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Demand for perishable food is sensitive to product prices and is affected by the prices of similar or alternative products. While brand loyalty influences the demand for products, determining a reasonable price requires a precise pricing strategy. In this paper, a pricing model for perishable food is presented in which the brand value of the product and the price of other manufacturers as competitors are considered. To this end, this study first predicts the price of competitors using a combination of optimized Neural Networks and presents an optimized model using a Genetic Algorithm. This algorithm combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a Genetic Algorithm (GA). The proposed model is then used to merge with a game-theory model for the pricing of perishable foods. In this game-theory model, pricing approaches are developed based on identified prices of competitors. In the coordination contract game-theory model, Multi Retailer- one Supplier and Price-sensitive demand of Perishable product are developed with and without quantity discount contract. Obtained results indicate that independent procurement provides retailers with higher profit, while lower profit will be presented when coordination is not considered. Also, with coordination, the ordering cycle increases, and the ordering frequency decrease. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTSActivity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data. Presents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation techniquePresents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation technique [ABSTRACT FROM AUTHOR]

Cognitive scientists and neuroscientists typically understand the brain as a complex communication/information-processing system. A limitation of this framework is that it requires cognitive systems to have prior knowledge about their environment to successfully perform some of their basic functions, such as perceiving. It is unclear how the source of such knowledge can be explained from within this framework. Drawing on Dennett (1981), we refer to this as the loans of intelligence problem. Recent advances in machine learning have resulted in the development of a family of algorithms, including the class known as autoencoders, that seem to provide a way for the information-processing framework to avoid this problem: cognitive systems do not require loans of intelligence, but instead acquire the knowledge necessary for perception through a process of unsupervised learning. This paper argues that although autoencoders do avoid the loans of intelligence problem, how they do so should not be understood from within the information-processing framework. Instead, their success should be interpreted as a proof of concept of how neural networks can attune to Gibsonian information. We thus propose that autoencoders belong to a class of algorithms for modeling the brain that have recently been dubbed direct fit algorithms. [ABSTRACT FROM AUTHOR]

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley's utility and interpretation with real-world data. The method is available as an open source Python package named geoshapley. [ABSTRACT FROM AUTHOR]

The oxidative coupling of methane (OCM) is a reaction with a promise to provide a gainful means of utilizing an abundant greenhouse gas, methane, to produce ethylene; one of the world's most important chemicals is challenged by the co-production of carbon dioxide, another greenhouse gas. The need to find efficient means of enhancing the reaction with a yield of the desirable C2 product and the reduction in the co-production of COx product continues to be the focus of increased research over the past two decades. The advent of modern computational techniques, including Density Functional Theory (DFT), and data analytical techniques, such as Machine Learning (ML), have inspired new ways of generating data and drawing intuition on the ways to improve the efficacy of the OCM reaction. This study focuses on highlighting the innovations carried out in the study of the OCM reaction over the last 22 years: the reaction mechanism, kinetics, and catalytic design. Despite the concerted efforts to model and design new catalysts, the development of improved catalysts that are selective for C2 yields higher than 30% at low temperatures continues to be a bottleneck in the process. The application of ML and DFT in OCM is poised to provide a means to predict, design, and develop new catalysts that will enhance the effectiveness of the reaction and the quality of the products. Both techniques provide opportunities to improve and ameliorate challenges bedeviling the OCM reaction, including the high activation energy, low C2 yield, and catalyst instability/deactivation. [ABSTRACT FROM AUTHOR]

Does acquiring artificial intelligence (AI) technologies from the US or China render countries more authoritarian or technologically less advantageous? In this article, we explore to what extent importing AI/high-tech from the US and/or China goes parallel with importers' (a) democratization or autocratization, (b) state capacity, and (c) technological progress across a decade (2010–2020). Our work demonstrates that not only are Chinese AI/high-tech exports not congruous with importers' democratic backsliding, but autocratization attributed to Chinese AI is also visible in importers of US AI. In addition, for most indicators, we do not observe any significant effect of acquiring AI from the US or China on importers' state capacity or technological progress across the same period. Instead, we find that the story has a global inequality dimension as Chinese exports are clustered around countries with a lower GDP per capita, whereas US high-technology exports are clustered around relatively wealthier states with slightly weaker capacity over territorial control. Overall, the article empirically demonstrates the limitations of some of the prevalent policy discourses surrounding the global diffusion of AI and its contribution to democratization, state capacity, and technological development of importer nations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

