Agent-based interpretations of classic network models

The paper is aimed at developing agent-based variants of traditional network models that make full use of concurrency. First, we review some classic models of the static structure of complex networks with the objective of developing agent-based models suited for simulating a large-scale, technology-enabled social network. Secondly, we outline the basic properties that characterize such networks. Then, we briefly discuss some classic network models and the properties of the networks they generate. Finally, we discuss how such models can be converted into agent-based models (i) to be executed more easily in heavily concurrent environments and (ii) to serve as basic blocks for more complex agent-based models. We evidence that many implicit assumptions made by traditional models regarding their execution environment are too expensive or outright impossible to maintain in concurrent environments. Consequently, we present the concurrency issues resulting from the violation of such assumptions. Then, we experimentally show that, under reasonable hypothesis, the agent-based variants maintain the main features of the classic models, notwithstanding the change of environment. Eventually, we present a meta-model that we singled out from the individual classic models and that we used to simplify the agent-oriented conversion of the traditional models. Finally, we discuss the software tools that we built to run the agent-based simulations.     

Examination of flexibility acquisition policies in dual resource constrained job shops with simultaneous worker learning and forgetting effects

This study investigates worker training related issues in dual resource constrained (DRC)job shop environments with the simultaneous presence of learning, forgetting (which causes relearning) and worker attrition effects. Learning and forgetting occurs as workers are trained in different departments on the shop floor. A recent survey of job shops based in the US indicates that firms are concerned about the relearning losses that accompany worker transfers, and are using a variety of strategies to reduce the effects of relearning. This study uses the learn-forget-learn (LFL) model to capture different learning and forgetting environments in order to understand whether up-front training can be used to reduce relearning and realize the benefits of worker flexibility. Results show that the existing forgetting rate has a significant impact on inventory and customer service performance. Extensive up-front training helps reduce relearning, however, it does not always help improve performance.     

An Anthology on the Homology of Simulation with Artificial Intelligence

Simulation and artificial intelligence (AI) can be related in the following ways: the methodological similarities, the actual and potential uses of AI concepts in simulation, expert systems in simulation environments, and the gains for AI when applying ideas from simulation. This paper highlights the personal experiences of the author by discussing, for each of the four above-mentioned areas, a topic or a computer system in which he is directly involved. To highlight the similarities, it will be shown that the three-phase simulation model can be considered as a production-system model. A natural-language understanding system that was used in the definition stage of a simulation project will provide the basis for discussing AI concepts in simulation environments. An expert system used in the development and verification stages will provide an insight into the application of expert systems in simulation modelling. An expert-system development tool which is partially based on simulation techniques will be used to outline the gains for AI.     

Application of a primarily deductive framework describing time consumption for hauling of logs to road-side

Forest management planning decisions are often based on the forest owner’s goals, which typically focus on economic criteria. Logging operation work productivity functions are used when costing forest operations. These functions affect the conclusions drawn during forest management analyses because different logging environments give rise to different harvesting costs. When evaluating new combinations of machines and environments, there is generally a shortage of field data on productivity that can be examined in advance. We applied a previously published deductive framework describing time consumption in forwarding to known environments, in which field studies on forwarding have been conducted and for which extensive data are available. We then adapted the deductive framework to better reproduce the results obtained in the time studies. The deductive framework accurately reproduced the observed forwarding productivities; on average, the adaptation process improved the accuracy of this reproduction. However, it may also have reduced the accuracy of individual predictions. We conclude that the deductive framework can be used as a basis for constructing work productivity functions for forest management analyses, and can serve as a foundation when constructing new productivity functions based on time study results to use when pricing forwarding.     

Complex bifurcation/chaotic behavior of acetylcholinesterase and cholineacetyltransferase enzymes system

This investigation utilizes available biokinetic information for the formulation of a diffusion-reaction model in order to simulate the in vivo behavior of acetylcholinesterase (AChE) and cholineacetyltransferase (ChAT) coupled enzymes system. A novel two-enzymes/two-compartments model is used to explore the bifurcation and chaotic behavior of this enzyme system simulating the acetylcholine neurocycle in the brain. Detailed bifurcation analysis over a wide range of parameters is carried out in order to uncover some important information about this system. The results of this investigation relate to the phenomena occurring in the physiological experiments, like periodic stimulation of neural cells and non-regular functioning of acetylcholine receptors. These results can be used to direct more systematic research on cholinergic brain diseases like Alzheimer’s and Parkinson’s diseases as there are strong evidences that these brain disorders are related to concentration of acetylcholine (ACh) in the brain.     

