How Product Locations Drive Traffic Throughout a Retail Store In “Store-Wide Shelf-Space Allocation with Ripple Effects Driving Traffic,” Flamand, Ghoniem, and Maddah develop a framework for deciding where to place products in a store, in addition to apportioning the shelf space among products, in a way that maximizes impulse profit, a phenomenon that may account for 50% of transactions. By analyzing a large data set of customer receipts from a grocery store in Beirut, the authors develop a regression model that estimates traffic at a shelf based on its location and the “attraction” from products allocated nearby. The traffic model is embedded within a mixed-integer nonlinear program, which they solve via specialized linear approximations. For the store in Beirut, a 65% improvement in impulse profit is anticipated, and the location of products is found to be significantly more important in driving store-wide traffic than the relative shelf-space allocation.

We address a team-building problem for a basketball team, where the team decides on new players to draft, current players to trade with those of other teams, and/or which free agents to acquire, to maximize its total value. Here, the team value is considered as the “ability to win a match.” While the individual values of current players in the league are directly obtained from historical data, we propose a predictive model to predict the individual values of collegiate players. We also consider an additional value that comes from the synergy potential among players on the team. We consider “archetypes” of players that represent logical groupings of players based on their characteristics. To estimate the synergy potential between two players, we develop a new metric that encompasses the synergy potential between archetype pairs that each player belongs to. Since the archetypes of collegiate players are unknown, before the synergy estimation, a classification model is performed in order to predict archetypes of collegiate players. These inform a mixed-integer nonlinear programming model for the draft, trade and free agent acquisition problem that maximizes the total team value and balances the synergy among players. We reformulate the objective function in order to overcome computational challenges due to its nonlinearity. Finally, we conduct a case study on a National Basketball Association (NBA) team using 2019–2020 data. Results are discussed and compared with the actual decisions to validate our approach.

In this paper, we propose a system dynamics (SD) model to examine the dynamics of an informal artisanal and small-scale gold mining (ASGM) supply chain that has interactions with the illegal gold supply chain in the Amazon rainforest region, Madre de Dios (MdD), Peru. In order to examine the system under climate impacts and validate the model, we run it under a flood scenario, which is one of the main climate impacts that causes disruption in mining activities. Our findings suggest that the dynamics of informal mines are highly affected by the illegal mercury supply, fuel supply, and availability of workers. In addition, the model under the flood scenario suggests that any external variable that could directly affect fuel and mercury supply would result in a disruption of informal and illegal gold production.