An XML-based modular system analysis and design for supply chain simulation

In the contemporary global market, managing the entire supply chain has become crucial for competitive advantage in different industries. However, the realization and success of supply chain management (SCM) depends significantly on the previous supply chain modeling and simulation for analyzing complex systems.     

A modular approach to defining and characterising notions of simulation

We propose a modular approach to defining notions of simulation, and modal logics which characterise them. We use coalgebras to model state-based systems, relators to define notions of simulation for such systems, and inductive techniques to define the syntax and semantics of modal logics for coalgebras. We show that the expressiveness of an inductively defined logic for coalgebras w.r.t. a notion of simulation follows from an expressivity condition involving one step in the definition of the logic, and the relator inducing that notion of simulation. Moreover, we show that notions of simulation and associated characterising logics for increasingly complex system types can be derived by lifting the operations used to combine system types, to a relational level as well as to a logical level. We use these results to obtain Baltag’s logic for coalgebraic simulation, as well as notions of simulation and associated logics for a large class of non-deterministic and probabilistic systems.     

A modular approach to the specification and management of time duration constraints in BPMN

The modeling and management of business processes deals with temporal aspects both in the inherent representation of activity coordination and in the specification of activity properties and constraints. In this paper, we address the modeling and specification of constraints related to the duration of process activities. In detail, we consider the Business Process Model and Notation (BPMN) standard and propose an approach to define re-usable duration-aware process models that make use of existing BPMN elements for representing different nuances of activity duration at design time. Moreover, we show how advanced event-handling techniques may be exploited for detecting the violation of duration constraints during the process run-time. The set of process models specified in this paper suitably captures duration constraints at different levels of abstraction, by allowing designers to specify the duration of atomic tasks and of selected process regions in a way that is conceptually and semantically BPMN-compliant. Without loss of generality, we refer to real-world clinical working environments to exemplify our approach, as their intrinsic complexity makes them a particularly challenging and rewarding application environment.     

A graph theoretic approach to simulation and classification

A new class of discrete random fields designed for quick simulation and covariance inference under inhomogeneous conditions is introduced and studied. Simulation of these correlated fields can be done in a single pass instead of relying on multi-pass convergent methods like the Gibbs Sampler or other Markov chain Monte Carlo algorithms. The fields are constructed directly from an undirected graph with specified marginal probability mass functions and covariances between nearby vertices in a manner that makes simulation quite feasible yet maintains the desired properties. Special cases of these correlated fields have been deployed successfully in data authentication, object detection and CAPTCHA¹ generation. Further applications in maximum likelihood estimation and classification such as optical character recognition are now given within.     

Milnor algebras could be isomorphic to modular algebras

We find and describe unexpected isomorphisms between two very different objects associated to hypersurface singularities. One object is the Milnor algebra of a function, while the other object is the local ring of the flatness stratum of the singular locus in a miniversal deformation, an invariant of the contact class of a defining function. Such isomorphisms exist for unimodal hypersurface singularities. However, for the moment it is not well understood, which principle causes these isomorphisms and how far this observation generalises. Here, we also provide an algorithmic approach for checking algebra isomorphy.     

A modular simulation approach for automated material handling systems

In this paper, the authors present a modular simulation approach for the evaluation of automated material handling systems (AMHSs). The approach involves the use of high level modularity so that alternative AMHS scenarios can be evaluated using identical or nearly identical source code. The approach is implemented using the AutoMod simulation platform, and the efficacy of the strategy is demonstrated through a case study with the United States Postal Service. Results indicate that the approach is viable and that it is of value for the evaluation of AMHS scenarios in general.     

Performance analysis of an adaptive dynamic grid-based approach to data distribution management

Data distribution management (DDM) plays a key role in traffic control for large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include the region-based, fixed grid-based, and dynamic grid-based (DGB) schemes, as well as grid-filtered region-based and agent-based DDM schemes. However, less effort has been directed toward improving the processing performance of DDM techniques. This paper presents a novel DDM scheme called the adaptive dynamic grid-based (ADGB) scheme that optimizes DDM time through the analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as the distribution rate (DR) is devised. The DR represents the relative processing load and communication load generated at each federate. The DR and the matching performance are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves the maximum gain with acceptable network traffic overhead. If we assume the same worst case propagation delays, when the matching probability is high, the performance estimation of ADGB has shown that a maximum efficiency gain of 66% can be achieved over the DGB scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.     

A fault-tolerant approach to robot teams

As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5–1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m² environment.     

A network-centric approach to space-restricted distributed processing

The Space Efficient Embedded Cluster (SEEC) is a new system that offers a practical solution to space-restricted distributed processing. Utilising Linux compatible, embedded network controllers and standard Beowulf libraries presents developers with an easy to use, modular, distributed architecture. Networking characteristics are examined to quantify MPI operational overheads. Analysis in terms of weight and volume is undertaken when compared to a reference PC system for the example application RC5. A similar analysis is presented for DGEMM when utilising a possible, modular, per node FPGA enhancement.     

