How do we consider problems and models in the practice of simulation? It is our possibly contentious observation that simulation model solving seems to be more critical to the mission of simulation modeling than problem solving. Inspired by the theme of this year's Winter Simulation Conference, we ask the question, "Is problem solving, or simulation model solving, mission critical?" To investigate this we look at three perspectives, those of the textbook, the article and the editorial. The textbook perspective is the balance of the "traditional" view of simulation presented by the academic textbook against practical experience. The article perspective is a classification of papers published in four leading simulation journals in the year 2004 (ACM TOMACS, SIMULATION, Simulation Modelling Practice and Theory, and Simulation & Gaming). The editorial perspective is a discussion of editorial policy presented by the same journals. Our findings show that our observation is not contradicted. INTRODUCTIONIn the long experience of the first author, one not contradicted by the lesser but fine experiences of the co-authors, simulation model solving seems to be a more critical to those pursuing the mission of simulation modeling than problem solving. Our viewpoint on this emerging worry is as follows. The mission of simulation is about problem solving, assisting, understanding, facilitating, the handling of change, etc. This usually involves a simulation model. Models are fun, enticing peacock devices that are money earners. Problems only exist if they have owners. A problem only exists for as long as its owners believe they still have a problem they want help with. If the owners decide they know what to do, there is no problem. Hence, problem existence = owner's attention span As simulation modelers, which are we more interested in? Solving the problem or the model? Consider the following: a model is the analyst's baby, and the nurturing and protecting of it makes the model more important than the problem. After all, the problem is the customer's baby, and one's own baby is always prettier, brighter and better than other babies. Is this true? Are simulation modelers so protective of their own creation they would rather concentrate on the model and not the problem? For the sake of debate we take the stance that this is so and seek to find evidence of practically based advice against this. To this end, and in keeping with the theme of this year's Winter Simulation Conference, this paper asks the question, "Is problem solving, or simulation model solving, mission critical?"To seek an answer to this question we consider three perspectives, those of the textbook, the article and the editorial. The first of these, the textbook perspective, is intended to represent the balance of the "traditional" view of simulation presented by the academic textbook against our practical experiences. The second of these, the article perspective, is a classification of papers published in four leading simulation journals in the year 2004 ...

This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing. POSITION STATEMENT OF PETER LENDERMANNThis contribution is specifically looking from the point of view of discrete event simulation as a tool for virtual experimentation to enable design and performance enhancement in manufacturing and logistics. In a world of increasing complexity and customization it will be very important to make sure that the complexity of the systems that simulations are supposed to represent does not develop faster than the capability to model these systems. To achieve this, modeling techniques will have to meet five major requirements as described in the following sections.