Publication policy is an inevitable topic for researchers and no recent solution has a real consensus. In my opinion, current offers in terms of open publishing are far from being appropriate, at least in computer science. It is very important to keep in mind that the major issue of this question is the variety of interests and actors (and not just a matter of financial interest). Here are some recent points of view that I find interesting in the current debate.
Communications of the ACM. He explains in an article the principle of "Fair Access" pursued by ACM. An interesting point is that "in the case of publishing by a professional association, such as ACM, the authors, as ACM members, are essentially also the publishers." 
this article about "Open Evaluation" published in "Frontier in Computational Neuroscience" . Again, in this article the close links between the different actors of the scientific publication (here readers and authors) are highlighted.
www.phdcomics.com: "What is Open Access". In my opinion, in this video the given view of open-access is over-simplified;
however the last slides give interesting elements (“current scientific cultural
practices”, “necessity to experiment some publishing practices”, “role of young
researchers in the movement for change”)
 Moshe Y. Vardi, "Fair Access", Communications of the ACM, May 2012, Vol. 55 No. 5, Page 5
 Nikolaus Kriegeskorte, Alexander Walther, and Diana Deca, "An emerging consensus for open evaluation: 18 visions for the future of scientific publishing", Front. Comput. Neurosci. 6:94. doi:10.3389/fncom.2012.00094
I recently read a call of papers from “Simulation Modeling Practice and Theory” journal (Elsevier) on “Simulation-Optimization of Complex Systems: Methods and Applications”. Coupling simulation and optimization tools is a very interesting topic and, in my opinion, multiagent approach is a promising research direction to tackle the underlying research issues.
Let’s take the case of optimizing a public transport network. The decision variables are, for instance, the frequencies of the lines (or the daily schedule of the buses, trains or tramways). If you find a model which is a good approximation of the system and can be efficiently tackled by an optimization procedure, you have your solution. However, you may require simulating the system for obtaining a good evaluation. For instance, simulation could help to determine the demand of each modes of transport (from a model of users’ behavior). In this case, the optimization procedure has to exploit a simplified model of the system to be efficient.
A potential way to combine simulation and optimization is given in the above figure. Note that this is not the only way to combine simulation and optimization. In this approach, the simulation works on a “complete” model of the system (the simulation model), and the optimization uses a simplified or partial model of the system (the optimization model).
In this configuration, the optimization procedure uses a set of parameters that approximates the system for a given solution (instance of decision variables that has been used by the simulation). These parameters may be a good approximation to evaluate the solutions that are close to the simulated solution, but new simulations are necessary to evaluate solutions that are significantly different to the simulated one. For instance, in our hypothetical case study, the simulation may determine the modal-choice for given frequencies of the lines. These fixed parameters of the modal-choice may remain a good approximation for small modifications of the frequencies. However, a new simulation is necessary to evaluate a solution with completely different frequencies of the lines, because these differences impact the modal-choice. In this combination of simulation and optimization, the simulation and optimization steps have to be repeated until a satisfactory solution based on a good approximation of the system is found.
A final word on agent approach for developing such a system. What are the interests of an agent approach in this context? Of course, parallel computation will be facilitated by agent approach, and if simulation or optimization procedures have been conceived as multiagent systems, an agent approach at a higher level may be valuable for a consistency in development tools. But another aspect, the “autonomy” of agents, deserves to be pointed out. I do not refer to the autonomy that may characterize intelligent agents, but rather the modularity (independence) of the system’s components. Agent oriented approach can support this modularity which is essential for developing such a complex combination between simulation and optimization. Conceiving such a system using a multiagent approach may support: the design and implementation of the interfaces between the agents (here the simulation and the optimization components) with a clear definition of roles, capacities, and interactions of agents; the management of asynchronous communications between the agents; and the integration of additional agents such as interface agent and data sources. As I said at the beginning, there are many perspectives.
Don’t hesitate to leave a comment or to send me additional resources for the blog.
A session on Innovations in APS (Advanced Planning and Scheduling) Systems is organized at the ILS'2012 conference (Quebec, Canada, August 26-29, 2012).
"APS are computer-supported planning systems that put forward various functions of Supply Chain Management, including procurement, production, distribution and sales, at the strategic, tactical and operational levels. These systems stand for a quantitative model-driven perspective on the use of Information Technology in supporting Supply Chain Management, for exploiting advanced analysis and supply chain optimization methods." Details
Dr. Gabriela Ochoa and Dr. Gisele L. Pappa will chair the Self-* track at GECCO 2012. "The aim of the Self-* Search track is to bring together researchers from computer science, artificial intelligence and operations research, interested in software systems able to automatically tune, configure, or even generate and design optimization algorithms and search heuristics."