Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

Smart manufacturing scheduling: A literature review

[EN] Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart manufacturing scheduling: A literature review. Journal of Manufacturing Systems. 61:265-287. https://doi.org/10.1016/j.jmsy.2021.09.0112652876

Dynamic scheduling model for the construction industry

Purpose:Basic project control through traditional methods is not sufficient to manage the majority of realtime events in most construction projects. This paper proposes a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cashflow, throughout project construction.Design/methodology/approach:Upon reviewing the topic of Dynamic Scheduling, a worldwide Internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.Findings:The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use, and software integration with contemporary project management practices.Practical/Social implications:Optimized real-time scheduling can provide better resources management including labour utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Originality/value:The paper illustrates the importance of DS in construction, identifies the user needs, and overviews the development, verification and validation of a model that supports the generation of high quality schedules beneficial to large scale projects.</div

Food supply chain network robustness : a literature review and research agenda

Today’s business environment is characterized by challenges of strong global competition where companies tend to achieve leanness and maximum responsiveness. However, lean supply chain networks (SCNs) become more vulnerable to all kind of disruptions. Food SCNs have to become robust, i.e. they should be able to continue to function in the event of disruption as well as in normal business environment. Current literature provides no explicit clarification related to robustness issue in food SCN context. This paper explores the meaning of SCN robustness and highlights further research direction

The economic consequences of water utility disruptions

This research produces an economic impact analysis of short duration water utility disruptions to evaluate the consequences of alternative restoration decisions. The study constructs a continuous dynamic disequilibrium demand driven social accounting matrix with supply constraints that incorporates short-run resilience and other strategies employed by businesses, government institutions and households. It is constructed using the IMPLAN database and survey responses of recent water disruption events. The utility of the model is demonstrated by simulating three alternative water service restoration schemes of hypothetical water outages. The results demonstrate that different restoration strategies produce different total output and value added losses. It also shows that, in addition to total valued added losses, time costs, and the additional losses of households and government institutions are important components of total losses and should be considered when comparing restoration strategies. Finally, it highlights the importance of resilience in reducing the overall economic consequences of disruptions. It is expected that this model will help policy makers assess post-alternative recovery and restoration strategies when this type of event occurs. The model can also be used to identify the most critical industries when evaluating precautionary measures and mitigation strategies in order to minimize economic losses.Includes bibliographical references (pages 155-174

Optimization Models and Approximate Algorithms for the Aerial Refueling Scheduling and Rescheduling Problems

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for fighter aircrafts (jobs) on multiple tankers (machines) to minimize the total weighted tardiness. ARSP can be modeled as a parallel machine scheduling with release times and due date-to-deadline window. ARSP assumes that the jobs have different release times, due dates, and due date-to-deadline windows between the refueling due date and a deadline to return without refueling. The Aerial Refueling Rescheduling Problem (ARRP), on the other hand, can be defined as updating the existing AR schedule after being disrupted by job related events including the arrival of new aircrafts, departure of an existing aircrafts, and changes in aircraft priorities. ARRP is formulated as a multiobjective optimization problem by minimizing the total weighted tardiness (schedule quality) and schedule instability. Both ARSP and ARRP are formulated as mixed integer programming models. The objective function in ARSP is a piecewise tardiness cost that takes into account due date-to-deadline windows and job priorities. Since ARSP is NP-hard, four approximate algorithms are proposed to obtain solutions in reasonable computational times, namely (1) apparent piecewise tardiness cost with release time rule (APTCR), (2) simulated annealing starting from random solution (SArandom ), (3) SA improving the initial solution constructed by APTCR (SAAPTCR), and (4) Metaheuristic for Randomized Priority Search (MetaRaPS). Additionally, five regeneration and partial repair algorithms (MetaRE, BestINSERT, SEPRE, LSHIFT, and SHUFFLE) were developed for ARRP to update instantly the current schedule at the disruption time. The proposed heuristic algorithms are tested in terms of solution quality and CPU time through computational experiments with randomly generated data to represent AR operations and disruptions. Effectiveness of the scheduling and rescheduling algorithms are compared to optimal solutions for problems with up to 12 jobs and to each other for larger problems with up to 60 jobs. The results show that, APTCR is more likely to outperform SArandom especially when the problem size increases, although it has significantly worse performance than SA in terms of deviation from optimal solution for small size problems. Moreover CPU time performance of APTCR is significantly better than SA in both cases. MetaRaPS is more likely to outperform SAAPTCR in terms of average error from optimal solutions for both small and large size problems. Results for small size problems show that MetaRaPS algorithm is more robust compared to SAAPTCR. However, CPU time performance of SA is significantly better than MetaRaPS in both cases. ARRP experiments were conducted with various values of objective weighting factor for extended analysis. In the job arrival case, MetaRE and BestINSERT have significantly performed better than SEPRE in terms of average relative error for small size problems. In the case of job priority disruption, there is no significant difference between MetaRE, BestINSERT, and SHUFFLE algorithms. MetaRE has significantly performed better than LSHIFT to repair job departure disruptions and significantly superior to the BestINSERT algorithm in terms of both relative error and computational time for large size problems

Automatische Generierung eines Simulationsmodells zur Unterstützung der Umplanung einer Baustellenmontage

Fixed-Layout Assembly (FLA) systems are used to assemble large and bulky products. These products are often unique and require customer-specific engineering and customization. FLA systems are frequently prone to disturbances and plan deviations throughout operations: delayed deliveries, incompatibility or failures of equipment, and unplanned absences of operators. Planners therefore need a simple and efficient tool to quickly forecast the impact of changes on the whole assembly system. A solution concept has been presented by the authors in a previous publication (Billiet and Stark, 2022). The authors presented a method to automatically generate a simulation model by using data concerning the products, orders and shifts from the ERP system. This paper describes the implementation of the previously presented solution concept by applying it to a FLA for the production of Large Motors and Converters (LMC) in Berlin