Educational assessment is inherently uncertain, where physiological, psychological and social factors play an important role in establishing judgements which are assumed to be "absolute". AI and other algorithmic approaches to grading of student work strip-out uncertainty, leading to a lack of inspectability in machine judgement and consequent problems of trust and reliability. The technique of Adaptive Comparative Judgement (ACJ), in focusing on small-stakes binary comparisons provides an alternative approach to dealing with uncertainty in grading. Rankings can be produced which codify uncertainty, rendering machine judgements inspectable. However, ACJ demands resources in terms of time and the number of people making comparisons. Machine learning trained to make binary comparisons can help to make the process of human comparison more efficient. In combining ACJ and AI, we argue that the result is an analogue-digital system where the physiological/analogue processes of assessment can be coordinated with digital services which steer human assessment to those judgements which are most uncertain requiring most human deliberation. Drawing on our design of such a system developed for medical judgements, we describe a general architecture for human–machine assessment in education and discuss its potential in bridging the gap between analogue human cognition and digital machine learning. [ABSTRACT FROM AUTHOR]

This study presents a unique method for network reconfiguration for Searching Maximum Loading Capacity Radial Network from an interconnected meshed network using continuation power flow. The network reconfiguration outcomes are also evaluated for various graph theory approaches presented in the literature. The combined graph theory and machine learning methods are used to determine the best sectional switch position. The system's radiality is verified using graph theory. The recommended technique is tested on a 30-node mesh network with 41 sectional switches. The results demonstrate that the distribution system can manage a higher connected load using the suggested Bayesian-Ridge approach while lowering total active and reactive power losses. [ABSTRACT FROM AUTHOR]

In certain domains, the actionable predictions of ML systems not only inform human decision-making but replace it entirely (Borch and Min [7]). Dalia Mukhtar-Landgren and Alexander Paulsson, in their paper 'From senses to sensors: autonomous cars and probing what machine learning does to mobilities studies', investigate the use of ML-powered sensors in autonomous vehicles. The proliferation of machine learning (ML) systems, which are algorithmic assemblages designed to extract patterns from data and make predictions, is visibly transforming society and everyday life. They either reconsider established notions to comprehend ML or analyze the implications of ML for specific theoretical traditions. [Extracted from the article]

We consider estimation problem in structural vector autoregressive model which disturbance has non-Gaussian distribution. We call this model as non-Gaussian vector autoregressive (NG-SVAR) model. Since the estimation problem of this model is closely related to the independent component analysis (ICA) developed in machine learning and signal processing we apply the theory of ICA to our estimation problem. However, since we do not know the true non-Gaussian distribution in practice, we cannot construct the exact loglikelihood function. In this paper we propose a pseudo maximum loglikelihood estimator instead. It is shown that our estimator is statistical efficient from view point of semiparametric statistics. Furthermore, we show that our estimator has satisfactory performance by Monte Carlo experiment and empirical example in small sample. [ABSTRACT FROM AUTHOR]

This article examines differences between landscapes and nightscapes, i.e. how what we encounter in daylight conditions differs considerably from what we encounter in the dark. I explore what landscape is and how it works, followed by examining how it and how it works is negated by darkness, only to be re-established through illumination. The purpose of this article is to illustrate how nightscapes are, in fact, superior to landscapes in terms of channelling people's desires. While darkness prevents the abstract machine of landscape from functioning, illumination returns it to action, providing those with the necessary capital the opportunity to influence people and shape their identities. Nightscapes do, however, provide opportunities to anyone who wishes to express oneself by utilising the illumination provided by others for their own purposes and, at times, against the others. [ABSTRACT FROM AUTHOR]

This paper presents a geometric approach to pitch estimation (PE) – an important problem in music information retrieval (MIR), and a precursor to a variety of other problems in the field. Though there exist a number of highly accurate methods, both mono-pitch estimation and multi-pitch estimation (particularly with unspecified polyphonic timbre) prove computationally and conceptually challenging. A number of current techniques, while incredibly effective, are not targeted towards eliciting the underlying mathematical structures that underpin the complex musical patterns exhibited by acoustic musical signals. Tackling the approach from both theoretical and experimental perspectives, we present a novel framework, a basis for further work in the area, and results that (while not state of the art) demonstrate relative efficacy. The framework presented in this paper opens up a completely new way to tackle PE problems and may have uses both in traditional analytical approaches as well as in the emerging machine learning (ML) methods that currently dominate the literature. [ABSTRACT FROM AUTHOR]

