonstrate the validation of the presented methodologies.     

A goal programming model for joint decision making of inventory lot-size,supplier selection and carrier selection

In this paper, we address a problem in which a storage space constrained buyer procures a single product in multiple periods from multiple suppliers. The production capacity constrained suppliers offer all-unit quantity discounts. The late deliveries and rejections are also incorporated in sourcing. In addition, we consider transportation cost explicitly in decision making which may vary because of freight quantity and distance of shipment between the buyer and a supplier. We propose a multi-objective integer linear programming model for joint decision making of inventory lot-sizing, supplier selection and carrier selection problem. In the multi-objective formulation, net rejected items, net costs and net late delivered items are considered as three objectives that have to be minimized simultaneously over the decision horizon. The intent of the model is to determine the timings, lot-size to be procured, and supplier and carrier to be chosen in each replenishment period. We solve the multi-objective optimization problem using three variants of goal programming (GP) approaches: preemptive GP, non-preemptive GP and weighted max–min fuzzy GP. The solution of these models is compared at different service-level requirements using value path approach.     

A step-by-step dual cycle sequencing method for unit-load automated storage and retrieval systems

The sequencing of requests in an automated storage and retrieval system was the subject of many studies in literature. However, these studies assumed that the locations of items to be stored and retrieved are known and the sequencing problem consisted in determining a route of minimal travel time between these locations. In reality, for a retrieval request, an item can be in multiple locations of the rack and so there is a set of locations associated with this item and not only one predetermined location in the rack. In this paper, we deal with the sequencing problem where a required product can be in several rack locations and there is a set of empty locations. Consequently, the retrieval and storage locations are not known a priori. We sequence by the minimum travel time of a double cycle (DC). An optimization method working step-by-step is developed to determine for each DC and according to storage and retrieval requests, the location of the item to be stored and the location of the item to be retrieved allowing the minimum DC time. The storage requests are processed in FCFS and retrieval requests retrievals requests are gathered by block according to wave sequencing.     

How to combine inconsistent ordinal and cardinal preferences: A satisficing modelling approach

This paper analyzes a situation where a decision-maker provides cardinal and ordinal information about her/his preferences towards m objects (criteria, alternatives, etc.). However, the two rankings derived from her/his preferences are different. This situation of “inconsistent preferences” is formalized. Moreover, in order to deal with this type of conflictive situation, a satisficing model based on the minimization of p-metric distance functions is proposed. In this way, a final set of weights associated with the m objects, by using the cardinal and the ordinal information, is obtained. The final model corresponds to a linear extended goal programming, so the proposed approach is operationally and computationally efficient, since the computations are reduced to solving a small number of linear programming models of a moderate size.     

A robust and accurate geometric model for automated design of drawbeads in sheet metal forming

A robust and accurate geometric model of real drawbeads that can be used for the automated design of drawbeads is presented in the paper. A three-dimensional geometric drawbead is a lofted surface, of which the section curves are constructed parallel to the stamping direction on the control points. Adaptive control point interpolation is introduced to simplify the management of the drawbead geometry and avoid unexpected shapes. Given primitive control points on a drawbead curve, dominant control points are adaptively obtained with the shapes of both the drawbead curve and the binder considered. An a priori heuristic parameter adjustment strategy is proposed to correct the parameter errors of section curves, which improves the accuracy and consistency of the drawbead geometry. By incorporating the proposed geometric drawbead with a previously developed intelligent drawbead optimization algorithm, a fully automated design process for drawbeads is realized that includes geometric modeling, finite element analysis, intelligent optimization of the drawbead geometry, and die manufacturing. Finally, a fender example is presented to verify the feasibility and validity of the fully automated drawbead design process. The simulation results with the optimized geometric drawbeads and equivalent drawbeads are compared with the experimental results. The proposed geometric drawbead shows remarkable practicability and accuracy in the automated design of drawbeads in sheet metal forming and demonstrates good consistency with the experimental results while the equivalent drawbead model introduces unneglectable deviations.     

