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The Federal Reserve System of the United States is making changes to its cash recirculation policy to reduce depository institutions' (banks') overuse of its cash processing services. These changes will affect operating policies and costs at many institutions having large cash businesses and, in turn, impact cash transportation and logistics providers. This study provides the framework to study the cash supply chain structure and analyzes it as a closed‐loop supply chain. Additionally, it describes the cash flow management system used by banks in the U.S.

New currency recirculation guidelines implemented by the Federal Reserve System (Fed) of the United States are intended to reduce the overuse of its currency processing services by depository institutions (banks). These changes are expected to have a significant impact on operating policies at those depository institutions that handle large volumes of currency. We describe two business models that capture the flow of currency between a bank and the Fed; the first model captures the current operations of most banks, while the second is expected to be adapted by many banks in response to the new guidelines. Motivated by our work with Brink’s, Inc., to assess the economic impact that banks will sustain from these guidelines, we present a detailed analysis that provides managers of banks with optimal strategies to manage the flow of currency to and from the Fed for a variety of cost structures and demand patterns. Given this insight into a bank’s optimal behavior, the Fed can also use our analysis to fine tune its guidelines to achieve the desired goals.

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In this paper a nonlinear mathematical programming model is developed for cell formation that identifies part families and machine groups simultaneously with no manual intervention or subjective judgement. The objective of the model is minimization of the weighted sum of the voids and the exceptional elements. Changing weights for void and exceptional elements aids the designer with a systematic generation of different solutions, i.e., forming large loose cells or small tight cells. An assignment allocation algorithm (AAA) and a simulated annealing algorithm (SAA) are developed to solve 'the model. AAA and SAA compare favorably with many well-known procedures for the problems tested. AAA is less computer-intensive and hence large problems with 400 parts and 240 machines were solved with AAA in less than a minute on Sun Spare station. However, AAA is sensitive to the initial machine grouping solution input to the algorithm. 8AA gives consistent results but requires more computational time.