Some of the real life decisions are made in decentralized manner under uncertainty. Stochastic bi-level linear programming problems are used to handle such decision making problems. In this paper, we have proposed stochastic bi-level linear programming problems where some of the right hand side parameters of the constraints in both first (leader) and second level (follower) as normal and log-no...

Using the linear programming approach to stochastic control introduced in [6] and [10], we provide a semigroup property for some set of probability measures leading to dynamic programming principles for stochastic control problems. An abstract principle is provided for general bounded costs. Linearized versions are obtained under further (semi)continuity assumptions.

multiple objective programming (mop) problems have become famous among many researchers due to more practical and realistic implementations. there have been a lot of methods proposed especially during the past four decades. in this paper, we develop a new algorithm based on a new approach to solve mop problems by starting from a utopian point (which is usually infeasible) and moving towards the...

In this paper, we address a surgery capacity sharing problem with multiple hospitals, and formulate a model where hospitals who have their own capacity (the Operating Room) locally. Each hospital has several surgery teams facing random demands and seeks appropriate capacities to accommodate the demands, so that its total profit is maximized. We first study the allocation of available surgery ca...

In this paper we discuss risk neutral and risk averse approaches to multistage (linear) stochastic programming problems based on the Stochastic Dual Dynamic Programming (SDDP) method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system. 2012 Elsevier B.V. All rights reserved.

This paper considers a stochastic dynamic inventory problem involving a single item, linear cost structures, and finite distributions (but not necessarily independent) for the stochastic cost and demand parameters. We develop primal and dual algorithms for a multi-stage stochastic linear programming formulation for the problem. The complexity of the proposed algorithms is shown to be within O(N...