Preface: Introduction to Special Issue

Demands for flexibility, greater variety of products, and advanced production processes complicate the logistics and planning of manufacturing systems; this also increases the number of alternatives that should be formalized in a model. The evaluation of all the possible alternatives of the planning problem is often considered an NP-hard combinatorial problem. Practical problems often contain nonlinearities, combinatorial relationships, and uncertainties that cannot be easily formalized under mathematical predefined relationships, binding the use of most commercial optimization toolboxes. Despite the fact that simulation models have proved to be useful for examining the performance of alternative system configurations and/or alternative operating procedures, several limitations arise when simulation techniques are applied to improve logistic systems performance due to its inability to evaluate more than a fraction of the immense range of options available. This special issue is the second of two issues that focus on the use of simulation techniques to improve manufacturing and logistic systems performance. Papers published in the first issue (Volume 80, Number 3) were mainly related to new modeling and simulation methodologies to improve logistic and manufacturing systems performance. Papers and technical notes in this second issue are focused on illustrating the use of experimental models to improve the performance of manufacturing and logistic systems by means of case studies. The article “A Petri Nets–Based Scheduling Methodology for Multipurpose Batch Plants’’ presents an optimization methodology of batch production processes assembled by shared resources that rely on a mapping of state events into time events, allowing in this way the straightforward use of well-consolidated scheduling policies developed for