This paper undertakes a comparative analysis of the famous eighteenth-century chess-playing automaton known as Mechanical Turk and the Amazon microwork platform of the same name. The original Mechanical Turk was a life-sized automaton made in 1770 and publicly exhibited until the mid-1800s, and which played games of chess with the audience. Its movements were fully mechanical, but even more remarkably, it was promoted as the world's first 'thinking machine,' deciding each move of the chess pieces for itself. From the outset, it was widely assumed that the Mechanical Turk was a hoax, and that a human must be hidden inside the machine, directing the game. But it was a clever hoax whose trick was never discovered, and widely admired as such. Amazon's Mechanical Turk job platform functions in a similarly open way, this paper argues, as a sort of open technological hoax. Mechanical Turk provides a source of human cognitive labour that can be used to invisibly operate digital systems and programs that are widely assumed to be fully automated. Artificial intelligence is a twenty-first century thinking machine, it requires a human brain to make it work. Amazon's Mechanical Turk is a marketplace in which companies can hire piecemeal cognitive labour to patch gaps and train programs to keep those systems functioning. Providing what Jeff Bezos has called 'artificial artificial intelligence,' Amazon's Mechanical Turk, like Kempelen's automaton, both draws attention to and obfuscates the limits of automation and artificial intelligence. Taking the two iterations of the Mechanical Turk as rich cultural figures of automation for their respective periods, this paper will argue that the open secret of their artificial artificial intelligence is itself a form of misdirection that hides other, more successfully guarded secrets: the true extent of that labour, and the conditions in which it is performed. [ABSTRACT FROM AUTHOR]

Rotational grazing in paddocks is a strong indicator of intensive grassland management. Uncertainty in the extent of grassland management intensity is a reported source of uncertainty in greenhouse gas budgeting. This article outlines a method of detecting paddock locations in Sentinel 2 satellite imagery using a statistical multimodality test. The test was compared to three machine learning algorithms (support vector machine, random forest and extreme gradient boosting). Photographic records of the Eurostat 2018 LUCAS survey were used as ground truth data to confirm the presence or absence of paddocks. The multimodality test achieved an overall accuracy of 88.4% versus the best machine learning accuracy of 82.4%. The test was also used to map paddock occurrence at a regional scale in the Republic of Ireland. Overall map accuracy was 85.7% versus validation data. The test can be applied in temperate grasslands with pre-mapped field boundaries where rotational grazing is practiced. [ABSTRACT FROM AUTHOR]

The procedure for the engineering of products and production systems in manufacturing companies is often distributed over several organisational units. The different units involved in these procedures use their respective methods and tools, resulting in silo-like and distributed information and data sources for engineering. In this paper, the open platform amePLM is introduced. This platform is based on a semantic data model. The ontology as explicitly formulated data model allows an integrated view on data and information available in the systems used along the product lifecycle, and the automatic provision of suitable information to the user. Furthermore, this open approach allows the linking of the solution to existing engineering software systems in the sense of a continuous flow of information. [ABSTRACT FROM AUTHOR]

Multi-manned assembly lines are often designed to produce big-sized products, such as automobiles and trucks. In this type of production lines, there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones. In this paper, such a class of problems, namely, the multi-manned assembly line balancing problem is addressed, with the objective of minimising the cycle time.Amixed-integer mathematical programming formulation is proposed for the considered problem. This model has the primary objective of minimising the cycle time for a given number of workstations and the secondary objective of minimising the total number of workers. Since the addressed problem is NP-hard, two meta-heuristic approaches based on the simulated annealing algorithm have been developed: ISA and DSA. ISA solves the problem indirectly while DSA solves it directly. The performance of the two algorithms are tested and compared on a set of test problems taken from the literature. The results show that DSA outperforms ISA in term of solution quality and computational time. [ABSTRACT FROM AUTHOR]

Emission inspection of motor vehicles (emission inspection) is a crucial player in solving the problem of motor vehicle exhaust pollution, and research on the features affecting emission inspection results and their importance is a basis for optimizing the environmental management of motor vehicles. However, there is no study on the multi-feature impact analysis of the emission inspection results. This hinders the emission inspection from playing a better guiding role in the policy formulation of motor vehicle management. In this paper, the ensemble learning algorithm and interpretable machine learning theory are used. Nineteen feature indicators and over 400,000 vehicle mass analysis system (VMAS) detection data in Chengdu were selected from the emission inspection database to construct prediction models for emission inspection results. Moreover, the factors affecting emission inspection results and their ranks by importance were also obtained. The results revealed that the environment has a strong influence on the outcomes from emission inspections (accounting for about one-third of the total effect). Besides, the following eight feature indicators displayed great effects on emission inspection results in sequence: emission inspection agency (18.38%), world manufacturer code (15.01%), vehicle usage days (9.60%), transmission type (9.41%), accumulated mileage (9.21%), emission standard (5.82%), temperature (5.54%), and driving mode (5.50%). In this study, prediction models for emission inspection results are established, and the results are interpreted based on the interpretable machine learning theory. It is considered that more attention should be paid to the effect of inspection differences among emission inspection agencies on fairness, as well as the effects of differences in world manufacturer and transmission type on vehicle deterioration in future research. The supervision of emission inspection agencies, training of inspectors, elimination of obsolete vehicles, and government-guided purchase should be strengthened. This study provides empirical support for optimizing the formulation of motor vehicle environmental management policies. Implications: Emission inspection of motor vehicles (emission inspection) is a crucial player in solving the problem of motor vehicle exhaust pollution. In this work, prediction models for emission of motor vehicles inspection results are established. The results revealed that following eight feature indicators displayed great effects on emission inspection results in sequence: emission inspection agency (18.38%), world manufacturer code (15.01%), vehicle usage days (9.60%), transmission type (9.41%), accumulated mileage (9.21%), emission standard (5.82%), temperature (5.54%), and driving mode (5.50%). It is considered that more attention should be paid to the effect of inspection differences among emission inspection agencies on fairness, as well as the effects of differences in world manufacturer and transmission type on vehicle deterioration in future research. The supervision of emission inspection agencies, training of inspectors, elimination of obsolete vehicles, and government-guided purchase should be strengthened. This study provides empirical support for optimizing the formulation of motor vehicle environmental management policies. [ABSTRACT FROM AUTHOR]

