Multicriteria Supplier Classification for DSS: Comparative Analysis of Two Methods

It has been widely demonstrated by many research works that the distribution of a factory can condition its productivity. Because of this, a factory in Santiago, Chile, asked the authors for advice to evaluate the current situation in the company and what alternatives could be proposed to improve performance by increasing productivity without incurring too high costs. Among the most important requirements requested was a study of the current design of the raw materials warehouse within the plant since this is a main pillar in the design of the plant. For this purpose, the current layout ws analyzed and alternative designs were proposed under two scenarios: use the same area that is currently available or make the design from scratch considering land purchases to build the warehouse. In order to give a precise answer to many possible design decisions, formulations were developed and deduced to calculate the optimal dimensions of the warehouse, and qualitative criteria were incorporated for decision making.

Countries located in the Pacific Ring of Fire, such as Chile, require robust evacuation plans for tall buildings to manage the ongoing threat of natural disasters. This study presents a methodology for developing evacuation plans by conducting pedestrian movement simulations with agents that have a model of their surroundings. This approach allows us to assess different scenarios and choose the best option based on the specific characteristics of the site. The method combines simulation and data analysis, using the Monte Carlo method to improve emergency evacuations. Initially, Pathfinder software was employed to simulate the evacuation of a tall building. This involved modeling pedestrian movements using a multiagent system. These agents were programmed to behave like real pedestrians and make decisions during evacuation scenarios, providing valuable information. The effectiveness of two evacuation strategies was then evaluated using the simulation data. The proposed methodology was validated using a case study. The simulations showed that the best strategy depends on factors such as the distribution of people, the capacity of the exits, and the time available for evacuation. Finally, the model includes a training process that uses virtual reality technology to improve situational awareness.

Currently, it is recognized that manufacturing systems are complex in their structure and dynamics. Management, control and forecasting of such systems are very difficult tasks due to complexity. Numerous variables and signals vary in time with different patterns so that decision makers must be able to predict the behavior of the system. This is a necessary capability in order to keep the system under a safe operation. This also helps to prevent emergencies and the occurrence of critical events that may put in danger human beings and capital resources, such as expensive equipment and valuable production. When dealing with chaotic systems, the management, control, and forecasting are very difficult tasks. In this article an application of neural networks and vector support machines for the forecasting of the time varying average number of parts in a waiting line of a manufacturing system having a chaotic behavior, is presented. The best results were obtained with least square support vector machines and for the neural networks case, the best forecasts, are those with models employing the invariants characterizing the system’s dynamics.