A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

The Peer’s Dilemma: A general framework to examine cooperation in pure peer-to-peer systems

The exploration of social dilemmas is being considered a major foundation for encountering the enforced necessities of cooperation in self-organizing environments. Such environments are characterized by self-interested parties and the absence of trusted third parties. Recent approaches apply evolutionary socio-inspired games to formally prove the existence and further prolongation of cooperation patterns within communities. For instance, the Prisoner’s Dilemma game has thus provided a rich opportunity to examine self-interested behaviors in pure peer-to-peer networks. However, assuming a total absence of coalitions, incentives and punishment mechanisms, several works argue against a durable maintenance of cooperation neither at single-shot nor repeated-scenarios. In this article, we formally and experimentally demonstrate a counterexample for the latter by applying evolutionary game theory and a particular instance of the Rock–Scissors–Paper game. Our framework proves that the cyclic dominance of certain type of nodes within a P2P system has an impact and introduces a strategic aspect to the evolution of the overall community.     

Secure authentication scheme for passive C1G2 RFID tags

Privacy and security concerns inhibit the fast adaption of RFID technology for many applications. A number of authentication protocols that address these concerns have been proposed but real-world solutions that are secure, maintain low communication cost and can be integrated into the ubiquitous EPCglobal Class 1 Generation 2 tag protocol (C1G2) are still needed and being investigated. We present a novel authentication protocol, which offers a high level of security through the combination of a random key scheme with a strong cryptography. The protocol is applicable to resource, power and computationally constraint platforms such as RFID tags. Our investigation shows that it can provide mutual authentication, untraceability, forward and backward security as well as resistance to replay, denial-ofth-service and man-in-the-middle attacks, while retaining a competitive communication cost. The protocol has been integrated into the EPCglobal C1G2 tag protocol, which assures low implementation cost. We also present a successful implementation of our protocol on real-world components such as the INTEL WISP UHF RFID tag and a C1G2 compliant reader.     

Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design

Design of optimal plans for environmental planning and management applications should ideally consider the multiple quantitative and qualitative criteria relevant to the problem. For example, in ground water monitoring design problems, qualitative criteria such as acceptable spatial extent and shape of the contaminant plume predicted from the monitored locations can be equally important as the typical quantitative criteria such as economic costs and contaminant prediction accuracy. Incorporation of qualitative criteria in the problem-solving process is typically done in one of two ways: (a) quantifying approximate representations of the qualitative criteria, which are then used as additional criteria during the optimization process, or (b) post-optimization analysis of designs by experts to evaluate the overall performance of the optimized designs with respect to the qualitative criteria. These approaches, however, may not adequately represent all of the relevant qualitative information that affect a human expert involved in design (e.g. engineers, stakeholders, regulators, etc.), and do not necessarily incorporate the effect of the expert's own learning process on the suitability of the final design. The Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) is a novel approach that addresses these limitations by using a collaborative human-computer search strategy to assist users in designing optimized solutions to their applications, while also learning about their problem. The algorithm adaptively learns from the expert's feedback, and explores multiple designs that meet her/his criteria using both the human expert and a simulated model of the expert's responses in a collaborative fashion. The algorithm provides an introspection-based learning framework for the human expert and uses the human's subjective confidence measures to adjust the optimization search process to the transient learning process of the user. This paper presents the design and testing of this computational framework, and the benefits of using this approach for solving groundwater monitoring design problems.     

Automated synthesis of multiple analog circuits using evolutionary computation for redundancy-based fault-tolerance

Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. Compared to digital circuits, designing fault-tolerant analog circuits is a difficult and knowledge-intensive task. A simple but powerful method for robustness is a redundancy approach to use multiple circuits instead of single one. For example, if component failures occur, other redundant components can replace the functions of broken parts and the system can still work. However, there are several research issues to make the redundant system automatically. In this paper, we used evolutionary computation to generate multiple analog circuits automatically and then we combined the solutions to generate robust outputs. Evolutionary computation is a natural way to produce multiple redundant solutions because it is a population-based search. Experimental results on the evolution of the low-pass, high-pass and band-stop filters show that the combination of multiple evolved analog circuits produces results that are more robust than those of the best single circuit.     

Two new characteristic parameters for runoff computation

A method capable of estimating the hydrograph from a prescribed storm for a practical mild slope upstream catchment is proposed. This method makes use of two new characteristic parameters, andS, in conjunction with the kinematic wave equation to compute lateral inflows of the main stream of the catchment. The depth profile of overland flow at any instant within the catchment and hydrograph at any location can be easily found. Lag times for individual lateral inflows are then considered and are linearly combined to obtain the hydrograph at the outlet of the catchment or depth profile of the main stream at any instant. The validity of the excess rainfall-surface runoff linear relationship in this study has also been verified with Tatsunokuchiyama catchment, and it shows good results for this computed runoff.     

Some efficient solutions to the affine scheduling problem. Part II. Multidimensional time

This paper extends the algorithms which were developed in Part I to cases in which there is no affine schedule, i.e. to problems whose parallel complexity is polynomial but not linear. The natural generalization is to multidimensional schedules with lexicographic ordering as temporal succession. Multidimensional affine schedules, are, in a sense, equivalent to polynomial schedules, and are much easier to handle automatically. Furthermore, there is a strong connection between multidimensional schedules and loop nests, which allows one to prove that a static control program always has a multidimensional schedule. Roughly, a larger dimension indicates less parallelism. In the algorithm which is presented here, this dimension is computed dynamically, and is just sufficient for scheduling the source program. The algorithm lends itself to a divide and conquer strategy. The paper gives some experimental evidence for the applicability, performances and limitations of the algorithm.     

基于混沌量子进化算法的单交叉口信号控制

城市道路各交叉口交通信号的配时优化和协同控制直接影响整个城市的交通状况.本文以单交叉口模型的交通信号控制问题为背景,构造了以单交叉口滞留的车辆数最少为目标的优化模型.用混沌量子进化算法进行仿真数据求解,得到实时控制的配时方案,并与其它算法的仿真结果进行比较,结果表明该算法对单交叉口的信号配时优化是非常有效的.     

On problem transformability in VLSI

The two basic performance parameters that capture the complexity of any VLSI chip are the area of the chip,A, and the computation time,T. A systematic approach for establishing lower bounds onA is presented. This approach relatesA to the bisection flow, . A theory of problem transformation based on , which captures bothAT ² andA complexity, is developed. A fundamental problem, namely, element uniqueness, is chosen as a computational prototype. It is shown under general input/output protocol assumptions that any chip that decides ifn elements (each with (1+)lognbits) are unique must have =(nlogn), and thus, AT²=(n ²log² n), andA= (nlogn). A theory of VLSI transformability reveals the inherentAT ² andA complexity of a large class of related problems.This work was supported in part by the Semiconductor Research Corporation under contract RSCH 84-06-049-6.     

针对无网格并行计算的基于模糊均值聚类的分区方法*

针对国际上普遍采用的基于回归几何二分的分区方法的缺陷,提出了基于模糊均值聚类的均匀模糊均值聚类分区算法。采用该方法对不同类型的无网格几何模型进行了分区,并根据分区信息对三维实体模型进行了并行计算。通过与基于回归几何二分法的比较,充分验证该算法的有效性和可行性。