Design of flexible transfer lines: A case-based reconfiguration cost assessment

Shortening product life cycles leads competing companies to continually release new products or modifying the existing ones; this is one of the major issues in production systems design. According to the changes in product features, production systems need to be properly configured or reconfigured to efficiently tackle new production requirements. A configuration approach for multi-product flexible transfer lines is presented which aims at minimizing the equipment cost. The approach is applied to a manufacturing case in the automotive sector. The robustness of the solution is tested against changes that affect the products to highlight reconfiguration costs and the penalty associated to the lack of a pro-active configuration approach.     

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. exact clones, can be identified and factored out as shared subprocesses, the repository's maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.     

A network flow based heuristic approach for optimising AGV movements

Automated Guided Vehicles (AGVs) are driverless carriers that automatically navigate along planned paths by means of several guidance and control methods. This paper proposes an approach for solving the dispatching problem in an AGV system. The problem is modelled through a network by relying on the formulation of a Minimum Cost Flow Problem. In the defined graph, the nodes represent transportation tasks and AGVs while the arcs consider, through the associated weights, several system’s aspects such as pick, drop, and travel times, battery recharging, capacity constraints, congestion and error issues. Two objectives can be achieved: (i) minimisation of the average time for carrying out transportation tasks or (ii) maximisation of the utilisation degree of AGVs. The modelling and solution approach adopted has provided a novel Vehicle–Initiated dispatching rule and parameters settings for the dynamic assignments of transportation missions to AGVs. The decision making process concurrently and dynamically considers several factors. The results show a relevant reduction in the average time for transportation order fulfilment and a decrease in its variability. The proposed approach has been exploited for optimising the AGVs performance in a pharmaceutical production system.     

Identification of polynomial input/output recursive models with simulation error minimisation methods

Polynomial input/output (I/O) recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature, which deal with both model selection and parameter estimation. Previous works have shown the limitations of the classical prediction error minimisation approach in this context, especially (but not only) when the disturbance contribution is unknown, and suggested the use of a simulation error minimisation (SEM) approach for a better selection of the I/O model. This article goes a step further by integrating the model selection procedure with a simulation-oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can sometimes achieve great performance improvements with respect to previously proposed approaches. Two different parameter estimation algorithms are suggested, namely a direct SEM optimisation algorithm, and an approximate method based on multi-step prediction iteration, which displays several convenient properties from the computational point of view. Several simulation examples are shown to demonstrate the effectiveness of the suggested SEM approaches.     

Real-time risk monitoring in business processes: A sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.     

An iterative algorithm for simulation error based identification of polynomial input–output models using multi-step prediction

Effective identification of polynomial input–output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input–output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.     

Potential of diffuse scatterer interferometry for monitoring CO2 storage sites in European contexts (land cover types)

This article proposes to test the feasibility of long-term surface deformation monitoring based on synthetic aperture radar (SAR) interferometry on carbon dioxide (CO₂) storage sites with land cover representative of potential European injection sites (agricultural or forests with minimum built-up land cover). Because no operational injection site is currently active in Europe, a SAR data set (based on EnviSAT–ASAR spaceborne data) is simulated by combining SAR scenes acquired over a potential future European injection site with deformation measurements from SAR analysis carried out on the In-Salah (Algeria) CO₂ injection demonstrator site. The study shows that under such conditions, both persistent scatterer interferometry (PSI) and diffuse scatterer (DS) interferometry appear insufficient to provide a sufficiently dense measurement network to characterize surface deformation correctly. Alternative solutions, to be investigated in further studies, include the use of data archives with shorter acquisition time spans (e.g. Sentinel-1 data when available) or installation of corner reflectors. The cost of the latter mixed space/ground solution must be evaluated with respect to conventional ground-based measurement methods in the proposed context.     

Thermoelectric Properties of TiNiSn Half Heusler Alloy Obtained by Rapid Solidification and Sintering

Thermoelectric TiNiSn-based half Heusler compound reveals high Seebeck coefficient and electrical conductivity, leading to high power factor. As a consequence of the solidification path, TiNiSn single phase cannot be obtained directly from the liquid phase. Thus, a long annealing step is needed to homogenize the alloy. In this work, we present a new processing route for half Heusler compound formation, combining arc melting of pure elements, rapid solidification of the molten alloy and sintering. Rapid solidification of the molten alloy allows to obtain almost single TiNiSn phase, limiting the formation of the primary TiNi₂Sn phase as a consequence of the deep undercooling of the liquid. The rapidly solidified alloy was ground to powder and sintered by open die pressing. As-sintered samples show a density around 95% of the theoretical value. Thermal cycling of the sintered samples shows evolution of the phases, suggesting that after sintering the sample is not in equilibrium yet. After the second thermal cycle thermoelectric properties become reproducible, indicating the attainment of the equilibrium. In conclusion, the proposed processing route allows to obtain dense TiNiSn in bulk form avoiding the time-consuming annealing step, typically used to homogenize this alloy after solidification.