Measuring corporate financial performance is an essential task in many supply chain decisions, such as supply chain strategic positioning and partner selection. This study introduces an analytical approach that can quickly scan financial data of many companies and produce a summary measure for each company. The approach offers organizations a less wearing way to obtain a holistic view of all target companies' financial performance patterns, which imply the underlying supply chain strategies. The strategy map, a two-dimensional representation of the summary, provides a comprehensible means to apprehend the relative strategic position and measure the similarity between companies. The approach relies on three popular machine learning models, forecasting, clustering, and classification. It takes multi-year, multi-variate financial time series from the three standard financial statements, learns the patterns from the data, and tunes model parameters to configure the final settings for future applications. The input data needed are relatively easy to obtain and the self-learning modules only require modest domain knowledge to apply the approach. Its noise reduction, outlier detection, and feature selection functions ensure a consistent and robust performance. The empirical test using data from all US manufacturers and traders listed on NYSE and NASDAQ demonstrates the efficacy of the approach. [ABSTRACT FROM AUTHOR]

In archaeology, predictive models play a key role in understanding the interactions between humans and the palaeo-environment. They are also of great value for cultural heritage management and planning purposes. This is particularly true for Palaeolithic sites in the east Austrian loess landscape, which are often deeply embedded in sediment sequences. In this study, we analyse the geospatial behaviour of 23 Upper Palaeolithic sites in Lower Austria. Hereby, we apply a new approach, which combines the advantages of a classical deductive method with the capabilities of machine learning, implemented via the MaxEnt software. The result is a predictive model for an area of 7850 km², exploring the potential for the presence of Upper Palaeolithic sites. The model highlights several spatial dynamics of site probability in the study area. Possible sources of inaccuracies within the source data and the methodology are critically discussed. [ABSTRACT FROM AUTHOR]

This research studies the optimal decision for product pricing, production lot sizing in a multi-stage serial just-in-time production system with kanban-controlled policy. A decentralised decision model and a centralised decision model of this problem are formulated as a mixed-integer nonlinear programming problem. In order to solve the models, three algorithms are developed. The first one is an approximate procedure which solves the decentralised decision model; the second one is a proximate optimal procedure using two-phase search technique that solves the centralised decision model, and the third one is an approximate method using meta-heuristic technique which is used for both decentralised and centralised models. Numerical example shows that centralised decision can obtain higher economic benefit with lower cost and higher revenue and profit. Meanwhile, when demand is more price sensitive, centralised decision can achieve significant profit enhancement. Computational results attribute to different characteristics of the problem and solution superiority. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy-based assessment model to evaluate system reliability of a labour-intensive manufacturing system with repair actions. Due to the uncertainty in human performance, labour-intensive manufacturing systems must determine the capacity of each labourer in order to accurately characterise the performance of the systems. Therefore, we model such a manufacturing system as a fuzzy multi-state network in order to characterise the labourers’ influence on workstation performance. First, the workstation reliability is defined according to the loading state by three fuzzy membership functions, namely ‘under loading’, ‘normal loading’ and ‘over loading’, respectively. The system reliability is subsequently evaluated with fuzzy intersection operations in terms of these workstation reliabilities. Thus, the system reliability is defined as a fuzzy membership function to assess whether the manufacturing system performance is sufficient to satisfy the demand reliably. A case study of a footwear manufacturing system is illustrated to explain the proposed model. Furthermore, we apply the proposed model to a non-labour-intensive manufacturing network in order to validate the applicability to this class of systems. [ABSTRACT FROM PUBLISHER]

