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In this paper, we study the maximum principle of mean field type control problems when the volatility function depends on the state and its measure and also the control, by using our recently developed method in [Bensoussan, A., Huang, Z. and Yam, S. C. P. [2023] Control theory on Wasserstein space: A new approach to optimality conditions, Ann. Math. Sci. Appl.; Bensoussan, A., Tai, H. M. and Yam, S. C. P. [2023] Mean field type control problems, some Hilbert-space-valued FBSDEs, and related equations, preprint (2023), arXiv:2305.04019; Bensoussan, A. and Yam, S. C. P. [2019] Control problem on space of random variables and master equation, ESAIM Control Optim. Calc. Var. 25, 10]. Our method is to embed the mean field type control problem into a Hilbert space to bypass the evolution in the Wasserstein space. We here give a necessary condition and a sufficient condition for these control problems in Hilbert spaces, and we also derive a system of forward–backward stochastic differential equations. 

Prior research on inventory control has been wide ranging, yet the environmental implications of an (s,S) inventory policy remain uninvestigated. This paper seeks to bridge the gap by characterising a firm’s voluntary environmental policy in the setup of an (s,S) inventory control policy. We suggest a mixed model structure wherein, due to the presence of fixed production costs, the inventory is determined continuously by sales and impulsively with ordering decisions obeying an optimal stopping process, while the uncertain sales process is controlled by continuous-time environmental goodwill-related decisions. We show that a firm should successively use voluntary environmental efforts to stimulate its sales when there is inventory and to increase backlogging to improve its production efficiency. Given the recurrent pattern of this policy, we conclude that voluntary environmental efforts under an (s,S) inventory control is not compatible with using these efforts as a means to generate ephemeral reputation insurance. 

A New Approach to Contract Design with Private Inventory Information

In a typical decentralized supply chain, a downstream retailer privately observes its inventory level and has an informational advantage over the upstream supplier. In “A Stationary Infinite-Horizon Supply Contract Under Asymmetric Inventory Information” by Bensoussan, Sethi, and Wang, the authors study how to optimally design a stationary, truth-telling, long-term contract in such a setting. In contrast to the classic first order approach in literature, they formulate the contract design as an optimization over a functional space and develop a solution approach based on the calculus of variations. They further apply their necessary optimality condition to the class of batch-order contracts, which replenish a prespecified inventory quantity for a fixed payment in each period only when the retailer has zero inventory on hand.

 

We propose an optimization formulation for the simultaneous estimation of a latent variable and the identification of a linear continuous-time dynamic system, given a single input-output pair. We justify this approach based on Bayesian maximum a posteriori estimators. Our scheme takes the form of a convex alternating minimization, over the trajectories and the dynamic model respectively. We prove its convergence to a local minimum which verifies a two point-boundary problem for the (latent) state variable and a tensor product expression for the optimal dynamics. 

As well studied in the operations research literature, optimization of a mutual reserve system (e.g., federal reserves) and a nonmutual one such as regular inventory systems requires solving simultaneous systems of quasi-variational inequalities, of which analytical solutions in closed form remain unattainable and computational solutions are still intractable. Thus far, the studies of reserve optimization are of intra-nations (e.g., central bank reserves) as opposed to inter-nations (e.g., COVID vaccine reserves of the United Nations). In this paper, we advance a method of computational analytics for mutual reserve optimization, with an international perspective in response to the intensifying challenge on global medical reserves during the COVID pandemic. A solution algorithm is developed in the context of maritime mutual insurances (a long existent international mutual reserve system) and then tested through comprehensive numerical experiments.

 

A novel approach to optimal control problems is proposed wherein instead of computing of the value function its gradient is computed by viewing it as a solution of a coupled system of partial differential equations. An iterative numerical algorithm is proposed whose convergence is rigorously established. For the implementation of the numerical algorithm supervised learning techniques are used. Numerical experiments confirm the expediency of the proposed techniques. 

We study the deterministic control problem in the Wasserstein space, following the recent works of Bonnet and Frankowska, but with a new approach. One of the major advantages of our approach is that it reconciles the closed loop and the open loop approaches, without the technicalities of the traditional feedback control methodology. It allows also to embed the control problem in the Wasserstein space into a control problem in a Hilbert space, similar to the lifting method introduced by P. L. Lions, used already in our previous works. The Hilbert space is different from that proposed by P. L. Lions, and it allows to recover the control problem in the Wasserstein space as a particular case.