The storage of goods plays a crucial role in warehouse management systems. Although this operation is often considered simple, it is in fact complex, due to the volume of products, the uncertainty in demands, and the rapid customer service response that the market requires. Moreover, the optimization of logistical resources to maintain an efficient operational flow, such as the allocation of storage spaces, often requires complex decisions. In this paper, we analyze academic contributions on the storage location assignment problem published between 2005 and 2017. The literature is classified according to the solution methods, objectives, and related considerations. Suggestions are also provided for future research.

No abstract

The Southington, Connecticut, water-supply system is characterized by a distribution network that contains more than 1 700 pipeline segments of varying diameters and construction materials, more than 186 mi (299 km) of pipe, 9 groundwater extraction wells capable of pumping more than 4 700 gal/min (0.2965 m3/s), and 3 municipal reservoirs. Volatile organic compounds, which contaminated the underlying groundwater reservoir during the 1970s, contaminated the water-supply system and exposed the town's residents to volatile organic chemicals. We applied a computational model to the water-supply system to characterize and quantify the distribution of volatile organic compounds in the pipelines, from which we estimated the demographic distribution of potential exposure to the town's residents. Based on results from modeling analyses, we concluded the following: (a) exposure to volatile organic compound contamination may vary significantly from one census block to another, even when these census blocks are adjacent to each other within a specified radius; (b) maximum spatial spread of contamination in a water-distribution system may not occur under peak demand conditions, and, therefore, maximum spatial distribution of the exposed population also may not correspond to peak demand conditions, and (c) use of the proposed computational model allows for a more refined and rigorous methodology with which to estimate census-block-level contamination for exposure assessment and epidemiologic investigations.

No abstract