A mesh type distribution network is often configured into a radial structure to offer a variety of practical benefits, such as lower short-circuit current, low operating costs, and low construction costs. This paper presented a novel technique for optimal planning of radial distribution networks based upon a combination of Graph Theory and Machine Learning (ML) approaches. A network comprises several sectionalizing and tie-type switches. The conversion of a mesh type system into a radial one involves the selection of the best set of switches on the feeder lines that are to be opened or closed. Out of all switching combinations, we have provided the optimal radial combination, consequently the maximum voltage stability of the network with less active and reactive power losses. The proposed system also preserves the system's radial nature, with out islanding any load by a simple coding scheme. To test the capabilities of the proposed technique, we carried out simulations on the 30-node test distribution system and the results are encouraging. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of scheduling a set of jobs having only two possible processing times on a set of unrelated parallel machines. This problem is a generalisation of the much more common problem of scheduling equal-length jobs on identical machines. Such a situation may occur in the production of two different types of products. First, we show that an earlier open problem of scheduling jobs with two possible processing times and on unrelated machines with the objective to minimise the makespan can be polynomially solved by an algorithm consisting of two phases. A slight modification of this algorithm yields an absolute worst-case error of for the case of two arbitrary processing times and ,. Thus, for practical problems of a large size with two types of products and two possible processing times, the approximation algorithm generates schedules very close to an optimal one. [ABSTRACT FROM AUTHOR]

Transitional inference is an empiricism concept, rooted and practiced in clinical medicine since ancient Greece. Knowledge and experiences gained from treating one entity (e.g., a disease or a group of patients) are applied to treat a related but distinctively different one (e.g., a similar disease or a new patient). This notion of "transition to the similar" renders individualized treatments an operational meaning, yet its theoretical foundation defies the familiar inductive inference framework. The uniqueness of entities is the result of potentially an infinite number of attributes (hence p = ∞ ), which entails zero direct training sample size (i.e., n = 0) because genuine guinea pigs do not exist. However, the literature on wavelets and on sieve methods for nonparametric estimation suggests a principled approximation theory for transitional inference via a multi-resolution (MR) perspective, where we use the resolution level to index the degree of approximation to ultimate individuality. MR inference seeks a primary resolution indexing an indirect training sample, which provides enough matched attributes to increase the relevance of the results to the target individuals and yet still accumulate sufficient indirect sample sizes for robust estimation. Theoretically, MR inference relies on an infinite-term ANOVA-type decomposition, providing an alternative way to model sparsity via the decay rate of the resolution bias as a function of the primary resolution level. Unexpectedly, this decomposition reveals a world without variance when the outcome is a deterministic function of potentially infinitely many predictors. In this deterministic world, the optimal resolution prefers over-fitting in the traditional sense when the resolution bias decays sufficiently rapidly. Furthermore, there can be many "descents" in the prediction error curve, when the contributions of predictors are inhomogeneous and the ordering of their importance does not align with the order of their inclusion in prediction. These findings may hint at a deterministic approximation theory for understanding the apparently over-fitting resistant phenomenon of some over-saturated models in machine learning. [ABSTRACT FROM AUTHOR]

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

The fitness evaluation (FE) management has been successfully applied to improve the performance of multi-population methods for dynamic optimisation problems (DOPs). In this work, we extend one of its variants to address DOPs which was recently proposed by the authors. The aim of our proposal is to increase the efficiency of the FE management. To this end, we propose a technique based on hierarchical learning automata that manages FEs at two level: at first level the algorithm decides which population should be executed, and at the second level it specifies the operation that should be performed by the selected population. A detailed experimental analysis shows the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

Formal languages and automata (FLA) theory is perceived by many as one of the hardest topics to teach or learn at the undergraduate level, due to the abstract nature of its contents; often containing proofs, lots of symbolic notations, and specifications of abstract computing machines that are difficult for many students to relate with. One way of approaching this problem is to administer numerous practice problems to students to aid learning. In this paper, we present: definitions of formal rules (using context-free grammar notations) for the synthesis of practice problems and solutions in a sub-domain of formal language theory – regular expressions, new algorithms for implementing these rules with iterations of generated problem instances and associated solutions. Generated problems are then embedded in predefined, short text response and multiple-choice question templates. The implementation of this technique produced numerous problems and solutions that can be used as practice, test or examination questions to support the teaching of regular expressions as a topic in FLA at the undergraduate level. [ABSTRACT FROM AUTHOR]

This paper has introduced new formal approaches to model behaviours and control of a class of uncertain timed discrete event systems represented by Timed Automata with Guards (TAGs). We propose alternative representations for Timed Automata with Guards which approximate their dynamics, since only extremal behaviours are considered. More precisely, recursive equations are proposed in (max , +) and (min , +) algebras to describe the worst and the optimistic behaviours. Thereafter, these developed linear models are used to treat a control problem of TAGs. Finally, the proposed methodologies are illustrated by a realistic study case that corresponds to a job-shop system. [ABSTRACT FROM AUTHOR]

To realize spot welding process of the id and base for micro-crystal resonator quickly and accurately, the action sequence is need to be designed for its spot welding device reasonably. The device's performance indexes are proposed and its preliminary action sequence is designed based the functional structure and process flow. Stateflow tool is used to the device's simulation model is established and the state transition diagram is obtained by Stateflow with the finite state machine theory. According to the simulation result, the action sequence for the spot welding device is optimized, and its final action sequence is determined. It shows that he optimized design can meet the function requirements for micro-crystal resonator spot welding device through the simulation analysis and prototype test. [ABSTRACT FROM AUTHOR]

