Lucia Cassettari

Monte Carlo Simulation Models Evolving in Replicated Runs: A Methodology to Choose the Optimal Experimental Sample Size

The idea of a methodology capable of determining in a precise and practical way the optimal sample size came from studying Monte Carlo simulation models concerning financial problems, risk analysis, and supply chain forecasting. In these cases the number of extractions from the frequency distributions characterizing the model is inadequate or limited to just one, so it is necessary to replicate simulation runs many times in order to obtain a complete statistical description of the model variables. Generally, as shown in the literature, the sample size is fixed by the experimenter based on empirical assumptions without considering the impact on result accuracy in terms of tolerance interval. In this paper, the authors propose a methodology by means of which it is possible to graphically highlight the evolution of experimental error variance as a function of the sample size. Therefore, the experimenter can choose the best ratio between the experimental cost and the expected results.

Simulation as support for production planning in small and medium enterprise: a case study

The proposed application is related to an Italian small factory that produces, assembles, and sells mechanical components for awnings. In such factories BPR (business process engineering) usually takes place without the support of modeling & simulation although such methodologies have proved to be very effective and helpful. Main reasons for that have to be investigated in the high costs usually associated with a simulation study, especially for data collection, model building and model validation. In order to avoid this problem a general-purpose simulation framework was designed enabling self-build according to production process information stored in a relational database. Moreover, the simulation model was used in conjunction with a statistical analysis tool in order to build the relationship among selected parameters and the proposed objective function by mean of response surface methodology and 2nd order regressions meta-models. Authors applied the proposed schema to several industrial applications obtaining interesting results.

Improving healthcare using cognitive computing based software: an application in emergency situation

The goal of this paper is to define the platform specifications dealing with medical information sharing both from research viewpoint and in terms of local health care. The purpose of this research work is based on doctor - patient relationships: VDS - Virtual Medical Doctor System. At this stage the platform is only used for scientific purposes. In particular, we assume the integrated design of the platform based on different levels (layers), which may be interconnected through the information flows (or links). The first level (the lower) involves the construction of a VDS platform. Based on this system it is possible to foresee a number of extensions such as social network for scientific research design, and risk analysis tool.

A generalized simulation framework to manage logistics systems: a case study in waste management and environmental protection

This paper presents an innovative modeling framework able to support planning, management and optimization of waste collection operations in an urban context. A proprietary simulator composed by three functionality modules (Global Positioning System, Data Mining system, Simulator for routing and resource exploitation) was implemented by the authors, and was then validated on a specific set of case studies. This application has been made possible within PLANAGO regional government funded research project and was based on previous experiences of the authors. This approach was also extended beyond the particular application and is now under test in different application fields strictly related to logistics and environmental protection.

An Innovative Contribution to Health Technology Assessment

Healthcare is moving towards increased assistance needs with limited resources, both in economics terms, in personnel or space terms, leading to the usage of specific analysis for the acquisition, evaluation and assessment of medical technologies. The systematic evaluation of properties, effects or other impacts of a medical (or health) technology with a broad multidisciplinary approach is named Health Technology Assessment (HTA).This work presents an approach of a HTA protocol for the classification of hospitals or health facilities equipments, realized by combining the classic HTA concepts with hierarchic clustering techniques in a multidisciplinary analysis of requirements, cost, impact of logistics, technology associated risks.

Improving the Efficiency of a Hospital ED According to Lean Management Principles Through System Dynamics and Discrete Event Simulation Combined with Quantitative Methods

The Emergency Department of a Hospital has both exogenous and endogenous management problems. The first ones are about the relationship with the other Departments, for which the Emergency Department is a noise element on the planned activities, as it generates an unplanned beds occupation. The second ones strictly depend on the Department organizational model.

IVF cycle cost estimation using Activity Based Costing and Monte Carlo simulation

The Authors present a new methodological approach in stochastic regime to determine the actual costs of an healthcare process. The paper specifically shows the application of the methodology for the determination of the cost of an Assisted reproductive technology (ART) treatment in Italy. The reason of this research comes from the fact that deterministic regime is inadequate to implement an accurate estimate of the cost of this particular treatment. In fact the durations of the different activities involved are unfixed and described by means of frequency distributions. Hence the need to determine in addition to the mean value of the cost, the interval within which it is intended to vary with a known confidence level. Consequently the cost obtained for each type of cycle investigated (in vitro fertilization and embryo transfer with or without intracytoplasmic sperm injection), shows tolerance intervals around the mean value sufficiently restricted as to make the data obtained statistically robust and therefore usable also as reference for any benchmark with other Countries. It should be noted that under a methodological point of view the approach was rigorous. In fact it was used both the technique of Activity Based Costing for determining the cost of individual activities of the process both the Monte Carlo simulation, with control of experimental error, for the construction of the tolerance intervals on the final result.

A Multi-Stage Algorithm for a Capacitated Vehicle Routing Problem with Time Constraints

The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a given set of customers. Our attention is focused on a variant of VRP, the capacitated vehicle routing problem when applied to natural gas distribution networks. Managing natural gas distribution networks includes facing a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Despite the numerous papers available on vehicle routing problem, there are only a few that study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. Therefore, this work introduces a new algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem with time and distance constraints. This joint algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of a time window. Results from a real case study in a natural gas distribution network demonstrates the effectiveness of the approach.

An Innovative DSS for the Contingency Reserve Estimation in Stochastic Regime

The problem of sizing and managing contingency reserve is always critical in project management, because of its impact on the project margin. A correct assessment of the contingency reserve to be allocated is, therefore, a main requirement to lead to success the project manager actions. In this research, the Authors propose an innovative Decision Support System to size, starting from an objective phase of risk assessment, the correct contingency reserve. The proposed solution provides the project manager a clear vision of the residual risk of cost overruns to be managed. The Decision Support System uses Failure Mode Effect Analysis and Monte Carlo Simulation.

A new stochastic multi source approach to improve the accuracy of the sales forecasts

Purpose The aim of this paper is to suggest a new approach to the problem of sales forecasting for improving forecast accuracy. The proposed method is capable of combining, by means of appropriate weights, both the responses supplied by the best-performing conventional algorithms, which base their output on historical data, and the insights of company’s forecasters which should take account future events that are impossible to predict with traditional mathematical methods. Design/methodology/approach The authors propose a six-step methodology using multiple forecasting sources. Each of these forecasts, to consider the uncertainty of the variables involved, is expressed in the form of suitable probability density function. A proper use of the Monte Carlo Simulation allows obtaining the best fit among these different sources and to obtain a value of forecast accompanied by a probability of error known a priori. Findings The proposed approach allows the company’s demand forecasters to provide timely response to market dynamics and make a choice of weights, gradually ever more accurate, triggering a continuous process of forecast improvement. The application on a real business case proves the validity and the practical utilization of the methodology. Originality/value Forecast definition is normally entrusted to the company’s demand forecasters who often may radically modify the information suggested by the conventional prediction algorithms or, contrarily, can be too influenced by their output. This issue is the origin of the methodological approach proposed that aims to improve the forecast accuracy merging, with appropriate weights and taking into account the stochasticity involved, the outputs of sales forecast algorithms with the contributions of the company’s forecasters.