Leonardo Chwif

On simulation model complexity

Nowadays the size and complexity of models is growing more and more, forcing modelers to face some problems that they were not accustomed to. Before trying to study ways to deal with complex models, a more important and primary question to explore is, is there any means to avoid the generation of complex models? The primary purpose of this paper is to discuss several issues regarding the complexity of simulation models, summarizing the findings in this area so far, and calling attention to this area that, despite its importance, appears to remain at the bottom of simulation research agendas.

A solution to the facility layout problem using simulated annealing

In this paper a solution in the continual plane to the Facility Layout Problem (FLP) is presented. It is based on Simulated Annealing (SA), a relatively recent algorithm for solving hard combinatorial optimization problems, like FLP. This approach may be applied either in General Facility Layout Problems (GFLP) considering facilities areas, shapes and orientations or in Machine Layout problems (MLP) considering machines pick-up and drop-off points. It has been applied to real-life situations with useful results, indicating the effectiveness of this approach.

Supply chain analysis: spreadsheet or simulation?

In the last few decades, a lot of company effort has been spent in the optimization of internal efficiency, aiming at cost reduction and competitiveness. Especially over the last decade, there has been a consensus that not only the company, but the whole supply chain in which it fits, is responsible for the success or failure of any business. Therefore, supply chain analysis tools and methodologies have become more and more important. From all tools, spreadsheets are by far the most widely used technique for scenario analysis. Other techniques such as optimization, simulation or both (simulation-optimization) are alternatives for in-depth analysis. While spreadsheet-based analysis is mainly a static-deterministic approach, simulation is a dynamic-stochastic tool. The purpose of this paper is to compare spreadsheet-based and simulation-based tools showing the impacts of using these two different approaches on the analysis of a real (yet simplified) supply chain case study.

Discrete event simulation model reduction: A causal approach

Abstract Discrete event simulation is an important system analysis technique. But for today’s demand for speed, the time to complete a simulation study is considered to be long, even with current developments in hardware and simulation software. In this scenario, simplification methods for simulation models could play a key role. This paper proposes a technique for reducing the complexity of a discrete event simulation model at the conceptual phase of simulation modeling that can be fully automatized through a computer program. We applied this technique on some problems which demonstrate the feasibility of this approach.

Warnings about simulation

Discrete-event simulation modelling is a powerful systems analysis tool. However, in practice, several mistakes can compromise a simulation study that might lead the decision maker to the wrong conclusion. Based on our review of the literature on related topics, and our experience in applying simulation, we have compiled some ‘warnings’ for the user community. These warnings are grouped into seven categories as follows: Data Collection, Model Building, Verification and Validation, Analysis, Simulation Graphics, Managing the Simulation Process, and Human Factors, Knowledge, and Abilities.

Simulation optimization with the linear move and exchange move optimization algorithm

The linear move and exchange move optimization (LEG) is an algorithm based on a simulated annealing algorithm (SA), a relatively recent algorithm for solving hard combinatorial optimization problems. The LEO algorithm was successfully applied to a facility layout problem, a scheduling problem and a line balancing problem. We try to apply the LEO algorithm to the problem of optimizing a manufacturing simulation model, based on a steelworks plant. This paper also demonstrates the effectiveness and versatility of this algorithm. We compare the search effort of this algorithm with a genetic algorithm (GA) implementation of the same problem.

Metodology for selecting the best suitable bottleneck detection method

Focusing on process constraints (or bottlenecks) is how companies are improving productivity, decreasing response times. However, a bottleneck is not easily detectable, especially when conventional bottleneck methods are used. This work presents a method, based on simulation, to help the selection of the bottleneck detection method to be applied to a given situation. The methodology extends previous works on the subject, mainly those by Roser, Nakano and Tanaka (2002) and Roser, Nakano and Tanaka (2003). The proposed method was successfully applied to a real bottling process.

A framework for specifying a discrete-event simulation conceptual model

Although conceptual modelling is one of the most significant steps in the modelling process for discrete-event simulation, this step deserves greater attention in the conduct of practical projects. There are many advantages in developing a conceptual model such as less rework, the availability of documentation for post-simulation study, revision, auditing, and the possibility that someone different than the modellers will conduct the model implementation. The conceptual modelling product can be expressed in different ways depending on the modelling framework adopted. This article extends the modelling framework of Robinson. Some of the extensions addressed include data requirements in 5W1H (What? When? Where? Why? Who? How?) format, revision table (in order to register the changes in the conceptual model document), complexity description (that specifies the level of detail and scope of the model), and other features (such as input/output definitions, process description and so on). At the end of this article, we provide a real case taken from a multinational logistics company using our framework.

Model reduction: some results

Simulation is becoming a popular tool, aided by the development of powerful and faster computers, new features like parallel and distributed simulation and easier simulation software that allows to reduce the time to implement a model. In spite of that, the cycle time of a simulation study is still long. The paper presents some remarks on model reduction reasoning and how, through this, the time of a simulation study could be reduced. The discussion is based on the example of Kienbaum and Paul (1994) of the pub problem, using a simplification of their H-ACD representation to describe the original and reduced models. The models are implemented and the results obtained shows their statistical equivalence which suggests the feasibility of this approach.

Estimating the implementation time for discrete-event simulation model building

There are several techniques for estimating cost and time for software development. These are known in software engineering as “software metrics.” LOC (lines of code), COCOMO (COnstructive COst Model), and FPA (Function Point Analysis) are examples of such techniques. Although Discrete Event Simulation Modeling (DESM) has some differences from classical software development, it is possible to draw a parallel between these techniques and DESM. This article reviews some of the metrics from software engineering, and, based on those, proposes a metric for estimating time for the implementation of a simulation model using one specific simulation software. The results obtained for 22 real simulation projects showed that the proposed technique can estimate the time for software development with acceptable accuracy (average error of 6% and maximum absolute error of 38%) for models that have less that 200 simulation objects.