Morphological approach for autonomous and adaptive systems based on self-reconfigurable modular agents

This paper describes a novel approach for managing self-organizing, distributed modular components in dynamically changing environments. The main concept is to fabricate a system which is composed of dynamically associated modular agents, that can migrate and reorganize by itself while the system is being executed. Association between modular agents can be varied and transmuted according to components’ own migration schemes including deployment based on biological processes. This paper presents a self-organizable architecture, which can reorganize and reconfigure a system based on modular agents. It is contrived through observation of biological phenomena, and implements a platform to host the architecture in dynamically changing environments. We draw several key features of the modular agents, describe the principles of the modular agent based self-organizable framework, and depict how the proposed framework satisfies the functional requirements of network applications, which are made of several agents. We also demonstrate the efficiency and scalability of the framework through examining some simulation results.     

Distributed simulation of modular time Petri nets: An approach and a case study exploiting temporal uncertainty

This paper proposes an approach to modular modelling and simulation of complex time-critical systems. The modelling language is represented by Merlin and Farber’s Time Petri Nets (TPNs) augmented with inhibitor arcs and modular constructs borrowed from the Petri Net Markup Language (PNML) interchange format. Analysis techniques depend on Temporal Uncertainty Time Warp (TUTW), a time warp algorithm capable of exploiting temporal uncertainty in general optimistic simulations over a networked context. A key feature of the approach is the fact that TPN models naturally exhibit a certain degree of temporal uncertainty which the TUTW control engine can exploit to achieve good speedup without a loss in the accuracy of the simulation results. The developed TUTW/TPN kernel is demonstrated by modelling and simulation of a real-time system example.A preliminary version of this paper was presented at 38th SCS Annual Simulation Symposium, April 4–6, 2005, San Diego (CA), IEEE Computer Society, pp. 233–240. Franco Cicirelli achieved a PhD in computer science from the University of Calabria (Unical), DEIS—department of electronics informatics and systems science. As a postdoc, he is making research on agent and service paradigms for the development of distributed systems, parallel simulation, Petri nets, distributed measurement systems. He holds a membership with ACM. Angelo Furfaro, PhD, is a computer science assistant professor at Unical, DEIS, teaching object-oriented programming. His research interests are centred on: multi-agent systems, modeling and analysis of time-dependent systems, Petri nets, parallel simulation, verification of real-time systems, distributed measurement systems. He is a member of ACM. Libero Nigro is a full professor of computer science at Unical, DEIS, where he teaches object-oriented programming, software engineering and real-time systems courses. He directs the Software Engineering Laboratory (www.lis.deis.unical.it). His current research interests include: software engineering of time-dependent and distributed systems, real-time systems, Petri nets, modeling and parallel simulation of complex systems, distributed measurement systems. Prof. Nigro is a member of ACM and IEEE.     

R2P: An open source hardware and software modular approach to robot prototyping

Rapid prototyping is a key aspect for the development of innovative robotic applications. A modular, platform-based, approach is the way to obtain this result. Modular approaches are common for software development, but hardware is still crafted often re-inventing solutions every time. As a consequence, the resources that should be invested in the development of a new robot get often drained by the implementation of a physical, working prototype to test the application idea. To overcome this problem, we propose R2P (Rapid Robot Prototyping), a framework to implement real-time, high-quality architectures for robotic systems with off-the-shelf basic modules (e.g., sensors, actuators, and controllers), integrating hardware and software, which can be assembled in a plug-and-play way. R2P provides hardware modules, a protocol for real-time communication, a middleware to connect components as well as tools to support the development of software on the modules. R2P aims at dramatically reduce time and efforts required to build a prototype robot, making it possible to focus the resources on the development of new robotic applications instead of struggling on their implementation. This enables also people with experience in a specific application domain, but with little technical background, to actively participate in the development of new robotic applications. R2P is open-source both in its software and hardware to promote its diffusion among the robotics community and novel business models that will substantially reduce the costs to design a new robotic product.     

A tool-supported approach to inter-tabular verification

The use of decision tables to verify knowledge based systems (KBS) has been advocated several times in the validation and verification (V&V) literature. However, one of the main drawbacks of these systems is that they fail to detect anomalies that occur over rule chains. In a decision table based context this means that anomalies that occur due to interactions between tables are neglected. These anomalies are called inter-tabular anomalies. In this paper we investigate an approach that deals with inter-tabular anomalies. One of the prerequisites for the approach was that it could be used by the knowledge engineer during the development of the KBS. This requires that the anomaly check can be performed on-line. As a result, the approach partly uses heuristics where exhaustive checks would be too inefficient. All detection facilities that will be described have been implemented in a table-based development tool called . The use of this tool will be briefly illustrated. In addition, some experiences in verifying large knowledge bases are discussed.     

