Felipe F. Baesler

Multi-objective simulation optimization for a cancer treatment center

The paper presents a case study application of a cancer treatment center facility. A simulation model was created and integrated to a multi-objective optimization heuristic developed by the authors with the purpose of finding the best combination of control variables that optimize the performance of four different objectives related to the system. The results obtained show that the implementation of the proposed solution could improve the four objectives, compared to the existing solution.

The use of simulation for process improvement at an ambulatory surgery center

The work presented has the objective of building a simulation model to evaluate different alternatives for operation of a projected center for ambulatory surgery. For the construction of the model, a group of pathologies was selected and real data was taken from statistics of surgeries from a clinical hospital. The information was supplemented by the opinion of medical surgeons and expert anesthesiologists. Similarly, the model was validated by means of the opinion of experts and implemented with the Arena simulation software. Different configurations of operating conditions were studied. It is concluded that the maximum throughput of daily surgeries is achieved, 10 in total, dedicating two beds to preparation of patient, five beds to the transitory hospitalization and using an LPT scheduling rule in the operating rooms, i.e., the heaviest surgeries first.

The use of simulation and design of experiments for productivity improvement in the sawmill industry

This work presents a discrete event simulation model of an important sawmill in Chile. This model was used to perform a bottleneck analysis of the wood process and to propose, based on these results, alternatives that would yield to an improvement in the process productivity. Different alternatives were simulated and the results obtained were used to perform a full factorial design in order to select the combination of factors that have the most important impact in the process productivity. The implementation of these improvement measures could increase the wood production up to a 25%.

Productivity improvement in the wood industry using simulation and artificial intelligence

The objective of this article is to present the results obtained after using a simulation optimization methodology applied to a production line from a secondary manufacturing wood processing plant of a well known Chilean mill. For this reason a simulation model constructed in ARENA, was integrated to a genetic algorithm heuristic. The results obtained show that using a different configuration of the plant resources, it is possible to reduce the total average cycle time in 18%. The resource configuration needed to reach this result was obtained evaluating just 1.6% of the total number of possible combinations.

A simulator to improve waiting times at a medical imaging center

Medical Imaging Centers (MIC) are critical units in every hospital or medical center because they are an important step in generating a patient¿s diagnostic. This paper shows a simulator designed to facilitate the development of simulation studies of MIC, which departs from traditional modeling because it uses a pull paradigm for the patients. A group technology approach was used to minimize the number of objects in the simulator, which includes objects that have the functionality of the equipments and processes found in these facilities, so that the analyst only needs to provide the parameters of the center. All the data is provided to the simulator through a Graphic User Interface (GUI), requiring no programming capacities. As an application of the simulator, an example is provided where the simulator is used to improve the waiting times and equipment rate of utilization at a research hospital in Chile.

A meta-heuristic based on simulated annealing for solving multiple-objective problems in simulation optimization

This paper presents a new meta heuristic algorithm based on the search method called simulated annealing, and its application to solving multiobjective simulation optimization problems. Since the simulated annealing search method has been extensively applied as a modern heuristic to solve single objective simulation optimization problems, a modification to this method has been developed in order to solve multiobjective problems. The efficiency of this new algorithm was tested on a real case problem modeled under discrete simulation.

Proceso logístico productivo de un centro de sangre regional: modelamiento y análisis

Background : The blood supply chain is a complex system that considers different interconnected elements that have to be synchronized correctly to satisfy in quality and quantity the fi nal patient requirements. Aim : To determine the blood center maximum production capacity, as well as the determination of the necessary changes for a future production capacity expansion. Material and Methods : This work was developed in the Blood Center of Concepcion, Chile, operations management tools were applied to model it and to propos e improvement alternatives for the production process. The use of simulation is highlighted, which permitted the replication of the center behavior and the evaluation of expansion alternatives. Results : It is possible to absorb a 100% increment in blood demand, without making major changes or investments in the production process. Also it was possible to determine the subse- quent steps in terms of investments in equipment and human resources for a future expansion of the center coverage. Conclusions: The techniques used to model the production process of the blood center of Concepcion, Chile, allowed us to analyze how it operates, to detect “bottle necks”, and to support the decision making process for a future expansion of its capacity

INTRODUCCIÓN DE ELEMENTOS DE MEMORIA EN EL MÉTODO SIMULATED ANNEALING PARA RESOLVER PROBLEMAS DE PROGRAMACIÓN MULTIOBJETIVO DE MÁQUINAS PARALELAS

This paper introduces a variant of the metaheuristic Simulated Annealing, oriented to solve multiobjective optimization problems. This technique is called MultiObjective Simulated Annealing with Random Trajectory Search (MOSARTS). This technique incorporates short an long term memory concepts to Simulated Annealing in order to balance the search effort among all the objectives involved in the problem. The algorithm was tested against three different techniques on a real life parallel machine scheduling problem, composed of 24 jobs and two identical machines. This problem represents a real life case study of the local sawmill industry. The results showed that MOSARTS behaved much better than the other methods utilized, because found better solutions in terms of dominance and frontier dispersion.