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A risk management version of the classical investment-consumption problem known as Merton's problem in the finance literature is proposed. Risk is measured by variance, which introduces a nonlinear function of the expected value into the control problem. Standard stochastic theory cannot properly handle this type of nonlinear stochastic optimization problem. Therefore, we study this time-inconsistent problem within the mean field-type control framework. We derive the sufficient condition of optimality and solve the problem completely. Numerical results illustrating the effect of risk on optimal policies are also presented. Applications can be numerous, including all kinds of investment decisions or operations decisions.

We develop a new approach for solving the optimal retirement problem for an individual with an unhedgeable income risk. The income risk stems from a forced unemployment event, which occurs as an exponentially distributed random shock. The optimal retirement problem is to determine an individual’s optimal consumption and investment behaviors and optimal retirement time simultaneously. We introduce a new convex-duality approach for reformulating the original retirement problem and provide an iterative numerical method to solve it. Reasonably calibrated parameters say that our model can give an explanation for lower consumption and risky investment behaviors of individuals, and for relatively higher stock holdings of the poor. We also analyze the sensitivity of an individual’s optimal behavior in changing her wealth level, investment opportunity, and the magnitude of preference for postretirement leisure. Finally, we find that our model explains a countercyclical pattern of the number of unemployed job leavers.

We study a periodic review inventory model with a nonperishable product over an infinite planning horizon. The demand for the nonperishable product arrives according to a Poisson process. Lost sales are unobservable but the stockout times are observable. We formulate the problem as a dynamic programming model with learning on arrival rate according to stockout times and further simplify it by using unnormalized probabilities. We then compare the system performance with those under other two information scenarios where lost sales are observable or both lost sales and stockout times are unobservable. We show that the optimal inventory order-up-to level with observable stockout times is larger than the one with observable lost sales. We also show that more information improves the system performance.

This paper revisits the finite-horizon model of a censored newsvendor by Ding et al. [Ding, X., M. L. Puterman, A. Bisi. 2002. The censored newsvendor and the optimal acquisition of information. Oper. Res. 50 517–527]. An important result claimed there without a proper proof is that the myopic order quantity is always less than or equal to the optimal order quantity. Lu et al. [Lu, X., J. S. Song, K. Zhu. 2008. Analysis of perishable inventory systems with censored demand data. Oper. Res. 56(4) 1034–1038.] supplied a correct proof of the result. We analyze the same model using the interesting concept of the unnormalized probability, which simplifies the dynamic programming equation considerably and facilitates the proof of the claim. Moreover, it produces the proof of the existence of an optimal solution for an infinite-horizon setting of the problem.