Humanitarian organizations (HOs) often base their warehouse locations on individuals' experience and knowledge rather than on decision-support tools. Many HOs run separate supply chains for emergency response and ongoing operations. Based on reviews of humanitarian network design literature combined with an in-depth case study of United Nations High Commissioner for Refugees (UNHCR), this paper presents a warehouse location model for joint prepositioning that incorporates political and security situation factors. Although accessibility, co-location, security, and human resources are crucial to the practice of humanitarian operations management, such contextual factors have not been included in existing network optimization models before. We found that when quantified, and modeled, such factors are important determinants of network configuration. In addition, our results suggest that joint prepositioning for emergency response and ongoing operations allows for expansion of the global warehouse network, and reducing cost and response time.

The effect of financial risks on (R, Q) inventory policies is analyzed in a real options framework. Simple adjustments of the usual formulas for R and Q are suggested and tested. Stochastic demand and purchase costs are considered, both with known systematic (business-cycle-related) risk. The systematic risk of stochastic demand has typically a negligible effect on the optimal values of R and Q, although an improvement may be achieved by a simple adjustment of R. The systematic risk of the purchase price, c, has a significant effect on R and Q. The capital holding cost should be estimated as r \cdot c, where r is the sum of the risk-free interest rate, the expected price decrease, and the risk premium associated with the systematic risk of c. For goods quoted on commodity exchanges, r may be estimated directly from the prices on forward contracts. Its size (and sign) varies considerably for different commodities.inventory control, inventory costing, capital cost and real options

In this paper we consider the problem of a firm that faces a stochastic (Poisson) demand and must replenish from a market in which prices fluctuate, such as a commodity market. We describe the price evolution as a continuous stochastic process and we focus on commonly used processes suggested by the financial literature, such as the geometric Brownian motion and the Ornstein-Uhlenbeck process. It is well known that under variable purchase price, a price-dependent base-stock policy is optimal. Using the single-unit decomposition approach, we explicitly characterize the optimal base-stock level using a series of threshold prices. We show that the base-stock level is first increasing and then decreasing in the current purchase price. We provide a procedure for calculating the thresholds, which yields closed-form solutions when price follows a geometric Brownian motion and implicit solutions under the Ornstein-Uhlenbeck price model. In addition, our numerical study shows that the optimal policy performs much better than inventory policies that ignore future price evolution, because it tends to place larger orders when prices are expected to increase.

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