A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimension-related feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure. The results were evaluated by expert radiologists and are very promising, showing equal or higher success rates compared to other related works, despite the fact that some of them used only selected portions of this specific mammographic database. In contrast, our methodology is applied to the complete miniMIAS database and it exhibits the reliability that is normally required for clinical use in CAD systems.     

A route and speed optimization model to find conflict-free routes for automated guided vehicles in large warehouses based on quick response code technology

This paper focuses on exploring conflict-free routes for automated guided vehicles (AGVs) based on quick response (QR) code technology. Intelligent warehousing has become an industrial development trend, with an increasing number of enterprises deploying AGVs based on QR code technology for transportation tasks, especially in large warehouses. Unlike the early single lane path following navigation systems, AGVs using QR code technology can freely switch tracks at any position. And two AGVs can be accommodated side by side. The conditions of conflict-free routes are analyzed firstly when AGVs use QR code navigation system. After that, a route and speed optimization model (RSOM) that aims at minimizing energy consumption and taking conflict-free routes with time windows as the constraint condition is established. Then a branch-and-bound (B&B) algorithm scheme is proposed by altering AGV routes or speed to arrive at schemes with conflict-free routes to complete transportation tasks. Lastly, the effectiveness and validity of our proposed model and algorithm are verified by numerical examples and a case study. The results show that resolving conflicts during route planning for AGVs guided through QR code have advantages over avoiding collision in the running phase by technology and path following techniques in both aspects of solution time and energy. In addition, the established B&B algorithm has better performance than the rapid-exploring random tree (RRT) algorithm in numerical examples. The gaps between them widen as the size of AGVs increases. The research work has potential for application to meet the requirements of AGV development.     

A domain-specific language for model mutation and its application to the automated generation of exercises

Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as main assets in all development phases. While many languages for model manipulation exist (e.g., for model transformation or code generation), there is a lack of frameworks to define and apply model mutations.A model mutant is a variation of an original model, created by the application of specific model mutation operations. Model mutation has many applications, for instance, in the areas of model transformation testing, model-based testing or education.In this paper, we present a domain-specific language called Wodel for the specification and generation of model mutants. Wodel is domain-independent, as it can be used to generate mutants of models conformant to arbitrary meta-models. Its development environment is extensible, permitting the incorporation of post-processors for different applications. In particular, we describe Wodel-Edu, a post-processing extension directed to the automated generation of exercises for particular domains and their automated correction. We show the application of Wodel-Edu to the generation of exercises for deterministic automata, and report on an evaluation of the quality of the generated exercises, obtaining overall good results.     

A constraint solver for discrete lattices,its parallelization,and application to protein structure prediction

This paper presents the design, implementation and application of a constraint programming framework on 3D crystal lattices. The framework provides the flexibility to express and resolve constraints dealing with structural relationships of entities placed in a 3D lattice structure in space. Both sequential and parallel implementations of the framework are described, along with experiments that highlight its superior performance with respect to the use of more traditional frameworks (e.g. constraints on finite domains and integer programming) to model lattice constraints. The framework is motivated and applied to address the problem of solving the protein folding prediction problem, i.e. predicting the 3D structure of a protein from its primary amino acid sequence. Results and comparison with performance of other constraint‐based solutions to this problem are presented. Copyright © 2007 John Wiley & Sons, Ltd.     

A vector C and Fortran compiler for the FPS T-series: Experiences with compiling to occam I

We describe our implementation of C and Fortran preprocessors for the FPS T-series hypercube. The target of these preprocessors is the occam I language. We provide a brief overview of the INMOS transputer and the Weitek vector processing unit (VPU). These two units comprise one node of the T-series. Some depth of understanding of the VPU is required to fully appreciate the problems encountered in generating vector code. These complexities were not fully appreciated at the outset. The occam I language is briefly described. We focus on only those aspects of occam I which differ radically from C. The transformations used to preprocess C into occam I are discussed in detail. The special problems with the VPU both in terms of its (non)interface with occam I and in dealing with numerical programs is discussed separately. A lengthy discussion on the special techniques required for compilation is provided. C and Fortran are simply incompatible with the occam I model. We provide a catalogue of problems encountered. We emphasize that these problems are not so much with occam I but with preprocessing to occam I. We feel the CSP and occam I models are quite good for distributed processing. The ultimate message from this work should be seen in a larger context. Several languages—such as Ada and Modula-2—are being touted as the standards for the 1990s. These languages severely restrict parallel programming style; this may make saving dusty decks by preprocessing an impossibility.     

