somewhat recent papers
Multi-time scale Markov decision process approach to strategic network growth of reverse supply chains by Wuthichai Wongthatsanekorn, Matthew J. Realff, Jane C. Ammons (all from Georgia Tech) in Omega, Volume 38, Issues 1-2, February-April 2010, Pages 20-32.
Reverse supply chains take back used consumer products for repair, reuse, refurbishment, and/or recycling. This paper uses a Markov decision process to model the allocation of capital budget resources for the purpose of growing the reverse supply network in a way that satisfies “long term collection targets and collection cost constraints”. A heuristic is developed based on dynamic programming, linear programming and Q-Learning, and it is applied numerically to some test cases. The results are not applied to a true setting in the paper, though the work is motivated by real-world examples from the carpet industry.
Entropy analysis of metal production and recycling by Stefan Gößling-Reisemann in Management of Environmental Quality: An International Journal, 2008, Volume 19, Issue 4, Page 487-492.
This paper uses thermodynamic entropy production to measure resource consumption and statistical entropy to measure materials separation, as both relate to metal recycling. The ideas sound interesting but the paper is quite brief, leaving out many of the details. Gößling-Reisemann has a book chapter on life cycle assessment and thermondynamics as well as a paper in the Journal of Industrial Ecology entitled What Is Resource Consumption and How Can It Be Measured? that generated a written response by John Manoochehri and counter by Gößling-Reisemann in the most recent issue of that journal. I am unable to access these at my institution at the moment, but plan to check them out at some point. The tie-in to green OR is that if these measures prove useful (and Gößling-Reisemann admits in the Entropy Analysis paper that that may be tough data-wise), the decision analysis for production, recycling network structure, etc. has a more solid quantitative basis.