A modular and integratable approach to CIM

The factory of the future holds the promise of improved productivity and efficiency through the computerization of manufacturing applications. To fulfill this promise, computer technologies must provide superior results for each targeted manufacturing application. One approach to providing those superior results takes advantage of the natural evolution of manufacturing operations, a modular set of computeraided applications that can be integrated. Each module provides a “pocket of excellence” that can be integrated through networking as and when such communication becomes advantageous. Graphics workstation technology has provided dramatic improvements in performance, at price levels attractive to more segments of the industry than ever thought possible. Modular systems already exist that take advantage of this technological explosion through application software tailored to specific manufacturing functions. Communications between these modular applications can be achieved by networking but only at the discretion of the user, not at the discretion of the vendor. This paper will discuss how industry can increase productivity and efficiency by adopting a modular computer-integrated manufacturing (CIM) approach that is practical.     

A metaheuristic approach for selecting a common platform for modular products based on product performance and manufacturing cost

A known strategy to implement Mass Customization is Product Modularization. To take advantage of the benefits of modularity, the selection of a common platform is required. This selection must be done with optimization criteria based on functionality and economics. In this paper we propose metaheuristic procedures to solve the problem of selecting a common platform for a modular product. This selection is based on an aggregate objective function that combines product performance and manufacturing cost. The problem is divided into two hierarchical problems that must be solved sequentially. The mathematical models have a non-linear integer formulation. Because of the computational complexity to solve optimally these models, metaheuristic procedures are proposed to solve each sub-problem. These procedures are based on Scatter Search and Tabu Search. A case study is presented with a small instance that is solved with these procedures and by total enumeration. The results of the metaheuristic procedures coincide with the optimal values found by total enumeration. The run times are reasonable and it is expected a greater benefit for a larger instance with similar results quality.     

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.     

A novel modular Q-learning architecture to improve performance under incomplete learning in a grid soccer game

Multi-agent reinforcement learning methods suffer from several deficiencies that are rooted in the large state space of multi-agent environments. This paper tackles two deficiencies of multi-agent reinforcement learning methods: their slow learning rate, and low quality decision-making in early stages of learning. The proposed methods are applied in a grid-world soccer game. In the proposed approach, modular reinforcement learning is applied to reduce the state space of the learning agents from exponential to linear in terms of the number of agents. The modular model proposed here includes two new modules, a partial-module and a single-module. These two new modules are effective for increasing the speed of learning in a soccer game. We also apply the instance-based learning concepts, to choose proper actions in states that are not experienced adequately during learning. The key idea is to use neighbouring states that have been explored sufficiently during the learning phase. The results of experiments in a grid-soccer game environment show that our proposed methods produce a higher average reward compared to the situation where the proposed method is not applied to the modular structure.     

A modular drinking philosophers algorithm

Summary A variant of the drinking philosophers algorithm of Chandy and Misra is described and proved correct in a modular way. The algorithm of Chandy and Misra is based on a particular dining philosophers algorithm and relies on certain properties of its implementation. The drinking philosophers algorithm presented in this paper is able to use an arbitrary dining philosophers algorithm as a subroutine; nothing about the implementation needs to be known, only that it solves the dining philosophers problem. An important advantage of this modularity is that by substituting a more time-efficient dining philosophers algorithm than the one used by Chandy and Misra, a drinking philosophers algorithm withO(1) worst-case waiting time is obtained, whereas the drinking philosophers algorithm of Chandy and Misra hasO(n) worst-case waiting time (forn philosophers). Careful definitions are given to distinguish the drinking and dining philosophers problems and to specify varying degrees of concurrency. Jennifer L. Welch received her B.A. in 1979 from the University of Texas at Austin, and her S.M. and Ph.D. from the Massachusetts Institute of Technology in 1984 and 1988 respectively. She has been a member of technical staff at GTE Laboratories Incorporated in Waltham, Massachusetts and an assistant professor at the University of North Carolina at Chapel Hill. She is currently an assistant professor at Texas A&M University. Her research interests include algorithms and lower bounds for distributed computing.Much of this work was performed while this author was at the Laboratory for Computer Science, Massachusetts Institute of Technology, supported by the Advanced Research Projects Agency of the Department of Defense under contract N00014-83-K-0125, the National Science Foundation under grants DCR-83-02391 and CCR-86-11442, the Office of Army Research under contract DAAG29-84-K-0058, and the Office of Naval Research under contract N00014-85-K-0168. This author was also supported in part by NSF grant CCR-9010730, an IBM Faculty Development Award, and NSF Presidential Young Investigator Award CCR-9158478This author was supported by the Office of Naval Research under contract N00014-91-J-1046, the Advanced Research Projects Agency of the Department of Defense under contract N00014-89-J-1988, and the National Science Foundation under grant CCR-89-15206. The photograph and autobiography of Professor N.A. Lynch were published in Volume 6, No. 2, 1992 on page 121     

A modular neural network approach to fault diagnosis

Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation.