We develop an optimization procedure for assisting decision-makers in the allocation of resources for cleaning up a specific oil spill. The objective function is to minimize a weighted combination of spill-specific response and damage costs. Inputs to this problem include information about the outflow of oil, availability and performance of spill cleanup equipment, as well as costs of equipment transported and on-scene operation. A general (albeit separable) damage function is assumed. The algorithm is deterministic and is based on a dynamic program within which a series of 0-1 knapsack problems are solved repeatedly. Although this algorithm is approximate, its worst-case performance is quantified and we argue that under realistic inputs the procedure can be expected to produce solutions very close to optimality. Under prescribed conditions we prove that the algorithm produces optimal solutions. A realistic example based on the Argo Merchant oil spill is presented to provide insight into the structure of this problem. Finally, we discuss possible uses of this model within the existing and alternative operational and policy environments.dynamic programming: applications, environmental management

Investment success in a cyclical market, such as the international bulk shipping market, can be dramatically enhanced by a cost-based buy-low, sell-high investment strategy. We developed an estimator of relative price level for a general asset using net present value techniques and publicly available information on costs and revenues. The estimator compares market price with a cost-based nominal price and thus identifies relative highs and lows in a market. As a test, we used the estimator on historical data to simulate how investment decisions could have been made in the bulk shipping market. Investment strategies using the estimator to time acquisitions and sales perform significantly better than a “blind” investment strategy (where ships are acquired and sold at fixed time intervals).