The objective of this study is to propose a framework of virtual plant models for the verification of PLC logic through modeling and simulation. The proposed virtual plant model consists of three types of object: the virtual device model (object model), the intermediary transfer model (functional model), and the PLC program & HMI (dynamic model). A virtual device model consists of a physical part, which is used to represent the properties of a real device and a logical part, which is used to manage the device's operation. For the fidelity of the virtual plant model, an intermediary transfer model controls the virtual device through a PLC program and sends information on the virtual device's state to the supervisory control model. Moreover, the PLC program and HMI are used for constructing communication environments similar to a real manufacturing line. For the implementation of the proposed virtual plant model, this study employs an I/O model based on the formalism of Automata and Discrete Event Systems Specifications (DEVS). As a result of the application to a car assembly line, 18 error codes are detected through the manual mode and have been revised. Finally, we can confirm there is no sequential error in the PLC program by checking the time chart. Moreover, the bottleneck and ramp-up/down times are reduced when a manufacturing system/line is built. [ABSTRACT FROM AUTHOR]

Earlier studies indicated that using multiple dispatching rules (MDRs) for the various zones in the system can enhance the production performance to a greater extent than using a single dispatching rule (SDR) over a given scheduling interval for all the machines in the system, since MDRs employ the multi-pass simulation approach for real-time scheduling (RTS). However, if a classical machine learning approach is used, an RTS knowledge base (KB) can be developed by using the appropriate MDRs strategy (this method is called an intelligent multi-controller in this paper) as obtained from training examples. The main disadvantage of using MDRs is that the classes (scheduling decision variables) to which training examples are assigned must be provided. Hence, developing an RTS KB using the intelligent multi-controller approach becomes an intolerably time-consuming task because MDRs for the next scheduling period must be determined. To address this issue, we proposed an intelligent multi-controller incorporating three main mechanisms: (1) simulation-based training example generation mechanism, (2) data pre-processing mechanism and (3) SOM-based real time MDRs selection mechanism. Under various performance criteria over a long period, the proposed approach yields better system performance than the machine learning-based RTS using the SDR approach and heuristic individual dispatching rules. [ABSTRACT FROM AUTHOR]

In the present work, a STEP-based platform-independent system for design and manufacturing feature recognition is developed. A manufacturing feature taxonomy with multiple levels, which is based on the access directions of the feature, is proposed. The system can recognise both design and manufacturing features from the lower level geometry and topology available in the STEP file. The developed system can recognise intersecting features, which is a major shortcoming of previous attempts based on neutral formats. A more complete feature relationship analysis than available in the literature is carried out to find the relationships between all the types of features. Removal volumes and access directions of the features are determined to couple the feature recognition with down line applications. Raw material geometry is also considered while recognising the features. The present system is limited to parts that can be machined on a three-axis machining centre. [ABSTRACT FROM AUTHOR]

The focus of this study is the identical parts robotic cell scheduling problem with m machines under the assumption of process and operational flexibility. A direct consequence of this assumption is a new robot move cycle that has been overlooked in the existing literature. We prove that this new cycle dominates all classical robot move cycles considered in the literature for m = 2. We also prove that changing the layout from an in-line robotic cell to a robot-centered cell reduces the cycle time of the proposed cycle even further, whereas the cycle times of all other cycles remain the same. For the m-machine case, we find the regions where the proposed cycle dominates the classical robot move cycles, and for the remaining regions present its worst case performance with respect to classical robot move cycles. Considering the number of machines as a decision variable, we also find the optimal number of machines that minimizes the cycle time of the proposed cycle. [ABSTRACT FROM AUTHOR]

This paper deals with the control of the production rates and set-up actions of an unreliable multiple-machine, multiple-product manufacturing system. Each part type can be processed for a specified period on one of the involved machines. When switching the production from one type to another, each machine requires both set-up time and set-up cost. Our objective is to determine the production rates and a sequence of set-ups in order to minimize the total set-up and surplus cost. As an analytical or even a numerical solution of the problem is very difficult to find, a combined approach is presented. The proposed approach is based on stochastic optimal control theory, discrete event simulation, experimental design and response surface methodology. It is proved experimentally that an extended version of the Hedging Corridor Policy is more realistic and guarantees better performance for two study cases. The first consists of the unreliable one-machine case facing exponential failure and repair time distribution. The second, which is more complex and where the optimal control theory may not be easily used to obtain the optimal control policy, consists of five machines facing non-exponential failure and repair time distributions. To illustrate the contribution of the paper and the robustness of the obtained control policy, numerical examples and sensitivity analysis are presented. [ABSTRACT FROM AUTHOR]

