The large-scale management and monitoring of environment is a complex issue for administrators and public authorities responsible for the detection of emergencies. They need to rank all possible environment issues and then solve them. A Decision Support System can be used to simplify such operations, to perform better analysis and to provide more widespread coverage to data. This paper presents a Web Environmental Decision Support System designed to estimate environmental condition, which includes a Data Mining Module (DMM) that computes an Environmental Condition Index by processing historical and real time data and a Hazard Detection Module (HDM). The HDM comprises a mobile APP that allows users to report environmental events of emergency, hazards and waste, in order to help public authorities in managing and planning activities. The collected data are processed by the Web Environmental Decision Support System which assigns automatically an Environmental Emergency Index to every report. This approach allows monitoring of wide environmental areas letting communities be part of the “sensors system”.

This paper presents an integrated system (IS) devoted to solve the complex scheduling problems in steel-making and casting (SMC) plants. The presented IS is composed of four modules: data base, optimization, simulation modules, and the user interface. In particular, we develop the two main components of the IS: the optimization and simulation modules. The optimization module is based on a mixed-integer linear programming formulation with the objective of minimizing the makespan, i.e., the completion time of the last job. Moreover, a discrete-event simulation module is used to validate and assess the proposed schedules. By designing the IS for a real case study, we show how it can be applied off-line to schedule the daily operations of the SMC, as well as online in order to face unpredictable events such as failures and blocks of the machines. Note to Practitioners-This paper is motivated by the necessity of solving the complex scheduling problems in steel-making and casting (SMC) plants. To this aim, the paper presents an integrated system (IS) that is devoted to help decision makers in selecting the optimal schedules of the operations also in the cases in which unpredictable events may occur. The main modules of the presented IS are an optimization module, based on a mixed-integer linear programming model, and a simulation module, able to verify and validate the proposed schedules by what if analyses and stochastic input parameters. Because of the complexity of the problem, some approximations referring to the transportation system planning are considered. However, the results show the efficiency of the proposed scheduling approach for real industrial applications. Future research aims at investigating about more efficient mathematical programming models in order to solve larger problems involving the planning of one or more weeks and a more detailed description of the plant.

In this paper we deal with the problem of modeling and simulating the Steelmaking and Continuous Casting (SM-CC) processes. The SM-CC production processes are very complex and exhibit production cycles that have to be managed with suitable sequencing and scheduling procedures in order to minimize blocking and bottleneck conditions. The paper describes in details the SM-CC process by a modular approach in a Timed Petri Net framework. In order to show that the modeling procedure is suitable to describe large systems, we model and simulate a real case study involving a SM-CC plant located in the North of Italy.

This paper deals with the scheduling of operations in a Steelmaking and Continuous Casting (SM-CC) plant. In particular, we consider a real case study of an integrated plant located in the North of Italy. First, we describe in details the SM-CC process of the considered system by using the Unified Modeling Language. Second, we focus on the schedule of casts on the continuous casting machine that is subject to different types of setup operations. With the objective of minimizing the maximum completion time, the schedule of the continuous casting machine is modeled by a Mixed Integer Linear Programming formulation. An example enlightens how a proper scheduling of the casts is of basic importance to obtain good system performances.

We present a novel lean manufacturing procedure relying on the Value Stream Mapping (VSM) tool and the Analytic Hierarchy Process (AHP) technique. The procedure is iterative and hierarchical. Starting from a detailed description of the manufacturing process by the Unified Modeling Language (UML), the VSM graphical approach allows the identification of non-value adding activities, and the AHP technique leads to a ranking of such system anomalies. The further application of the VSM tool produces an overall picture of the desired manufacturing system, and the UML framework allows to describe in detail the updated system activities. An application of the procedure to a real case study shows its effectiveness.