To solve the problem:
clingo maxbottomparallelism.asp (instance file) -c k=4
where the value of k is the number of devices nR.
Requires ECLiPSe
The following .zip file contains two MILP formulations (see included README file): MILP formulations for both problems
Requires Python and Gurobi.
When designing a water distribution network, one of the steps is designing the isolation system: in case a pipe has to be repaired (e.g., because of a break), a part of the network has to be disconnected from the rest of the network, in order to allow workers to fix the broken pipe. The isolation system consists of a set of isolation valves, that are placed in the pipes of the network. Once closed, the isolation valve blocks the flow of water through the valve itself.
The group developed algorithms to find the optimal placement of the isolation system, considering both the number of valves and the disruption for users:
Currently we are developing new hybrid methods that integrate answer set programming and metaheursistics in order to solve larger instances of the problem.
]]>Focusing on the BDMaaS+ engine, it consists of three main stages that work in a pipeline, namely, modeling, optimization, and decision making. At the modeling stage, Demand Model, Network Model, and Service Model are the three main components. They provide, respectively, the functions for: i) building the models of the service user service request arrival process (e.g., customers’ locations, distributions of service request inter-arrival times, etc.); ii) gathering and processing network measurements collected on-the-field by local measurement agents deployed at all private/public cloud datacenters to draw realistic network models; iii) and emulating IT service execution and deployment (e.g., service time distribution, current service component placement, etc.). These three modules are fed by their respective monitoring agent twins on the leftmost part of the figure that integrate with existing cloud platforms to gather monitoring information about the infrastructure (virtual resources) and applications component levels by updating the parameters of the service execution model.
The optimization stage consists of the Optimization macro-component and represents the core part of BDMaaS+. It is in charge of reenacting the Cloud computing IT service and of evaluating possible alternative service placement configurations over the hybrid cloud environment. First, the Service Placement Simulation component mimics possible service placements among those generated by the modeling stage. Then, the Business Impact Analysis component assigns an overall cost (namely, business impact) to each of these possible configurations.
At the third stage, the Decision Making component selects the best IT service placement configuration, namely, the one minimizing the business impact, according to the user preferences, current network conditions, and the output data provided by the Optimization component. Finally, BDMaaS+ was designed to be easily integrated with existing Cloud-based IT services through lightweight BDMaaS agents in-stalled at each data center. Each agent includes three relatively simple and implementation-specific “connector” components: Demand Monitoring, Service Monitoring, and Actuator, depicted in green in Fig. 1. Finally, the Actuator component is capable of automatically putting the new service configuration in place as required by the Decision Making component.
]]>
The delivery of high quality after-sales assistance in the ice cream making machines market is particularly challenging. Ice cream making machines are heavy-duty food processors and, as a result of the heavy operational stress, they require routine on-site maintenance and occasional major assistance interventions for failure recovery from highly qualified technical support personnel.
Unfortunately, in the ice cream making machines market the cost of on-site technical support is very high. In fact, ice cream making machines are often installed in remote areas, and technicians do not have always precise information about the machine location. The problems in gaining physical access to the ice cream machines significantly increase the average duration and cost of maintenance interventions for both Carpigiani and its customers. Because of the high revenue of ice cream making machines, it is of utmost importance to have timely detection of failures and prompt technical support interventions.
As a result, the process of maintenance and repairing interventions is inefficient, leading to high system downtimes. This calls for automated e-maintenance solutions to reduce costs while improving quality of after sales support. However, automating maintenance operations for small manufacturing systems deployed on customers’ premises, such as Carpigiani’s ice cream making machines, is a non trivial effort. In fact, these systems have unique characteristics for both monitoring and communications perspectives, such as limited access to machine status data and low cost constraints, that significantly differentiate them from energy industry and heavy industry applications where real-time e-maintenance systems are usually employed. This effectively prevents the adoption of Supervisory Control And Data Acquisition (SCADA) and/or Machine-to-Machine (M2M) telemetry platforms and calls for the development of ad hoc e-maintenance solutions.
To reduce the costs and improve the efficiency of after-sales assistance operations, Carpigiani launched the highly innovative Teorema project, that enables the integrated management of every aspect related to after-sales assistance. Leveraging on machineinstalled remote control kits and a centralized monitoring station, Teorema realizes a comprehensive after-sales e-maintenance platform that enables the remote assistance of ice cream making machines.
Publications
[1] R. Lazzarini, C. Stefanelli, M. Tortonesi, G. Virgilli, "Teorema: a Comprehensive Solution for the Remote Assistance of Ice Cream Making Machines", in Proceedings of 10th Annual International Conference on The Modern Information Technology in the Innovation Processes of the Industrial Enterprises (MITIP 2008), 14-17 November 2008, Prague, Czech Republic.
[2] M. Canato, R. Lazzarini, C. Stefanelli, M. Tortonesi, S. Veronese, G. Virgilli, "Teorema: An Integrated Solution for Effective After-sales Assistance in the Ice Cream Making Machines Market”, in Proceedings of 11th Annual International Conference on The Modern Information Technology in the Innovation Processes of the Industrial Enterprises (MITIP 2009), 15-16 October 2009, Bergamo, Italy.