Staged Models for Interdisciplinary Research

Modellers of complex biological or social systems are often faced with an invidious choice: to use simple models with few mechanisms that can be fully analysed, or to construct complicated models that include all the features which are thought relevant. The former ensures rigour, the latter relevance. We discuss a method that combines these two approaches, beginning with a complex model and then modelling the complicated model with simpler models. The resulting "chain" of models ensures some rigour and relevance. We illustrate this process on a complex model of voting intentions, constructing a reduced model which agrees well with the predictions of the full model. Experiments with variations of the simpler model yield additional insights which are hidden by the complexity of the full model. This approach facilitated collaboration between social scientists and physicists- The complex model was specified based on the social science literature, and the simpler model constrained to agree (in core aspects) with the complicated model

CART for supply chain simulation models reduction

Part 2: Case StudiesInternational audienceEvaluation of supply chain or workshop management is often based on simulation. This simulation task needs models which are difficult to design. The aim of this work is to reduce the complexity of simulation model design and to partially automate this task by combining discrete and continuous approaches in order to construct more efficient and reduced model. Model design focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop is modeled in a less precise way by using continuous model in order to describe only how the bottlenecks are fed. This used continuous model is a regression tree algorithm. For validation, this approach is applied to the modeling of a sawmill workshop and the results are compared with results obtained previously by using a neural network model

Reduction of Computational Load in Robust Facility Layout Planning Considering Temporal Production Efficiency

Part 3: Production Management Theory and MethodologyInternational audienceMost researches of facility layout planning (FLP) have aimed at finding a layout with which evaluation indices based on distance are minimized. Because temporal efficiency has not been considered in this stage but in post stages, the resultant temporal efficiency may not be optimal enough. The authors have developed an FLP method considering temporal efficiency, in which facility layout is optimized using genetic algorithm (GA), and have enhanced it so that robustness against changes in production environment can be taken into consideration. However, the enhanced method involves a large computational load, since numerous production scenarios need to be considered. This paper provides a method for reducing computational load in the robust FLP based on the sampling approach where each layout plan is evaluated with only a limited number of production scenarios in the optimization process by GA. Numerical experiments showed the potential of the proposed method to efficient robust FLP considering temporal efficiency

A PRESCRIPTIVE TECHNIQUE FOR V&V OF SIMULATION MODELS WHEN NO REAL-LIFE DATA ARE AVAIABLE

Verification and Validation (V&V) is a key process to guarantee that any model represents adequately a given system. Although no one can guarantee a 100 % valid model, it is possible to increase model confidence by the utilization of V&V techniques. There are many V&V techniques which have a descriptive nature (they tell us what to do but not how to do it). There are also prescriptive techniques, that tell us how to do it, but in simulation practice they are underused. The main goal of this paper is based on Kleijnen (1999) procedure. It is to propose a prescriptive V&V technique that is simple enough for practical application and, because of its procedural nature, it could be easily built into any simulation software, thus enabling the automation of the V&V process. This approach was also applied to some test problems confirming its feasibility.

Software process simulation modelling: A survey of practice

In recent years, simulation modelling of software development processes has attracted considerable interest in software engineering. Despite the growing interest, there is little literature available that reports on the state-of-practice in software process simulation modelling (SPSM). We report results of a survey of simulation in SPSM and relate it to simulation practice in general. The results of this survey indicate that software process simulation (SPS) modellers are generally methodical, work on large complex problems, develop large models, and have a systematic simulation modelling process in place. However, on the other hand, the simulation modelling process and simulation model evaluation have been identified as the most urgent problems to be addressed in SPSM. The results from this investigation are interesting and bring many problems into focus. The paper helps understand the characteristics of the SPSM and SPS modellers, and highlights areas of interest for further in-depth research in the SPSM

A practical guide for operational validation of discrete simulation models

As the number of simulation experiments increases, the necessity for validation and verification of these models demands special attention on the part of the simulation practitioners. By analyzing the current scientific literature, it is observed that the operational validation description presented in many papers does not agree on the importance designated to this process and about its applied techniques, subjective or objective. With the expectation of orienting professionals, researchers and students in simulation, this article aims to elaborate a practical guide through the compilation of statistical techniques in the operational validation of discrete simulation models. Finally, the guide's applicability was evaluated by using two study objects, which represent two manufacturing cells, one from the automobile industry and the other from a Brazilian tech company. For each application, the guide identified distinct steps, due to the different aspects that characterize the analyzed distribution

A neural network for the reduction of a product-driven system emulation model

International audienceIn new Intelligent Manufacturing Systems, Product Driven Systems (PDS) architectures require emulation tool (Thomas et al. 2008) to be developed. Discrete events simulation is often used to build such emulation tool, nevertheless this remains complex because of large scale problems. The goal of this paper is to propose a way to design a simulation model by reducing its complexity. According to theory of constraints, we build reduced models composed exclusively of bottlenecks and a neural network. In Particular, a multilayer perceptron is used. The structure of the network is determined by using a pruning procedure. This work highlights the impact of discrete data on the computational results. An application to a sawmill internal supply chain is suggested to validate the proposed approach