Diego Broz

Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

Forest management decision making based on a real options approach: An application to a case in northeastern Argentina

Abstract The Net Present Value (NPV) approach is widely applied to assess forest investments, but this method has serious shortcomings, which we propose to overcome by switching to the assessment through the Real Options Approach (ROA). The model in this paper starts with the simulation of the forest’s growth, combined with the projection of the products’ prices and valuing the assets using a binomial model. We include an option of postponement, determining the optimal period of felling. We find that ROA is more robust than the NPV approach because it relaxes the assumption of constancy of both the prices and the discount rate, allowing the determination of the optimal time of felling based on the growth rate of either the forest or the prices of its products. Contrary to the traditional NPV approach, the results obtained with ROA exhibit longer harvest turns and consequently higher profits. The key variable in the ROA, the Real Option Value (ROV) can be shown to be less (albeit moderately) sensitive to decreases of the discount rate than NPV. Moreover, ROV is moderately sensitive to decreases in the price of logs and is negligibly affected by rises in the costs of harvesting, loading and transporting rolls.