Marcos Ribeiro Pereira Barretto

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.

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.

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.

OPC and CORBA in manufacturing execution systems: a review

While MES (manufacturing execution systems) are becoming increasingly more popular in the factory environment, the intrinsic hardware heterogeneity of equipment suppliers has always been a problem to their easy and cost-effective implementation. New distributed objects technologies now exist, both open and proprietary, operating system-dependent and independent, that can be used to solve this heterogeneity related problem. This work is intended to review the technologies of MES and new distributed objects, making a comparison of specifications, standards and commercial MES products based on each one. First, this work shows a general overview of MES and the potential of distributed objects applied to them: i.e. new concepts as intelligent sensors and other. Then, the two leading distributed object technologies will be reviewed: OPC/DCOM and CORBA. Finally, an industry survey focusing on commercial MES products and the distributed objects technology used by each one will be shown.

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.

A heuristic approach to stowing general cargo into platform supply vessels

This paper addresses a practical problem encountered in the oil industry, related to the supplying of general cargo to offshore rigs and production units. For a given route assigned to a supply vessel we seek to determine the optimal two-dimensional positioning of deck cargoes such that the overall profit is maximized, while ensuring that several safety and operational constraints are respected. In terms of mathematical modelling, the resulting problem can be seen as a rich variation of the two-dimensional knapsack problem, since some cargoes may wait for a later trip. Furthermore, given that the trip may serve many offshore units and that a substantial number of items must also return from these units, the problem becomes even more complex and can be viewed as a pickup and delivery allocation problem. We propose a probabilistic constructive procedure combined with a local search heuristic to solve this problem. We also report the results of computational experiments with randomly generated instances. These results evidence that our proposed heuristic can effectively help ship planners when dealing with such large-scale allocation problems, with many operational constraints.

Simulation CT-scan: a marketing perspective

There is a general understanding in the simulation community that simulation is not reaching its full potential and is not widely used despite its well-known (among simulationists) benefits. This papers primary intent is to discuss this belief, applying a marketing model whereby simulation is viewed as a product.

A heuristic approach to stowing general cargo platform supply vessels

This paper addresses a practical problem encountered in the oil industry, related to the supplying of general cargo to offshore rigs and production units. For a given route assigned to a supply vessel we seek to determine the optimal two-dimensional positioning of deck cargoes such that the overall profit is maximized, while ensuring that several safety and operational constraints are respected. In terms of mathematical modelling, the resulting problem can be seen as a rich variation of the two-dimensional knapsack problem, since some cargoes may wait for a later trip. Furthermore, given that the trip may serve many offshore units and that a substantial number of items must also return from these units, the problem becomes even more complex and can be viewed as a pickup and delivery allocation problem. We propose a probabilistic constructive procedure combined with a local search heuristic to solve this problem. We also report the results of computational experiments with randomly generated instances. These results evidence that our proposed heuristic can effectively help ship planners when dealing with such large-scale allocation problems, with many operational constraints.