W e consider a joint location-inventory problem involving a single supplier and multiple retailers. Associated with each retailer is some variable demand. Due to this variability, some amount of safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers (and therefore inventory storage locations) for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. We formulate this problem as a nonlinear integer-programming model. We then restructure this model into a set-covering integer-programming model. The pricing problem that must be solved as part of the column generation algorithm for the set-covering model involves a nonlinear term in the retailerdistribution-center allocation terms. We show that this pricing problem can (theoretically) be solved efficiently, in general, and we show how to solve it practically in two important cases. We present computational results on several instances of sizes ranging from 33 to 150 retailers. In all cases, the lower bound from the linear-programming relaxation to the set-covering model gives the optimal solution.

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Customer behavior modeling has been gaining increasing attention in the operations management community. In this paper we review current models of customer behavior in the revenue management and auction literatures and suggest several interesting research directions in this area.

In this paper, we study the stochastic transportation-inventory network design problem involving one supplier and multiple retailers. Each retailer faces some uncertain demand. Due to this uncertainty, some amount of safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers (and therefore inventory storage locations) for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. Shen et al. (2000) and Daskin et al. (2001) formulated this problem as a setcovering integer-programming model. The pricing subproblem that arises from the column generation algorithm gives rise to a new class of submodular function minimization problem. They only provided efficient algorithms for two special cases, and assort to ellipsoid method to solve the general pricing problem, which run in O(n 7 log(n)) time, where n is the number of retailers. In this paper, we show that by exploiting the special structures of the pricing problem, we can solve it in O(n 2 log n) time. Our approach implicitly utilizes the fact that the set of all lines in 2-D plane has low VC-dimension. Computational results show that moderate size transportation-inventory network design problem can be solved efficiently via this approach.

Two socioeconomic transformations, namely, the booms in the sharing economy and retail e-commerce, lead to the prospect where shared mobility of passenger cars prevails throughout urban areas for home delivery services. Logistics service providers as well as local governments are in need of evaluating the potentially substantial impacts of this mode shift, given their economic objectives and environmental concerns. This paper addresses this need by providing new logistics planning models and managerial insights. These models characterize open-loop car routes, car drivers’ wage-response behavior, interplay with the ride-share market, and optimal sizes of service zones within which passenger vehicles pick up goods and fulfill the last-mile delivery. Based on theoretical analysis and empirical estimates in a realistic setting, the findings suggest that crowdsourcing shared mobility is not as scalable as the conventional truck-only system in terms of the operating cost. However, a transition to this paradigm has the potential for creating economic benefits by reducing the truck fleet size and exploiting additional operational flexibilities (e.g., avoiding high-demand areas and peak hours, adjusting vehicle loading capacities, etc.). These insights are insignificantly affected by the dynamic adjustment of wages and prices of the ride-share market. If entering into this paradigm, greenhouse gas emissions may increase because of prolonged car trip distance; on the other hand, even exclusively minimizing operating costs incurs only slightly more emissions than exclusively minimizing emissions. The online appendix is available at https://doi.org/10.1287/msom.2017.0683 .