Giulia Pedrielli

An Ontology-based Framework for Sustainable Factories

ABSTRACTThe problem of factory sustainability is commonly addressed by focusing on specific aspects related to products, processes or production resources, while the impact of the building and facilities is usually neglected even though it counts for 40% of the total worlds energy consumption. This paper presents a holistic framework based on an integrated collaborative virtual environment that facilitates the sharing of the complete factory information and knowledge between various software tools, supporting the sustainable design and management of all the factory entities. In particular, the attention is focused on the Semantic Data Model that provides a semantic representation of the data and knowledge required for sustainability assessment.

Integrated Simulation/Optimization of Pull Control Systems

Pull policies are considered to be among the most efficient control strategy. Setting the correct parameters to maximise their efficiency is, however, not a trivial task. Simulation–optimisation techniques have received particular attention as a means to solve this problem. Nevertheless, they require the iterative solution of an optimisation model to generate the parameter values and a discrete event simulator to evaluate the resulting system performance. In the framework of simulation-optimisation, this paper proposes a combined solution of the optimisation and simulation problems for the optimal operation of pull control systems under several control strategies. Numerical experiments were performed to evaluate the performance of the proposed technique.

An HLA-based distributed simulation for networked manufacturing systems analysis

Manufacturing systems can be thought as production networks nodes whose relations have a strong impact on design and analysis of each system. Commercial simulators are already adopted to analyse complex networked systems, but the development of a monolithic model can be too complex or infeasible when a detailed description of the nodes is not available outside the ‘owner’ of the node. Then the problem can be decomposed modelling complex systems with various simulators that interoperate in a synchronized manner. Herein, the integration of simulators is addressed by taking as a reference the High Level Architecture (HLA). This paper proposes modifications to Commercial-off-the-shelf Simulation Package Interoperability Product Development Group protocols and to use patterns of how HLA can be effectively adopted to support Commercial Simulation Package interoperability: a new solution for the synchronous entity passing problem and modifications to the Entity Transfer Specification are presented. The resulting infrastructure is validated and tested over an industrial case.

Mathematical programming models for joint simulation–optimization applied to closed queueing networks

The optimization of stochastic Discrete Event Systems (DESs) is a critical and difficult task. The search for the optimal system configuration (optimization problem) requires the assessment of the system performance (simulation problem), resulting in a simulation–optimization problem. In the past ten years, a noticeable research effort has been devoted to this area. Recently, mathematical programming has been proposed to integrate simulation and optimization for multi-stage open queueing networks. This paper proposes the application of this approach to closed queueing networks. In particular, the optimal pallet allocation problem is tackled through linear mathematical programming models for simulation–optimization.

Event relationship graph lite: event based modeling for simulation-optimization of control policies in discrete event systems

Simulation-optimization has received a spectacular attention in the past decade. However, the theory still cannot meet the requirements from practice. Decision makers ask for methods solving a variety of problems with diverse aggregations and objectives. To answer these needs, the interchange of solution procedures becomes a key requirement as well as the development of (1) general modeling methodologies able to represent, extend and modify simulation-optimization as a unique problem, (2) mapping procedures between formalisms to enable the use of different tools. However, no formalism treats simulation-optimization as an integrated problem. This work aims at partially filling this gap by proposing a formalism based upon Event Relationship Graphs (ERGs) to represent the system dynamics, the problem decision variables and the constraints. The formalism can be adopted for simulation-optimization of control policies governing a queueing network. The optimization of a Kanban Control System is proposed to show the whole approach and its potential benefits.

Optimal Computing Budget Allocation to Select the Nondominated Systems—A Large Deviations Perspective

We consider the optimal computing budget allocation problem to select the nondominated systems on finite sets under a stochastic multi-objective ranking and selection setting. This problem has been addressed in the settings of correct selection guarantee when all the systems have normally distributed objectives with no correlation within and between solutions. We revisit this problem from a large deviation perspective and present a mathematically robust formulation that maximizes the lower bound of the rate function of the probability of false selection (

Discrete event optimization: single--run integrated simulation--optimization using mathematical programming

P(\text{FS})

The object-oriented discrete event simulation modeling: a case study on aircraft spare part management

) defined as the probability of not identifying the true Pareto set. The proposed formulation allows general distributions and explicitly characterizes the sampling correlations across performance measures. Three budget allocation strategies are proposed. One of the approaches is guaranteed to attain the global optimum of the lower bound of the rate function but has high computational cost. Therefore, a heuristic to approximate the global optimal strategy is proposed to save computational resources. Finally, for the case of normally distributed objectives, a computationally efficient procedure is proposed, which adopts an iterative algorithm to find the optimal budget allocation. Numerical experiments illustrate the significant improvements of the proposed strategies over others in the existing literature in terms of the rate function of

Multi-objective multi-fidelity optimization with ordinal transformation and optimal sampling

P(\text{FS})

Design and control of manufacturing systems: a discrete event optimisation methodology

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