A different paradigm for expert systems: an introduction to logic programming and related knowledge representation issues

Abstract: Current expert system technology is 30 years old. Expert system shells find their origins in the work of early expert systems, most notably MYCIN which was developed at Stanford in the mid-1970s. Even Prolog programmers are settling for less robust reasoning power. The logic programming community (from which both expert systems and Prolog arose) has made notable advances since those times. These advances are lacking from current expert system technology. The advances include a well-developed theory of multiple forms of negation, an understanding of open domains and the closed world assumption, default reasoning with exceptions, reasoning with respect to time (i.e. a solution to the frame problem, and introspection with regard to previous beliefs), reasoning about actions, introspection, and maintaining multiple views of the world simultaneously.
The contribution of this paper is to discuss these developments in a singular, integrated, practical, digestible manner. Some of these ideas exist in a variety of papers spread across decades. They also exist in the minds of a very small community of researchers. Some of these ideas are unpublished. The presentation in this paper is from a different point of view, and intended to be more comprehensive and pedagogical. The presentation is also intended to be accessible to a much wider audience. Both the synthesis and the simplicity of this presentation are absent from the literature.     

A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems

Providing efficient workload management is an important issue for a large-scale heterogeneous distributed computing environment where a set of periodic applications is executed. The considered shipboard distributed system is expected to operate in an environment where the input workload is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The tasks consist of multiple strings, each made up of an ordered sequence of applications. There is a quality of service (QoS) minimum throughput constraint that must be satisfied for each application in a string, and a maximum utilization constraint that must be satisfied on each of the hardware resources in the system. The challenge, therefore, is to efficiently and robustly manage both computation and communication resources in this unpredictable environment to achieve high performance while satisfying the imposed constraints. This work addresses the problem of finding a robust initial allocation of resources to strings of applications that is able to absorb some level of unknown input workload increase without rescheduling. The proposed hybrid two-stage method of finding a near-optimal allocation of resources incorporates two specially designed mapping techniques: (1) the Permutation Space Genitor-Based heuristic, and (2) the follow-up Branch-and-Bound heuristic based on an Integer Linear Programming (ILP) problem formulation. The performance of the proposed resource allocation method is evaluated under different simulation scenarios and compared to an iteratively computed upper bound.     

A novel risk attitudinal ranking method for intuitionistic fuzzy values and application to MADM

The ranking of intuitionistic fuzzy sets (IFSs) is very important for the intuitionistic fuzzy decision making. The aim of this paper is to propose a new risk attitudinal ranking method of IFSs and apply to multi-attribute decision making (MADM) with incomplete weight information. Motivated by technique for order preference by similarity to ideal solution (TOPSIS), we utilize the closeness degree to characterize the amount of information according to the geometrical representation of an IFS. The area of triangle is calculated to measure the reliability of information. It is proved that the closeness degree and the triangle area just form an interval. Thereby, a new lexicographical method is proposed based on the intervals for ranking the intuitionistic fuzzy values (IFVs). Furthermore, considered the risk attitude of decision maker sufficiently, a novel risk attitudinal ranking measure is developed to rank the IFVs on the basis of the continuous ordered weighted average (C-OWA) operator and this interval. Through maximizing the closeness degrees of alternatives, we construct a multi-objective fractional programming model which is transformed into a linear program. Thus, the attribute weights are derived objectively by solving this linear program. Then, a new method is put forward for MADM with IFVs and incomplete weight information. Finally, an example analysis of a teacher selection is given to verify the effectiveness and practicability of the proposed method.