This article reports on tool path generation by the iso-scallop height method for the three-axis ball-end milling of sculptured surfaces. In order to achieve the specified machining accuracy, constant scallop height machining requires an understanding of the three-dimensional machining geometry and the use of iterative approaches. Feng and Li have accomplished such work using the bisection method to search the scallop curves and the tool centre curves. This paper presents an analytic and geometric study of the machining aspects. Analysing the local properties of the distance functions, which indicate where the scallop point and the tool are centred, the bisection method can be replaced by the Newton iterative algorithm which converges faster. The derivatives of the functions are formulated by their Taylor approximations with a small error. The initial guesses are obtained by considering the local machining geometry. The proposed method significantly reduces the total computing time necessary to generate tool paths. [ABSTRACT FROM AUTHOR]

Due to the complexity of machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true for high-speed machining processes. To model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural-fuzzy approach appears ideally suited for this purpose. To illustrate the approach, the recently developed fuzzy adaptive networks are used to model dimensional error in turning operations. The fuzzy adaptive network has both the learning ability of a neural network and the linguistic representation of a complex, not well understood or vague phenomenon. An approximate model representing the influences of machining parameters on dimensional error is first established. This model is then improved by learning with the given training data. The improved models are verified by the use of test data, which are obtained by the use of actual experiments. [ABSTRACT FROM AUTHOR]

A robot system was developed for the assembly of flexible belt-shaped objects, and the assembly procedures employed by workers in using it were analysed. A multi-fingered hand was developed to hold the objects at several points, and to move them into the proper position for attachment to a part. In addition, an image processing method that can recognize the gripping positions on source objects and conditions after the source objects are attached is proposed. [ABSTRACT FROM AUTHOR]

This work considers an NP-Hard scheduling problem that is fundamental to the production planning of flexible machines, to cutting pattern industries and also to the design of VLSI circuits. A new asynchronous collective search model is proposed, exploring the search space in a manner that concentrates effort onto those areas of higher perceived potential. This is done with the use of a coordination policy which enables the processes with greatest performance to act as 'attractors' to those processes trapped in areas of worse perceived potential. Numerical results are obtained for problems of realistic industrial size, and those results are compared to previously published optimal solutions. This comparison demonstrates the effectiveness of the method, as in 276 problems out of a set of 280 we are able to match previously reported optimal results. [ABSTRACT FROM AUTHOR]

This paper presents the development of an intelligent predictive tool for improving the accuracy of parts produced on CNC turning centers. Actual dimensions and shape (form) produced on components during machining differ from the nominal dimensions commanded in the CNC program. These dimensional and form errors on parts depend on a variety of factors such as tool/work deflections, machine tool accuracy, machining conditions etc. These errors, if predicted, can be used to correct the ideal CNC code and thus improve the part accuracy in CNC machining. In the present work artificial neural networks are used to capture the complex relationship between the errors on the component and the input process conditions. Significant improvement in part accuracy has been obtained using the corrected CNC code during machining. [ABSTRACT FROM AUTHOR]

This article presents initial results in the search for analytical models that can predict the performance of one-machine systems under periodic and event-driven rescheduling strategies in an environment where different job types arrive dynamically for processing and set-up must incur when production changes from one product type to another. The scheduling algorithm considered uses a first-in firstout dispatching rule to sequence jobs and it also groups jobs with similar types to save set-up time. The analytical models can estimate important performance measures like average flow time and machine utilization, which can then be used to determine optimal rescheduling parameters. Simulation experiments are used to show that the analytical models accurately predict the performance of the single machine under the scheduling algorithm proposed. [ABSTRACT FROM AUTHOR]

A three-step approach is proposed to substantially reduce the high cost of development of control software for an FMS on the shop floor. First, by introducing the concept of multiple computing processes into Automata Theory, a finite capacity machine (FCM) is presented as an extended finite machine. An FCM is used to model the control of a flexible manufacturing system, which avoids the state explosion problem. Secondly, a structured adaptive supervisory control (SASC) model meeting the desired control objectives for an FMS is derived from a composed FCM. The SASC model can be constructed systematically. Finally, generic software components for an SASC model are described, which allow the SASC model to be cost-effectively transferred into shop-floor applicable control software. [ABSTRACT FROM AUTHOR]

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model's adaptive abilities in various operation conditions and can improve its generalization ability. [ABSTRACT FROM AUTHOR]

We consider parametric reachability control problems for real-time systems. We model the plant as an extension of parametric timed automata in which the timing constraints on these clocks can make use of parameters. This extension, which we call parametric game automata (PGAs), allows for partitioning the actions in the model between two antagonistic entities: the controller and the environment. The most general problem we study then consists in synthesising both a controller and values for the parameters such that some control location of the automaton is reachable. This problem is undecidable and we therefore provide a subclass of PGA called L/U game automata for which it is decidable. We then consider a backward fixed-point semi-algorithm for solving timed games with reachability objective allowing to compute the most permissive winning strategy. We argue the relevance of this approach and demonstrate its practical usability with a small case-study. [ABSTRACT FROM AUTHOR]