Robust simulation with a base environmental scenario

In the design of a system, the comparison of possible solutions using simulation is generally performed with fixed environmental conditions. In practice, however, unexpected changes can occur for example in the part mix of a manufacturing facility or in the customer demand. Such changes, which are considered as modifications in environmental factors, can impact the system response. As a consequence, a solution A that is better than B for a given environment, can yield poorer performance than B for another environment. Therefore, we are interested in robust simulation studies, which aim at taking into account several possible environments. In methods based on Taguchi’s principles, no distinction is made between these environments in the robustness computation. In the suggested heuristic approach, we focus on problems where a particular environment is expected when the system will be in operation (the others being unexpected environments). This particular environment will be considered in the study as a “base environmental scenario”. The robustness of a solution of the design problem is computed as an approximate measure of what will be saved or lost if the environment becomes the unexpected. Reference curves are suggested to allow these solutions to be empirically compared in accordance with the decision-maker’s requirements. A simplified example is provided. The results are different from those obtained using a signal to noise ratio, which is typically used in Taguchian approaches.     

A survey of literature on automated storage and retrieval systems

Automated Storage and Retrieval Systems (AS/RSs) are warehousing systems that are used for the storage and retrieval of products in both distribution and production environments. This paper provides an overview of literature from the past 30 years. A comprehensive explanation of the current state of the art in AS/RS design is provided for a range of issues such as system configuration, travel time estimation, storage assignment, dwell-point location, and request sequencing. The majority of the reviewed models and solution methods are applicable to static scheduling and design problems only. Requirements for AS/RSs are, however, increasingly of a more dynamic nature for which new models will need to be developed to overcome large computation times and finite planning horizons, and to improve system performance. Several other avenues for future research in the design and control of AS/RSs are also specified.     

Students' understanding of the notion of function in dynamic geometry environments

To make optimal use of computational environments, one must understand how students interact with the environments and how students' mathematical thinking is reflected and affected by their use of the environments. Similarly, to make sense of research on students' thinking and learning, one must understand how the environments and contexts used in the research may affect the conclusions one derives.The research on students' learning of functions has approached the topic in terms of symbols and graphs (see, for example, Leinhardt et al. (1990) for a review of work up to that point; Harel and Dubinsky (1992) for a collection of research; and Dugdale et. al. (1995), for some recent thinking about implications for curriculum reform using technology). Dynamic geometry environments (DGEs) like Cabri Geometry or Geometer's Sketchpad, offer us an opportunity to get a new perspective on these old and important issues. DGEs let students build geometrical constructions and then drag certain objects around the screen in a continuous manner while observing how the entire construction responds dynamically. In this way DGEs model functional relationships that are not specified by symbols or represented by graphs.Based on interviews with undergraduate mathematics majors, this paper presents preliminary observations that confirm some old results and raise some new questions about students' notions of function.     

Design of electronic assembly lines: An analytical framework and its application

The design of component assembly lines in Printed Circuit Board (PCB) manufacturing environments is a challenging problem faced by many firms in the electronics industry. The main design approaches to such component assembly lines are the Mini-Line, Flexible Flow Line, and Hybrid Line designs. In this paper, we discuss the operational trade-offs associated with these design alternatives and present a mathematical programming framework that captures relevant system design issues. Each of the design alternatives can be viewed as a special case of the stated mathematical programming model. We develop effective algorithms to solve these mathematical programs. We have used the framework in a specific PCB manufacturing environment to advise managers on the best configuration of their lines. The models were used as sensitivity analysis tools. The results of our computational experiments, combined with qualitative comparisons of different design approaches developed by a crossfunctional team (engineers, manufacturing and product managers), have led to the development of a set of managerial guidelines for the selection of the design plan for component assembly lines in the studied environment.     

Fast and Exact Simulation of Fractional Brownian Surfaces

Fractional Brownian surfaces are commonly used as models for landscapes and other physical processes in space. This work shows how to simulate fractional Brownian surfaces on a grid efficiently and exactly by embedding them in a periodic Gaussian random field and using the fast Fourier transform. Periodic embeddings are given that are proven to yield positive definite covariance functions and hence yield exact simulations for all possible densities of the simulation grid. Numerical results show these embeddings can sometimes be made more efficient in practice. Further numerical results show how the ideas developed for simulating fractional Brownian surfaces can be used for simulating other Gaussian random fields. The simulation methodology is used to study the behavior of a simple estimator of the parameters of a fractional Brownian surface.     

Comments on algorithms for grid interfaces in simulating Euler flows

Some results concerning the algorithms for grid interfaces, which are crucial in simulating flows by zonal methods, are presented in this paper. It is indicated that the commonly used conservative interface scheme can ensure the discrete entropy condition, but it may be inconsistent and would bring a nonoverlapping solution on overlapping grids. A nonconservative interface matching obtained by interpolation can be monotonicity preserving, and it leads large conservation error when discontinuities are close to the interfaces. Methods for improvement of interface algorithms are also proposed.     

NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

In this paper, we propose a testing-coverage software reliability model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP). Software is usually tested in a given control environment, but it may be used in different operating environments by different users, which are unknown to the developers. Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures, but most of the underlying common assumptions of these models are that the operating environment is the same as the developing environment. But in fact, due to the unpredictability of the uncertainty in the operating environments for the software, environments may considerably influence the reliability and software's performance in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the theory reliability, and also from all its similar applications in other fields. In this paper, a new model is proposed with the consideration of the fault detection rate based on the testing coverage and examined to cover ID subject to the uncertainty of operating environments. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real software failure data based on seven criteria. Improved normalized criteria distance (NCD) method is also used to rank and select the best model in the context of a set of goodness-of-fit criteria taken all together. All results demonstrate that the new model can give a significant improved goodness-of-fit and predictive performance. Finally, the optimal software release time based on cost and reliability requirement and its sensitivity analysis are discussed.     

Analysis of stochastic problem decomposition algorithms in computational grids

Stochastic programming usually represents uncertainty discretely by means of a scenario tree. This representation leads to an exponential growth of the size of stochastic mathematical problems when better accuracy is needed. Trying to solve the problem as a whole, considering all scenarios together, yields to huge memory requirements that surpass the capabilities of current computers. Thus, decomposition algorithms are employed to divide the problem into several smaller subproblems and to coordinate their solution in order to obtain the global optimum. This paper analyzes several decomposition strategies based on the classical Benders decomposition algorithm, and applies them in the emerging computational grid environments. Most decomposition algorithms are not able to take full advantage of all the computing power available in a grid system because of unavoidable dependencies inherent to the algorithms. However, a special decomposition method presented in this paper aims at reducing dependency among subproblems, to the point where all the subproblems can be sent simultaneously to the grid. All algorithms have been tested in a grid system, measuring execution times required to solve standard optimization problems and a real-size hydrothermal coordination problem. Numerical results are shown to confirm that this new method outperforms the classical ones when used in grid computing environments.     

On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems

We point out the need for Behavioral Operational Research (BOR) in advancing the practice of OR. So far, in OR behavioral phenomena have been acknowledged only in behavioral decision theory but behavioral issues are always present when supporting human problem solving by modeling. Behavioral effects can relate to the group interaction and communication when facilitating with OR models as well as to the possibility of procedural mistakes and cognitive biases. As an illustrative example we use well known system dynamics studies related to the understanding of accumulation. We show that one gets completely opposite results depending on the way the phenomenon is described and how the questions are phrased and graphs used. The results suggest that OR processes are highly sensitive to various behavioral effects. As a result, we need to pay attention to the way we communicate about models as they are being increasingly used in addressing important problems like climate change.     

The signal model: A model for competing risks of opportunistic maintenance

This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.     

Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online Fuzzy–Genetic system

In this paper we present our novel Fuzzy–Genetic techniques for the online learning and adaptation of an intelligent robotic navigator system. Such a system could be used by autonomous mobile vehicles navigating in unstructured and changing environments. In this work we focus on the online learning of the obstacle avoidance behaviour, which is an example of a behaviour that receives delayed reinforcement. We show how this behaviour can be co-ordinated with other behaviours that receive immediate reinforcement (such as goal seeking and edge following) learnt during our previous work to generate an intelligent reactive navigator that can deal with unstructured and changing outdoor environments. The system described uses a life long learning paradigm whereby it is able to dynamically adapt to new environments and update its knowledge base.     

高速公路运输管理技术的启示：自然技术模仿社会的再探讨

以交通运输高速公路与信息高速公路为典例,通过这两类性质相异的交通运输管理技术系统和电子信息技术系统类比,显示两有诸多方面的相似行为与技术,从而表明社会技术与自然技术可以相互借鉴和移植;事理与物理相通。开展自然技术模仿社会的研究,以及在系统工程范畴里,开展社会技术和自然技术的综合研究,不仅有理论意义,而且有实用价值。     

A note on domain decomposition approaches for solving 3D variational data assimilation models

Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a big and computationally expensive problem. In the present work we prove that the functional decomposition of the 3D variational data assimilation (3D Var DA) operator, previously introduced by the authors, is equivalent to apply multiplicative parallel Schwarz (MPS) method, to the Euler–Lagrange equations arising from the minimization of the data assimilation functional. It results that convergence issues as well as mesh refininement techniques and coarse grid correction—issues of the functional decomposition not previously addressed—could be employed to improve performance and scalability of the 3D Var DA functional decomposition in real cases.

     

Global dynamics of a competition model with non-local dispersal I: The shadow system

Equations with non-local dispersal have been widely used as models in biology. In this paper we focus on logistic models with non-local dispersal, for both single and two competing species. We show the global convergence of the unique positive steady state for the single equation and derive various properties of the positive steady state associated with the dispersal rate. We investigate the effects of dispersal rates and inter-specific competition coefficients in a shadow system for a two-species competition model and completely determine the global dynamics of the system. Our results illustrate that the effect of dispersal in spatially heterogeneous environments can be quite different from that in homogeneous environments.