In this paper, the problem of relative predictability of failure event occurrences is investigated. The notion of relative predictability is proposed for logical automata and the concept of predictable rate is introduced to characterise the predictability property of a discrete-event system, which takes values in the interval [0, 1]. Intuitively, a discrete-event system being relatively predictable means that there exist some failure events which can be predicted from the observations of the system. The relationship between relative predictability and predictability introduced by Sahika Genc et al. analysed and the analysis shows that relative predictability is weaker than predictability for discrete-event systems. Furthermore, a necessary and sufficient condition for relative predictability is presented. In particular, an opacity-based algorithm is developed to test the relative predictability, which is polynomial in the number of states of the system. Also, some examples are provided to illustrate the presented results. [ABSTRACT FROM AUTHOR]

Automata defined by monads in categories are introduced as special examples of monoids actions on free -algebras, where is a monad in a category. Morphisms between monads are introduced as special functors between Kleisli categories. Any morphism generates a functor between the corresponding categories of monadic automata. The relationship between morphisms of monads and functors of corresponding monadic automata categories gives a common framework in the theory of automata defined by monads. The proposed framework unifies many of well-known automata types and transformation processes of one type automata to other type. The notion of a monadic automaton with input and output morphisms, and a language accepted by this monadic automaton are introduced. An acceptance of a language is preserved by morphisms between monadic automata with input and output morphisms and it is also preserved by morphisms between monads. [ABSTRACT FROM AUTHOR]

We investigated the feasibility of using stochastic Petri nets (SPNs) to describe the behaviours of automated storage/retrieval systems as well as to evaluate the performance of different control policies of such systems. SPNs have been studied in the past 2 decades, and have become a powerful tool in modelling concurrent and distributed systems that exhibit synchronization and contention of shared resources. It appeared that some important characteristics of AS/RS could be well modelled with SPNs. The SPN is a graph-based tool suitable for modelling systems (manufacturing, computer, biological, etc.). It can be used to model a system at different levels of abstraction. in this study. the efficiency, control rules, bay assignment, and many other performance issues associated with AS/RS have been studied in detail using SPN models. SPNs are shown to be equivalent to the stochastic processes in which embedded Markov chains can be recognized. In addition, a stochastic Petri net package, SPNP, was adopted to solve the reachability trees from which an embedded Markov chain was generated. Finally, the performance of those systems of our interest was evaluated. The unique features and flexibility of SPNs are presented. Furthermore, the SPN models for different control algorithms of an AS/RS are discussed with examples. [ABSTRACT FROM AUTHOR]

The problem of scheduling in flowshop and flowline-based manufacturing cell is considered with the bicriteria of minimizing makespan and total flowtime of jobs. The formulation of the scheduling problems for both the flowshop and the flowline- based manufacturing cell is first discussed. We then present the development of the proposed heuristic for flowshop scheduling. A heuristic preference relation is developed as the basis for the heuristic so that only the potential job interchanges are checked for possible improvement with respect to bicriteria. The proposed heuristic algorithm as well as the existing heuristic are evaluated in a large number of randomly generated large-sized flowshop problems. We also investigate the effectiveness of these heuristics with respect to the objective of minimizing total machine idletime. We then modify the proposed heuristic for scheduling in a cell, and evaluate its performance. [ABSTRACT FROM AUTHOR]

The design of flexible manufacturing systems is a complex decision-making process that typically involves the evaluation of a number of different design alternatives. The evaluation is often performed using modelling and simulation techniques. The role of visual interactive modelling and simulation in design of flexible manufacturing systems is discussed. The application of artificial intelligence techniques to implement a set of knowledge sources, used for accomplishment of various design activities, is described. An object-orientated implementation is used to illustrate the underlying concepts. [ABSTRACT FROM AUTHOR]

We present a set of diagnostic tools for machine-pan matrix clustering. Machine- part matrix clustering is traditionally viewed as the first step in designing a Cellular Manufacturing System. Clustering is achieved by permuting the rows and columns of the matrix to get a Block Diagonal Form (BDF). Without actual matrix clustering being done, most methods for clustering fail to predict objectively the adaptability of the given matrix for Cellular Manufacturing. This paper presents an approach which predicts clusters accurately without initial machine-part matrix clustering. In addition, it also provides quantitative information on whether the matrix encourages Cellular Manufacturing without obtaining or partitioning the BDF. Lastly, the method also identifies the part families corresponding to each machine group in the same sequence for both dimensions of the matrix. This yields a BDF rather than a Block Checkerboard Form for the clustered matrix which happens when the machine groups and part families are not correctly sequenced in both dimensions of the matrix. [ABSTRACT FROM AUTHOR]